| Excellence in Undergraduate TeachingAwarded by Interdepartmental Science Students' Society every year University of Alberta | Award | 2018-01-11 | Mar, A. |
| Excellence in Undergraduate TeachingAwarded by Interdepartmental Science Students' Society every year University of Alberta | Award | 2017-01-09 | Mar, A. |
| Students' Choice Honour Roll Awarded to instructors of Science courses attaining >75% percentile average on student evaluations University of Alberta | Award | 2018-04-04 | Mar, A. |
| Machine learning in solid-state chemistry: Heusler compounds University of Alberta, Manhattan College | Publication | 2021-04-27 | Alex Gzyl, Mar, A., Oliynyk, A. |
| Compositional and elemental descriptors for perovskite materialsIn this extended abstract we compare the performance of different families of descriptors – molar composition descriptor, weight composition descriptor and elemental descriptor – for regression task (prediction of bandgap) and include examples of a classification task for perovskite oxide materials with general formulas ABO3, A2BB′O6 and AxA′ 1−xByB′ 1−yO6. The best performance was observed for our elemental descriptor which consisted of -site and -site element information on: Shannon’s ionic radius, ideal bond length, electronegativity, van der Waals radius, ionization energy, molar volume, atomic number, and atomic mass. The weight composition descriptor showed superior results over a simpler molar composition descriptor. The results of principal component analysis, regression models with the hyperparameters optimized using an autoML software and Wasserstein autoencoders are briefly discussed for a possible use in inverse materials design. University of Alberta | Publication | 2023-04-21 | "Maicon Pierre", "Jiri Hostas", John Garcia, "Hatef Shahmohamadi", "Alain Tchagang", Shankar, K., "Venkataraman Thangadurai", Dennis R Salahub |
| Accelerated discovery of perovskites and prediction of band gaps using machine-learning methodsContributed oral presentation University of Alberta | Activity | 2019-06-07 | Alex Gzyl, Jonathan Trach, Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome ThemThis was an invited seminar to a workshop about machine learning. University of Alberta | Activity | 2019-04-05 | Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome ThemContributed oral presentation University of Alberta | Activity | 2019-06-07 | Oliynyk, A., Adutwum, L., Mar, A. |
| Accelerating the Discovery of Solid State MaterialsAs part of a satellite meeting to the North American Solid State Chemistry Conference (Colorado School of Mines, Golden, CO), I was one of the instructors in an intensive weekend workshop entitled "Introduction to Machine Learning for Solid State Chemistry: A Practical Workshop" held on July 29-30, 2019. The audience consisted of active researchers in the solid state chemistry community wishing to apply machine learning to their own work. I discussed practical examples of machine learning. University of Alberta | Activity | 2019-07-29 | Mar, A. |
| Accelerating the Discovery of Solid State MaterialsThe event was the annual meeting of the ATUMS CREATE program (between U of A and Technical University of Munich), and the focus is on student presentations. However, this year, short presentations were given by faculty members. University of Alberta | Activity | 2019-11-11 | Mar, A. |
| Accelerating the Discovery of Solid State Materials with Machine-Learning ApproachesContributed poster. University of Alberta | Activity | 2018-07-25 | Oliynyk, A., Mar, A. |
| Accelerating the discovery of solid state materials: From traditional to machine- learning approachesThis was an invited talk to a small (~25 participants) select group of experts in machine-learning at the Telluride Science Workshops. University of Alberta | Activity | 2018-10-01 | Mar, A. |
| Bridging chemistry from high school to university: A Canadian perspective University of Alberta | Activity | 2024-03-22 | Mar, A. |
| Can we use machine learning to find synthesizable compounds? Nanode Battery Technologies, University of Alberta | Activity | 2023-08-04 | Selvaratnam, B., Vidyanshu Mishra, Dundappa Mumbaraddi, "Maxwell Wallace", Arkadii Pominov, Mar, A. |
| Cerium-containing chalcohalides as tunable photoluminescent materials University of Alberta | Activity | 2022-06-16 | Dundappa Mumbaraddi, Vidyanshu Mishra, Mohammad Jomaa, Abhoy Karmakar, Andrew P Grosvenor, Vladimir K Michaelis, Al Meldrum, Mar, A. |
| Classification of crystal structures: Machine-learning predictions and experimental validationThis was an invited presentation to a workshop organized by the Centre for Nonlinear Studies, Los Alamos National Laboratory, on machine learning. Attendance was highly selective (only invited speakers). University of Alberta | Activity | 2021-05-13 | Mar, A. |
| Classification of Half-Heusler Compounds through Machine Learning ApproachesContributed talk. Half-Heusler compounds have many applications as thermoelectric materials, spintronic materials, superconductors, and topological insulators. We have applied machine-learning approaches to classify, verify, and predict half-Heusler compounds. University of Alberta | Activity | 2018-05-28 | Alex Gzyl, Oliynyk, A., Adutwum, L., Mar, A. |
| Classification of Half-Heusler Compounds through Machine-Learning ApproachesContributed poster. University of Alberta | Activity | 2018-07-25 | Alex Gzyl, Oliynyk, A., Adutwum, L., Mar, A. |
| Coloured Li-containing intermetallic compounds University of Alberta | Activity | 2022-06-16 | Mohammad Jomaa, Mar, A. |
| Comparison of computational and experimental inorganic crystal structures University of Alberta | Activity | 2021-07-28 | Jan Poehls, Manon Heyberger, Mar, A. |
| Computational workshopThis workshop involved discussion of advanced techniques for computation of electronic structure, especially for highly disordered structures, which is relevant for many materials used for solar energy conversion. It was led by a visiting professor, Vitaliy Romaka, from Lviv Polytechnic University, who is an expert in this area. University of Alberta | Activity | 2018-09-13 | "Vitaliy Romaka", Oliynyk, A., Mar, A., Alex Gzyl, Vidyanshu Mishra |
| Controlling the luminescence of rare-earth chalcohalides Ce3Ge2–xSixS8I and Ce3Si2S8–ySeyI University of Alberta | Activity | 2023-06-06 | Dundappa Mumbaraddi, Vidyanshu Mishra, Mohammad Jomaa, Xiaoyuan Liu, Abhoy Karmakar, "Sambhavi Thirupurasanthiran", Al Meldrum, Andrew P Grosvenor, Vladimir K Michaelis, Mar, A. |
| Crystal growth of multivalent rare-earth intermetallics using metal fluxes University of Alberta | Activity | 2023-06-06 | Dundappa Mumbaraddi, Vidyanshu Mishra, Ritobroto Sikdar, Mar, A. |
| Crystallography in Chemistry and Materials ScienceThis was an invited talk to a crystallography workshop, where I discussed how crystallography is used on a practical level for materials science research, and includes an introduction to how machine learning can help advance understanding in structural chemistry. University of Alberta | Activity | 2018-05-24 | Mar, A. |
| Discovery of Li-containing compounds with channel structures guided by machine learning Nanode Battery Technologies, University of Alberta | Activity | 2023-08-01 | Volodymyr Gvozdetskyi, Mohammad Jomaa, Selvaratnam, B., Mar, A. |
| Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning ApproachContributed talk. The absence of an inversion centre in crystalline solids is an important prerequisite for many useful electrical and optical properties in materials applications such as piezoelectric and nonlinear optical devices. Here, we apply machine-learning approaches to discover new noncentrosymmetric compounds based solely on their composition. University of Alberta | Activity | 2018-05-28 | Adutwum, L., Oliynyk, A., Jan Poehls, "Yuqiao Zhou", Mar, A. |
| Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proofContributed oral presentation University of Alberta | Activity | 2019-06-07 | Adutwum, L., Oliynyk, A., Vidyanshu Mishra, Mar, A. |
| Effect of Li Addition on the Nonlinear Optical Activity of Ag1-xLixMSe2 (M = Ga, In) University of Alberta | Activity | 2023-06-08 | Mohammad Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, "Diganta Sarkar", "Mengran Sun", "Jiyong Yao", Vladimir K Michaelis, Mar, A. |
| Effect of Li Addition on the Nonlinear Optical Activity of Ag1-xLixMSe2 (M = Ga, In) University of Alberta | Activity | 2023-08-01 | Mohammad Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, "Diganta Sarkar", "Mengran Sun", "Jiyong Yao", Vladimir K Michaelis, Mar, A. |
| Experimental Validation of High Thermoelectric Performance in RECuZnP2 Predicted by High-Throughput DFT CalculationsOral presentation given by Jan Poehls at the Virtual Conference on Thermoelectrics 2020 University of Alberta | Activity | 2020-07-21 | Jan Poehls, "Sevan Chanakian", "Junsoo Park", "Alex Ganose", "Alex Dunn", Nick Friesen, Amit Bhattacharya, "Anubhav Jain", "Alexandra Zevalkink", Mar, A. |
| Explainable machine learning in materials chemistry: Decision trees as scoring function University of Alberta | Activity | 2022-06-16 | Selvaratnam, B., Mar, A. |
| Exploring the colours of gold alloys with machine learningContributed poster University of Alberta | Activity | 2019-06-06 | Oliynyk, A., Vidyanshu Mishra, Dundappa Mumbaraddi, Mar, A. |
| Gordon Research Conference in Solid State Chemistry University of Alberta | Activity | 2022-07-24 | Mar, A. |
| Hot Carrier-Mediated Generation of Solar Fuels Using Plasmon-Semiconductor NanoheterojunctionsSurface plasmons (or “plasmons”) are the quantized collective and coherent oscillations of conduction band electrons at metal-dielectric interfaces. Hot electron-hole pairs are generated by dephasing of plasmons. The excess energy of hot carriers can be used to drive chemical reactions. In this Invited Talk, we discuss strategies to harvest hot electrons and hot holes in plasmon-semiconductor heterojunctions to generate solar fuels. Examples of the successful use of hot carriers to drive CO2 photoreduction and water-splitting to generate fuels such as methane and hydrogen will be shown and discussed. Machine learning is playing an increasingly important role in the optimization of such nanoplasmonic architectures. University of Alberta | Activity | 2022-05-26 | Shankar, K. |
| How large is an atom?Contributed talk. We employed a machine-learning approach to gain chemical insight on the factors that affect atomic size, with implications on how this revises some fundamental concepts in crystal chemistry such as radius ratio rules. This model also accurately predicts interatomic distances in hypothetical compounds, which can be valuable information when investigating experimentally challenging systems containing expensive or radioactive elements. University of Alberta | Activity | 2018-05-28 | Oliynyk, A., Adutwum, L., Mar, A. |
| In Search of Coloured IntermetallicsContributed poster. University of Alberta | Activity | 2018-05-29 | Vidyanshu Mishra, "Abishek Iyer", Oliynyk, A., Jan Poehls, Guy Bernard, Vladimir K Michaelis, Mar, A. |
| Introduction to Machine Learning: A Practical WorkshopThis full-day workshop to introduce machine learning for materials discovery and analysis was organized and offered by several members of FES.
This workshop provided an overview of machine learning applied by UofA researchers at
Engineering and Chemistry departments. The introduction to the practical application included
data processing and preparation aspects, as well as creating and running machine-learning models
on free demo version software. Participants had an opportunity to learn step-by-step how to
handle the data, use the models, and interpret the results.
This workshop was offered twice, first on August 13, 2018 and second on August 16, 2018. University of Alberta | Activity | 2018-08-13 | Oliynyk, A., Adutwum, L., Mar, A., "Ajay Ganesh", "Anjana Puliyanda", "Kaushik Sivaramakrishnan", Gokul Sai Subraveti, Kasturi Nagesh Pai, Prasad, V. |
| Investigating ordering in chalcogenide solid electrolytes using solid-state NMR spectroscopy University of Alberta | Activity | 2022-06-15 | Madhu Sudan Chaudhary, Dundappa Mumbaraddi, Guy Bernard, Mar, A., Vladimir K Michaelis |
| Investigation of Li-Zn-X (X = Ga, In) coloured intermetallics University of Alberta | Activity | 2021-07-28 | "Mohammed Jomaa", Vidyanshu Mishra, Dundappa Mumbaraddi, Madhu Sudan Chaudhary, Vladimir K Michaelis, Mar, A. |
| Luminescence properties of rare-earth chalcohalides RE3Ge2-xSixS8I (RE = La, Ce, Pr) and Ce3Si2S8-ySeyI for potential application in phosphor-converted white LEDs University of Alberta | Activity | 2022-07-27 | Dundappa Mumbaraddi, Vidyanshu Mishra, Mohammad Jomaa, Xiaoyuan Liu, Abhoy Karmakar, "Sambhavi Thirupurasanthiran", Al Meldrum, Andrew P Grosvenor, Vladimir K Michaelis, Mar, A. |
| Machine Learning and Models: How we find optimal materials for Solar and CCS technologiesFuture Energy Systems hosted its first Interdisciplinary Lunch and Learn, Machine Learning and Models: How we find optimal materials for Solar and CCS technologies. The session brought together two research groups from different themes and faculties that had never previously had an opportunity to collaborate. University of Alberta | Activity | 2018-05-15 | Oliynyk, A., Alex Gzyl, Jan Poehls, Mar, A., Rajendran, A., Gokul Sai Subraveti, Kasturi Nagesh Pai, Prasad, V. |
| Machine-learning classification of Laves phases and prediction of new compounds Nanode Battery Technologies, University of Alberta | Activity | 2023-06-08 | Ritobroto Sikdar, Selvaratnam, B., Vidyanshu Mishra, "Nilanjan Roy", Partha Pratim Jana, Mar, A. |
| Machine-learning prediction of new Laves phases with experimental validation University of Alberta | Activity | 2022-06-16 | Ritobroto Sikdar, Selvaratnam, B., "Partha Jana", "Nilanjan Roy", Mar, A. |
| Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler StructuresThe American Chemical Society Division of Inorganic Chemistry (DIC) is organizing monthly, Zoom-based symposia covering each subdivisions. The format is 2 speakers for 20 minute talks each, with each talk followed by 10 min Q&A. One will be 20-minute emerging faculty lecture (assistant professors), and the other a 20-minute established faculty lecture. University of Alberta | Activity | 2020-11-18 | Mar, A. |
| Machine-learning predictions and experimental validation of full- and half-Heusler structuresInvited presentation at the Solid Compounds of Transition Elements Conference, held virtually at Wrocław, Poland. University of Alberta | Activity | 2021-04-12 | Mar, A. |
| Machine-learning predictions of half-Heusler structures University of Alberta | Activity | 2019-10-01 | Mar, A. |
| Materials discovery of intermetallics through machine learning: Experimental validation and interpretable models Nanode Battery Technologies, Hunter College, University of Alberta | Activity | 2023-09-25 | Volodymyr Gvozdetskyi, Selvaratnam, B., Oliynyk, A., Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2023-04-27 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2023-02-17 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2023-07-08 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models Nanode Battery Technologies, Manhattan College, University of Alberta | Activity | 2023-06-07 | Volodymyr Gvozdetskyi, Selvaratnam, B., Oliynyk, A., Mar, A. |
| Mechanochemical processing of W-substituted solid-state electrolytes and its effects on electrochemical performance and crystal structure University of Alberta | Activity | 2022-06-16 | Fuwei Wen, Geng Xie, Mar, A., Sang, L. |
| New quaternary metallic phosphides University of Alberta | Activity | 2022-11-08 | Arkadii Pominov, Mar, A. |
| NSERC discovery grant roundtable University of Alberta | Activity | 2022-07-04 | Mar, A. |
| Predicting noncentrosymmetric quaternary tellurides using machine learning University of Alberta, Manhattan College, University of Ghana | Activity | 2021-07-28 | Selvaratnam, B., Oliynyk, A., Adutwum, L., Mar, A. |
| Predicting thermoelectric figures-of-merit for half-Heusler compounds using machine learning University of Alberta | Activity | 2022-06-16 | Selvaratnam, B., Volodymyr Gvozdetskyi, Mar, A. |
| Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)Contributed poster. University of Alberta | Activity | 2018-05-29 | Dong Zhang, Oliynyk, A., "Abishek Iyer", "Gabriel Duarte", "Leila Ghadbeigi", "Taylor Sparks", Mar, A. |
| Quaternary rare-earth transition-metal phosphides RE5M3Ni16P12 (M = Zr, Hf) University of Alberta | Activity | 2023-06-08 | Arkadii Pominov, Mar, A. |
| Rare earth gallium oxyselenides with unprecedented GaSe5 units as potential optical and thermoelectric materials University of Alberta | Activity | 2023-06-07 | Vidyanshu Mishra, Dundappa Mumbaraddi, Mohammad Jomaa, Abishek K Iyer, "Wenlong Yin", Mar, A. |
| Rare-earth transition-metal oxychalcogenides University of Alberta | Activity | 2021-07-28 | Vidyanshu Mishra, Dundappa Mumbaraddi, Abishek K Iyer, Mar, A. |
| Rare-earth transition-metal oxyselenidesContributed talk. University of Alberta | Activity | 2019-06-06 | Vidyanshu Mishra, Abishek K Iyer, Dundappa Mumbaraddi, Oliynyk, A., Mar, A. |
| Rare-earth transition-metal oxyselenides University of Alberta | Activity | 2022-06-16 | Vidyanshu Mishra, Mar, A. |
| Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX University of Alberta, Manhattan College | Activity | 2022-07-27 | Volodymyr Gvozdetskyi, Selvaratnam, B., Oliynyk, A., Mar, A. |
| Revisiting the classification of spinels through machine learning Nanode Battery Technologies, University of Alberta | Activity | 2023-06-06 | Vidyanshu Mishra, Selvaratnam, B., Mar, A. |
| Searching for new spinels using machine learning University of Alberta | Activity | 2022-06-17 | Vidyanshu Mishra, Selvaratnam, B., Mar, A. |
| Searching for new spinels using machine learning University of Alberta | Activity | 2022-07-27 | Vidyanshu Mishra, Selvaratnam, B., Mar, A. |
| Session chair at North American Solid State Chemistry Conference University of Alberta | Activity | 2023-08-02 | Mar, A. |
| Solid state chemistry - Influence of structure and form on properties University of Alberta | Activity | 2022-06-17 | Mar, A. |
| Solving the Colouring Problem in Half- Heusler Structures: Machine-Learning Predictions and Experimental ValidationPoster presentation at the North American Solid State Chemistry Conference. University of Alberta | Activity | 2019-07-31 | Alex Gzyl, Oliynyk, A., Adutwum, L., Mar, A. |
| Some concepts in machine learning, with applications to materials discovery University of Alberta | Activity | 2023-07-31 | Mar, A. |
| Structure and Luminescence Properties of Rare-Earth Chalcohalides RE3Ge2Ch8X (Ch = S, Se; X = Cl, Br, I)Contributed poster. University of Alberta | Activity | 2018-05-29 | Dundappa Mumbaraddi, "Abishek Iyer", "Ebru Üzer", "Tom Nilges", Mar, A. |
| Ternary and quaternary rare‐earth germanides: discovery of intermetallic compounds from traditional to machine‐learning approaches University of Alberta | Activity | 2018-09-05 | Mar, A. |
| Ternary phases in the Yb-Cu-Ga and Yb-Ni-Ga systems Manhattan College, University of Alberta | Activity | 2021-07-28 | Dundappa Mumbaraddi, Vidyanshu Mishra, Oliynyk, A., Mar, A. |
| The effect of electrolyte structure, ion conductivity, and decomposition due to mechanochemical processing University of Alberta | Activity | 2022-11-08 | Fuwei Wen, Mar, A., Sang, L. |
| USchool: Materials and InformaticsMini-lesson (2 hours) taught to Grade 8 students about materials and informatics, including several demonstrations on superconductivity, magnetism, solar cells, and thermoelectrics. Students and teachers were highly appreciative of this exciting lesson. University of Alberta | Activity | 2019-03-08 | Oliynyk, A., Alex Gzyl, "Yuqiao Zhou", "Yu Qiu", Dundappa Mumbaraddi, Mar, A. |
| USchool: Materials and InformaticsIn continuation of our commitment to this program, run by the University of Alberta Senate "to introduce and connect students in Grades 4 to 9 from socially vulnerable communities," my research group led a morning class on March 11, 2020 (two days before the university restricted in-person activities) to grade 5 students from St. Anne School to teach them about "Materials and Informatics." Two of my researchers and myself presented demonstrations about materials science (superconductivity, thermoelectrics) and informatics (Google Quick Draw). A second event scheduled for "Energy Week" on May 6, 2020 was cancelled. University of Alberta | Activity | 2020-03-11 | Dundappa Mumbaraddi, Hussien Osman, Mar, A. |
| Li-containing coloured intermetallic compounds and chalcogenides | Publication | 2023-05-31 | Mohammad Jomaa |
| Rare-earth-containing chalohalides and intermetallics | Publication | 2023-06-30 | Dundappa Mumbaraddi |
| Rare-Earth-Containing Selenides and Oxyselenides University of Alberta | Publication | 2022-12-08 | Vidyanshu Mishra, Mar, A. |
| A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family | Publication | 2019-01-01 | Alexander Gzyl |
| Ternary and Quaternary Rare-Earth Transition-Metal GermanidesThis is the M.Sc. thesis for my graduate student Dong Zhang. University of Alberta | Publication | 2019-01-16 | Dong Zhang, Mar, A. |
| Accelerating the Discovery of Materials: Machine-Learning ApproachThis was an invited seminar to the Carnegie Institution for Science. University of Alberta | Activity | 2019-02-25 | Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2018-02-23 | Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2018-02-21 | Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning ApproachesThis was an invited talk to the Chemical and Materials Engineering department as part of their "D. B. Robinson Distinguished Speakers Series." University of Alberta | Activity | 2018-09-20 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2018-04-20 | Oliynyk, A., Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-06 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-10 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-15 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-17 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-24 | Mar, A. |
| Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches University of Alberta | Activity | 2019-05-27 | Mar, A. |
| Bridging chemistry from high school to university University of Alberta | Activity | 2024-01-25 | Mar, A. |
| Bridging chemistry from high school to university University of Alberta | Activity | 2024-02-15 | Mar, A. |
| High-Throughput Approaches for Discovering Thermoelectric MaterialsThis was an invited seminar to the University of Waterloo as part of their seminar series for their CREATE program known as "HEATER" focused on the development of thermoelectric materials. The seminar was simultaneously broadcast to McMaster University and the University of Toronto. University of Alberta | Activity | 2018-10-26 | Mar, A. |
| How to look for compounds Hunter College, University of Alberta | Activity | 2017-05-22 | Oliynyk, A., Mar, A. |
| How to look for compounds Hunter College, University of Alberta | Activity | 2017-05-16 | Oliynyk, A., Mar, A. |
| How to look for compounds Hunter College, University of Alberta | Activity | 2017-05-02 | Oliynyk, A., Mar, A. |
| Machine learning in solid state chemistry: A workshop for the rest of usAnton and I organized a two-day workshop on machine learning for solid state chemistry, as a satellite meeting to the North American Solid State Chemistry Conference. Hunter College, University of Alberta | Activity | 2023-07-31 | Oliynyk, A., Mar, A. |
| Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler StructuresInvited virtual departmental colloquium at University of Saskatchewan University of Alberta | Activity | 2020-10-30 | Mar, A. |
| Machine-Learning Predictions and Experimental Validation of Heusler StructuresVirtual invited seminar at the University of Guelph. University of Alberta | Activity | 2020-11-25 | Mar, A. |
| Materials and informatics University of Alberta | Activity | 2023-01-09 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2022-08-24 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2022-08-25 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2022-08-26 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2023-07-07 | Mar, A. |
| Materials discovery through machine learning: Experimental validation and interpretable models University of Alberta | Activity | 2023-07-10 | Mar, A. |
| Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach University of Alberta | Activity | 2018-04-06 | Oliynyk, A., "Jakoah Brgoch", Mar, A. |
| Symmetry in art and chemistry; machine learningThis was a 1-hour class for the Edmonton Lifelong Learners Association as part of a series on Modern Aspects of Chemistry. University of Alberta | Activity | 2022-02-17 | Mar, A. |
| Active Learning for Optimum Experimental Design - Insight into Perovskite OxidesFinding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data is obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to ABO3 perovskites where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites (Ba(1-x)AxTi(1-y)ByO3) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B = Zr, Sn, Hf), aiming at the maximization of the energy storage density; stoichiometric ABO3 perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software; which has the potential to be applied in inorganic and organic synthesis (e.g.: search for the optimum concentrations, catalysts, reactants, temperatures and pH to improve the yield) and materials science (e.g.: search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites. University of Alberta | Publication | 2023-04-01 | Maicon Pierre Lourenco, Alain Tchagang, Shankar, K., Venkataraman Thangadurai, Dennis R Salahub |
| Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning Nanode Battery Technologies, University of Alberta | Publication | 2024-03-19 | Ritobroto Sikdar, "Nilanjan Roy", Selvaratnam, B., Vidyanshu Mishra, Dundappa Mumbaraddi, "Amit Mondal", "Krishnendu Buxi", Partha Pratim Jana, Mar, A. |
| Artificial Neural Network-Based Prediction of the Optical Properties of Spherical Core\textendash Shell Plasmonic MetastructuresThe substitution of time- and labor-intensive empirical research as well as slow finite difference time domain (FDTD) simulations with revolutionary techniques such as artificial neural network (ANN)-based predictive modeling is the next trend in the field of nanophotonics. In this work, we demonstrated that neural networks with proper architectures can rapidly predict the far-field optical response of core–shell plasmonic metastructures. The results obtained with artificial neural networks are comparable with FDTD simulations in accuracy but the speed of obtaining them is between 100–1000 times faster than FDTD simulations. Further, we have proven that ANNs does not have problems associated with FDTD simulations such as dependency of the speed of convergence on the size of the structure. The other trend in photonics is the inverse design problem, where the far-field optical response of a spherical core–shell metastructure can be linked to the design parameters such as type of the material(s), core radius, and shell thickness using a neural network. The findings of this paper provide evidence that machine learning (ML) techniques such as artificial neural networks can potentially replace time-consuming finite domain methods in the future. University of Alberta | Publication | 2021-03-01 | Ehsan Vahidzadeh, Shankar, K. |
| Coloured intermetallic compounds Li2ZnGa and Li2ZnInThis manuscript describes continuing efforts in our research group to identify new coloured intermetallics, which remaining exceedingly rare (perhaps ~102, or <1% of known intermetallic compounds) and largely limited to combinations with very expensive precious metals (e.g., Au, Pt). The origin for the appearance of colour in these compounds differs from the more familiar cases of inorganic semiconductors or insulators because there are no band gaps, charge transfer, or d-d/f-f transitions. The combination of colour and metallic lustre makes these compounds particularly attractive as decorative coatings and jewellery; more ambitiously, they may prove to be possible candidates for plasmonic materials. Our hypothesis is that Li-containing intermetallics synthesized with less expensive elements (e.g., Cu, Zn) would possess similar crystal and electronic structures as the previously known analogues, and thus be fruitful targets for new, more accessible coloured intermetallics. We report here the two new coloured intermetallics Li2ZnGa and Li2ZnIn, determine their crystal structures by powder X-ray diffraction methods, confirm the location of Li atoms (difficult to detect by XRD) through solid state 7Li NMR spectroscopy, quantitatively characterize the colour through optical reflectance measurements and extraction of CIE colour coordinates, and perform electronic structure calculations. University of Alberta | Publication | 2021-11-30 | Mohammad Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Madhu Sudan Chaudhary, "Grygoriy Dmytriv", Vladimir K Michaelis, Mar, A. |
| Coloured intermetallic compounds LiCu2Al and LiCu2GaThis paper describes the search for coloured metallic substances, which are very rare (in contrast to coloured semiconductors or insulators, which are typically compounds with significant ionic character, such as halides and oxides). Manhattan College, University of Alberta | Publication | 2020-09-02 | Vidyanshu Mishra, Abishek K Iyer, Dundappa Mumbaraddi, Oliynyk, A., "Guillaume Zuber", "Aurélien Boucheron", "Grygoriy Dmytriv", Guy Bernard, Vladimir K Michaelis, Mar, A. |
| Comparison of computational and experimental inorganic crystal structuresThis paper tackles one of the most compelling problems in materials discovery: Density functional theory calculations rely on crystal structures to predict properties, but how accurate are the structural parameters? This study is the first to evaluate the accuracy for >10,000 crystal structures of inorganic compounds reported both computationally and experimentally, to gauge their internal consistency. Within a day of the paper being published, I received this unsolicited message from a materials scientist: “These results will be fundamentally valuable for our computational materials design community!” University of Alberta | Publication | 2020-07-19 | Jan Poehls, Manon Heyberger, Mar, A. |
| Controlling the luminescence of rare-earth chalcogenide iodides RE3(Ge1–xSix)2S8I (RE = La, Ce, and Pr) and Ce3Si2(S1–ySey)8I University of Alberta | Publication | 2023-07-18 | Dundappa Mumbaraddi, Vidyanshu Mishra, Mohammad Jomaa, Xiaoyuan Liu, Abhoy Karmakar, "Sambhavi Thirupurasanthiran", Vladimir K Michaelis, Andrew P Grosvenor, Al Meldrum, Mar, A. |
| Discovery of Intermetallic Compounds from Traditional to Machine-Learning ApproachesAn invited review article on machine-learning approaches to materials discovery in a special issue of Accounts of Chemical Research on "Advancing Chemistry through Intermetallic Compounds." This paper was highlighted on the journal cover. University of Alberta | Publication | 2017-12-15 | Oliynyk, A., Mar, A. |
| Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABCThis paper illustrates that apparently "ambiguous" predictions (with probabilities in an intermediate range not close to zero or one) made by machine-learning methods can be interpreted in a meaningful way by relating them to the likely occurrence of structural polymorphism, as illustrated here for ternary phases ABC. The work is an important breakthrough in machine-learning methods for structural predictions. University of Alberta | Publication | 2017-11-13 | Oliynyk, A., Adutwum, L., Brent W Rudyk, Harshil Pisavadia, "Sogol Lotfi", "Viktor Hlukhyy", James J Harynuk, Mar, A., "Jakoah Brgoch" |
| Drop that activation energy: Tetragonal to cubic transformations in Na3PS4−xSex for solid state sodium ion battery materials University of Alberta | Publication | 2023-12-14 | Madhu Sudan Chaudhary, Arkadii Pominov, Dundappa Mumbaraddi, "Bryce Allen", "Jan Meyer", Anna Maria Kirchberger, Guy Bernard, "Tom Nilges", Mar, A., Vladimir K Michaelis |
| Effect of aliovalent bismuth substitution on structure and optical properties of CsSnBr3 University of Alberta | Publication | 2023-04-19 | Madhu Sudan Chaudhary, Abhoy Karmakar, Vidyanshu Mishra, Amit Bhattacharya, Dundappa Mumbaraddi, Mar, A., Vladimir K Michaelis |
| Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculationsThis investigation of RECuZnP2 (RE = rare earth) is the first in-depth study of transport properties in quaternary CaAl2Si2-type compounds, a promising class of thermoelectric materials. The new concept demonstrated is the combined application of DFT calculations with advanced physics-based scattering calculations to gain insight on the underlying mechanisms of thermoelectric properties. In contrast to existing research, which is impeded by restrictions of conventional DFT methods, our approach accurately models thermoelectric properties of complex materials exhibiting disorder, a pervasive feature of real materials. The results directly challenge the usual assumption that acoustic phonon scattering is the limiting factor of electron transport in most thermoelectric materials; instead, polar optical phonon scattering is likely more important than previously believed. Moreover, the calculations shed light on the unexpectedly low lattice thermal conductivity in RECuZnP2: despite high bulk moduli and speeds of sounds, strongly anharmonic bonding can significantly reduce the thermal conductivity. These insights on the mechanisms of electron and heat transport have broader implications to the materials science community, by guiding researchers to discover more efficient thermoelectric materials in applying new concepts to optimize their properties. The low computational cost of these methods could also herald an exciting era of DFT-guided materials discovery. University of Alberta | Publication | 2020-11-04 | Jan Poehls, "Sevan Chanakian", "Junsoo Park", Alex M Ganose, "Alexander Dunn", Nick Friesen, Amit Bhattacharya, "Brea Hogan", "Sabah Bux", "Anubhav Jain", Mar, A., "Alexandra Zevalkink" |
| Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental ValidationIn this manuscript, we present a machine-learning approach to confront a significant problem in the crystal chemistry of half-Heusler compounds, which belong to the larger family of Heusler compounds and which have important applications as thermoelectric and spintronic materials. In particular, we wish to identify new half-Heusler compounds that have existing full-Heusler counterparts, because the occurrence of such pairs enables thermoelectric and magnetic properties to be controlled and improved. What distinguishes our contribution from the majority of machine-learning studies targeted to discovering new materials is that the predictions made in our investigation are supported by experimental verification. Manhattan College, University of Alberta | Publication | 2020-09-01 | Alex Gzyl, Oliynyk, A., Mar, A. |
| Hexagonal Double Perovskite Cs2AgCrCl6Published in a special issue celebrating the 60th birthday of Thomas Fässler (a renowned main-group
inorganic chemist at the Technical University of Munich), this invited paper reports on a new double
perovskite (belong to the family of halide perovskites that are of current popular interest as photovoltaic materials) that is unexpectedly not cubic but rather hexagonal. University of Alberta | Publication | 2018-10-31 | "Yuqiao Zhou", "Abdelrahman Askar", Jan Poehls, Abishek K Iyer, Oliynyk, A., Shankar, K., Mar, A. |
| How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic PhotovoltaicsThis widely publicized paper, in the form of a “Perspective,” was the result of an ongoing collaboration between the Buriak and Mar group, illustrating how machine learning is applied to optimize several experimental conditions so that efficiencies of photovoltaic devices can be rapidly improved. [To date, this paper has been viewed over 7100 times in just over a year.] University of Alberta | Publication | 2018-07-01 | Bing Cao, Adutwum, L., Oliynyk, A., Erik Luber, Brian Olsen, Mar, A., Jillian Mary Buriak |
| Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell NanocylindersThe application domain of deep learning (DL) has been extended into the realm of nanomaterials, photochemistry, and optoelectronics research. Here, we used the combination of a computer vision technique, namely convolutional neural network (CNN), with multilayer perceptron (MLP) to obtain the far-field optical response at normal incidence (along cylinder axis) of concentric cylindrical plasmonic metastructures such as nanorods and nanotubes. Nanotubes of Si, Ge, and TiO2 coated on either their inner wall or both their inner and outer walls with a plasmonic noble metal (Au or Ag) were thus modeled. A combination of a CNN and MLP was designed to accept the cross-sectional images of cylindrical plasmonic core-shell nanomaterials as input and rapidly generate their optical response. In addition, we addressed an issue related to DL methods, namely explainability. We probed deeper into these networks’ architecture to explain how the optimized network could predict the final results. Our results suggest that the DL network learns the underlying physics governing the optical response of plasmonic core-shell nanocylinders, which in turn builds trust in the use of DL methods in materials science and optoelectronics. University of Alberta | Publication | 2023-03-01 | Ehsan Vahidzadeh, Shankar, K. |
| Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future DirectionsAdvances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder–Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions. University of Alberta | Publication | 2022-02-01 | Xinkai Xu, Dipesh Aggarwal, Shankar, K. |
| Interpretable machine learning in solid state chemistry, with applications to perovskites, spinels, and rare-earth intermetallics: Finding descriptors using decision trees Nanode Battery Technologies, Manhattan College, University of Alberta | Publication | 2023-06-30 | Selvaratnam, B., Oliynyk, A., Mar, A. |
| La4Ga2Se6O3: A rare-earth oxyselenide built from one-dimensional strips University of Alberta | Publication | 2022-07-25 | Vidyanshu Mishra, Alexander Zabolotnii, Mar, A. |
| Lost horses on the frontier: K2BiCl5 and K3Bi2Br9This manuscript addresses the absence of some members of the defect perovskite halides A3Bi2X9, of which the most well known are the iodides, especially Cs3Bi2I9, which have been identified as promising candidates for lead-free alternatives to the halide semiconductors APbX3 for photovoltaic materials. In particular, among the bromides, A3Bi2Br9 is known for A = Rb, Cs, Tl, but not K, and among the chlorides, A3Bi2Cl9 is known only for A = Cs. It is curious that there remain such missing members despite the enormous effort of numerous research groups embarked on the investigation of these compounds. Have these missing members simply been lost, or have they been neglected because their expected band gaps may have been assumed to be unsuitably large? In this study, we present the attempted preparation of K3Bi2Cl9 and K3Bi2Br9. Although we failed to prepare K3Bi2Cl9, obtaining K2BiCl5 instead, we confirmed the existence of K3Bi2Br9. The most significant result is that the measured band gap of K3Bi2Br9 is 2.2 eV, not that much different from those of the iodides (1.92.1 eV) and contradicting various predictions of a much larger band gap. University of Alberta | Publication | 2021-09-28 | "Yuqiao Zhou", "Yu Qiu", Vidyanshu Mishra, Mar, A. |
| Low bandgap carbon nitride nanoparticles incorporated in titania nanotube arrays by in situ electrophoretic anodization for photocatalytic CO 2 reductionWe report an in situ electrophoretic anodization process to realize a binary semiconductor heterojunction photocatalyst comprising green-emitting, water-soluble carbon nitride (CN) nanoparticles (NPs) embedded in TiO2 nanotube (TNT) arrays. Embedding CN inside a TiO2 matrix eliminates the possibility of the CNNPs leaching away during photocatalysis or photoelectro-chemistry. The synthesized CN exhibits visible light absorption down to 600 nm and an unusually redshifted green emission peak at 527 nm, which are attributed to a carbon rich g-C3N4 composition with a C:N ratio of ∼ 1.9 at the surface. Spectroscopy revealed the excess carbon to be both amorphous and graphitic while the structural features characteristic of g-C3N4 were preserved. Raman spectroscopy, transmission electron microscopy (TEM), electron energy-loss spectroscopy (EELS) and X-ray photoelectron spectroscopy (XPS) analysis verified the formation of the heterostructure as well as indicated strong interaction between the CN and TiO2 in the hybrid. The CNNP@TNT hybrid demonstrated superior performance in sunlight driven photocatalytic CO2 reduction without the need for a sacrificial agent. The CO yield of photo-reduction showed a more than threefold improvement for the CNNP@TNT hybrid compared to the stand-alone TNT photocatalyst. The synergistic enhancement of photocatalytic performance emerged due to the formation of a high-quality interface between the constituent semiconductors (TiO2 and CN) that facilitated efficient charge carrier separation. Density functional theory (DFT) calculations showed the feasibility of efficient photogenerated electron-hole pair separation at the heterointerface. Molecular dynamics (MD) simulations validated the facile dispersibility of CNNPs in water and polar solvents. University of Alberta | Publication | 2023-01-01 | Kazi Alam, Narendra Chaulagain, Ehsan Shahini, Md Masud Rana, John Garcia, Navneet Kumar, Alexander E Kobryn, Sergey Gusarov, Tian Tang, Shankar, K. |
| Mechanochemistry in sodium thioantimonate solid electrolytes: Effects on structure, morphology, and electrochemical performance University of Alberta | Publication | 2023-08-09 | Fuwei Wen, Geng Xie, "Ning Chen", Qichao Wu, Madhu Sudan Chaudhary, Xiang You, Vladimir K Michaelis, Mar, A., Sang, L. |
| Mercurial possibilities: determining site distributions in Cu2HgSnS4 using 63/65Cu, 119Sn, and 199Hg solid-state NMR spectroscopy University of Alberta | Publication | 2022-09-21 | Amit Bhattacharya, Vidyanshu Mishra, Dylan G Tkachuk, Mar, A., Vladimir K Michaelis |
| Mere anarchy is loosed: Structural disorder in Cu2Zn1-xCdxSnS4 University of Alberta | Publication | 2021-06-03 | Amit Bhattacharya, Dylan G Tkachuk, Mar, A., Vladimir K Michaelis |
| Minority report: Structure and bonding of YbNi3Ga9 and YbCu3Ga8 obtained in gallium fluxThis manuscript presents a detailed investigation of two ternary gallides, YbNi3Ga9 and YbCu3Ga8. To contend with difficulties in preparing Yb-containing intermetallics through conventional routes, gallium flux reactions were performed. Although flux methods are well known, predicting their outcomes is not obvious, and we test a previously proposed stability diagram for determining whether crystals of ternary intermetallics are likely to be obtained. YbNi3Ga9 has been recently identified as an intermediate valence compound but its structure was not clear. Here, we have finally succeeded in performing a structure determination of YbNi3Ga9, which suffers from severe twinning problems. YbCu3Ga8 is a new, apparently metastable, ternary phase in the Yb-Cu-Ga, in which many other Ga-rich phases are known. By applying the concept of “crystal orbital bond index,” developed recently by Dronskowski, we provide evidence for multicentre bonding containing significant covalent character within the anionic Ga networks in these compounds, confirming assertions of electron transfer that have often been proposed, but rarely confirmed, for such gallides. University of Alberta | Publication | 2022-04-20 | Dundappa Mumbaraddi, Vidyanshu Mishra, "Sven Lidin", Mar, A. |
| Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal ConductivityThis paper tackles two scientific challenges, the first in the synthesis of a large number (73) of intermetallic quaternary germanides (more than doubling the previously known examples) and the second in validating machine-learning predictions for discovering compounds with low thermal conductivity (a key criterion for thermoelectric materials, which provide a possible solution for energy conversion of heat to electricity). A reviewer of this paper noted that “Mar is a pioneer in the application of machine learning for inorganic chemistry.” University of Alberta | Publication | 2018-10-26 | Dong Zhang, Oliynyk, A., "Gabriel Duarte", "Abishek Iyer", "Leila Ghadbeigi", "Steven Kauwe", "Taylor Sparks", Mar, A. |
| Quaternary rare-earth oxyselenides RE4Ga2Se7O2 (RE = Pr, Nd) with trigonal bipyramidal GaSe5 units: Evaluation of optical, thermoelectric, and electrocatalytic properties University of Alberta | Publication | 2024-01-02 | Vidyanshu Mishra, Ibrahim Munkaila Abdullahi, Dundappa Mumbaraddi, Mohammad Jomaa, Louis Guérin, "Manashi Nath", Mar, A. |
| Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge) University of Alberta | Publication | 2019-08-15 | "Yuqiao Zhou", Abishek K Iyer, Oliynyk, A., Manon Heyberger, "Yixuan Lin", "Yu Qiu", Mar, A. |
| Rare-earth indium selenides RE3InSe6 (RE = La-Nd, Sm, Gd, Tb): Structural evolution from tetrahedral to octahedral sitesThis manuscript presents a detailed analysis of a series of ternary rare-earth selenides RE3InSe6 revealing new insight on a previously unrecognized structural feature, in which substitution with smaller RE atoms is accompanied by a gradual change in the coordination of In atoms within a linear stack from tetrahedral to octahedral geometry. Importantly, this analysis draws a connection between the various series RE3MCh6 (M = Ga, In; Ch = S, Se) indicating that there is a continuum from noncentrosymmetric to centrosymmetric structures, and suggests that these other series need to be reexamined with a more critical eye. Optical measurements show direct band gaps (1.2-1.4 eV) that match closely with those desirable for solar cells, and thus these compounds may be attractive as photovoltaic materials. We hope that the results will interest structural chemists investigating chalcogenides as well as materials scientists looking for promising new candidates. University of Alberta | Publication | 2021-02-27 | Vidyanshu Mishra, Dundappa Mumbaraddi, Abishek K Iyer, Mar, A. |
| Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX Nanode Battery Technologies, Manhattan College, University of Alberta | Publication | 2023-01-30 | Volodymyr Gvozdetskyi, Selvaratnam, B., Oliynyk, A., Mar, A. |
| Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf–In SystemThis paper applies a combined approach of machine learning, first-principles calculations, and experimental validation to discover a new binary intermetallic compound. The important insight gained from this work is that despite complexities such as disorder and site mixing, machine-learning methods can nevertheless be useful in helping to predict an initial model. University of Alberta | Publication | 2018-06-21 | Oliynyk, A., "Michael Gaultois", "Martin Hermus", "Andrew Morris", Mar, A., "Jakoah Brgoch" |
| Semiconducting Sm3GaSe5O with trigonal bipyramidal GaSe5 unitsThis manuscript presents the new oxyselenide, Sm3GaSe5O, obtained after longstanding efforts to prepare mixed-anion compounds, which offer the promise of greater tunability of physical properties. The results are remarkable in several respects. First, the structure type is new and features unprecedented trigonal bipyramidal GaSe5 units (CN5), in contrast to the more typical tetrahedral GaSe4 units (CN4) found in known Ga-containing selenides. To our knowledge, this is the first observation of five-coordinate Ga surrounded by Se atoms; for comparison, even GaO5 units are quite rare. Second, statements often made in the literature that oxychalcogenides can be partitioned into “more ionic” oxide and “more covalent” chalcogenide blocks, but these claims have rarely been evaluated. By analyzing electron localization function (ELF) plots, we have obtained evidence that agrees surprisingly well with this assertion. Third, although most oxychalcogenides have relatively large band gaps (typically >2 eV), Sm3GaSe5O has a nearly direct band gap of 1.2 eV, similar to that of silicon. Thus, this compound may be a good candidate as an optical material. University of Alberta | Publication | 2022-01-13 | Vidyanshu Mishra, Dundappa Mumbaraddi, Abishek K Iyer, "Wenlong Yin", Mar, A. |
| Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental ValidationThis paper tackles a significant challenge in inorganic structural chemistry known as the “colouring problem,” namely how atoms are distribution over different sites in a crystal structure. For the first time, we successfully apply a machine-learning approach to predict the site distributions in the very large family several hundred compounds) of half-Heusler compounds, which find many applications as thermoelectric and spintronic materials. Unique to our approach, we validate these predictions with experimental measurements. University of Alberta | Publication | 2019-06-25 | Alex Gzyl, Oliynyk, A., Adutwum, L., Mar, A. |
| Structure and optical properties of LixAg1-xGaSe2 and LixAg1-xInSe2 University of Alberta | Publication | 2023-04-28 | Mohammad Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, "Diganta Sarkar", "Mengran Sun", "Jiyong Yao", Vladimir K Michaelis, Mar, A. |
| Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)Mixed-anion compounds in general are relatively scarce, so the synthesis of a new series of chalcogenide halides reported here is a significant achievement; they offer greater flexibility for controlling band gaps of semiconducting compounds. University of Alberta | Publication | 2019-03-18 | Dundappa Mumbaraddi, "Abishek Iyer", "Ebru Üzer", Vidyanshu Mishra, Oliynyk, A., "Tom Nilges", Mar, A. |
| Ternary rare-earth-metal nickel indides RE23Ni7In4 (RE = Gd, Tb, Dy) with Yb23Cu7Mg4-type structure University of Alberta, Manhattan College | Publication | 2021-11-21 | Yurij B Tyvanchuk, "Matthew Fecica", "Griheydi Garcia", Mar, A., Oliynyk, A. |
| The centre cannot hold University of Alberta | Publication | 2022-09-13 | Mar, A. |
| Thermoelectric properties of inverse perovskites A3TtO (A = Mg, Ca; Tt = Si, Ge): Computational and experimental investigationsPublished in a special issue dedicated to the topic of “Advanced Thermoelectrics,” this invited paper reports high-level computational predictions of an unusual class of solids, namely inverse perovskites, that have
rarely been reported as thermoelectric materials; the predictions were validated by experimental
measurements of physical transport properties. University of Alberta | Publication | 2019-07-12 | Jan Poehls, Mar, A. |
| Three Rh-rich ternary germanides in the Ce-Rh-Ge systemThis manuscript presents a lengthy analysis of the Rh-rich region of the Ce-Rh-Ge phase diagram, culminating in the discovery and detailed structural characterization of three new ternary germanides: Ce3Rh11Ge5, Ce6Rh30Ge19.5, and CeRh3Ge2. Each of these compounds would have been worthy of separate papers, but we have opted to combine these results to achieve a unified presentation. For context, ternary Ce-Rh-Ge phases have been a particularly rich source of exotic physical phenomena (e.g., heavy fermion behaviour, superconductivity, valence fluctuations), so the identification of new phases in this system opens up even more opportunities to uncover unusual properties in these materials. Early efforts were made nearly 30 years ago to investigate the Ce-Rh-Ge phase diagram systematically, but about half of the 20 claimed ternary phases have never been properly characterized. Two of the compounds we have prepared are probably unrelated to these previously claimed phases, indicating that the Ce-Rh-Ge system is much more complicated than originally imagined. The structure determinations of these compounds were not trivial, and we have expended significant effort to describe these structures in relation to known types, including developing group-subgroup relationships. Finally, we have carried out a bonding analysis using electron localization functions (ELF) to demonstrate that charge transfer occurs from Ce atoms, not to Ge but rather to Rh atoms, indicating the presence of negatively charged Rh species in these polar intermetallic compounds. Manhattan College, University of Alberta | Publication | 2021-09-11 | Dong Zhang, Oliynyk, A., Mar, A. |
| True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds University of Alberta | Publication | 2022-06-28 | Mohammad Jomaa, Vidyanshu Mishra, Madhu Sudan Chaudhary, Dundappa Mumbaraddi, Vladimir K Michaelis, Mar, A. |
