FES Funded ProjectsOutputs
| Title |
Category |
Date |
Authors |
| 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.T12-P01 University of Alberta | Publication | 2017-12-15 | | | 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.T12-P01 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 " | | Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning ApproachesT12-P01 University of Alberta | Activity | 2018-04-20 | | | Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning ApproachT12-P01 University of Alberta | Activity | 2018-04-06 | | | 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.T12-P01 University of Alberta | Publication | 2018-06-21 | Oliynyk, A., "Michael Gaultois ", "Martin Hermus ", "Andrew Morris ", Mar, A., "Jakoah Brgoch " | | Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning ApproachesT12-P01 University of Alberta | Activity | 2018-02-23 | | | Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning ApproachesT12-P01 University of Alberta | Activity | 2018-02-21 | | | How to look for compoundsT12-P01 Hunter College, University of Alberta | Activity | 2017-05-22 | | | How to look for compoundsT12-P01 Hunter College, University of Alberta | Activity | 2017-05-16 | | | How to look for compoundsT12-P01 Hunter College, University of Alberta | Activity | 2017-05-02 | | | 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.T12-P01, T12-P02 University of Alberta | Publication | 2018-10-31 | | | 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.]T12-P01, T12-P04 University of Alberta | Publication | 2018-07-01 | | | 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.”T12-P01, T12-Z01 University of Alberta | Publication | 2018-10-26 | Dong Zhang, Oliynyk, A., "Gabriel Duarte ", "Abishek Iyer ", "Leila Ghadbeigi ", "Steven Kauwe ", "Taylor Sparks ", 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.T12-P01, T12-Z01 University of Alberta | Publication | 2019-03-18 | Dundappa Mumbaraddi, "Abishek Iyer ", "Ebru Üzer ", Vidyanshu Mishra, Oliynyk, A., "Tom Nilges ", 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.T12-P01, T12-Z01 University of Alberta | Publication | 2019-06-25 | | | 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.T12-P01 University of Alberta | Activity | 2018-05-28 | | | 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.T12-P01 University of Alberta | Activity | 2018-05-28 | | | 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.T12-P01, T12-Z01 University of Alberta | Activity | 2018-05-28 | | | Ternary Germanides in the Ce–M–Ge (M = Rh, Co) SystemsContributed talk. The Ce–Rh–Ge system was investigated as part of the search for new cerium intermetallics, which are excellent candidates to observe unusual magnetic and heavy-fermion behaviour.T12-Z01 University of Alberta | Activity | 2018-05-28 | | | Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)Contributed poster.T12-P01, T12-Z01 University of Alberta | Activity | 2018-05-29 | Dong Zhang, Oliynyk, A., "Abishek Iyer ", "Gabriel Duarte ", "Leila Ghadbeigi ", "Taylor Sparks ", Mar, A. | | In Search of Coloured IntermetallicsContributed poster.T12-P01, T12-Z01 University of Alberta | Activity | 2018-05-29 | Vidyanshu Mishra, "Abishek Iyer ", Oliynyk, A., Jan Poehls, Guy Bernard, Vladimir K Michaelis, Mar, A. | | Classification of Half-Heusler Compounds through Machine-Learning ApproachesContributed poster.T12-P01, T12-Z01 University of Alberta | Activity | 2018-07-25 | | | Accelerating the Discovery of Solid State Materials with Machine-Learning ApproachesContributed poster.T12-P01 University of Alberta | Activity | 2018-07-25 | | | 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.T12-P01 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. | | 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.T02-P02, T12-P01 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. | | 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.T12-P01 University of Alberta | Activity | 2018-09-13 | | | 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.T12-P01 University of Alberta | Activity | 2019-03-08 | | | Accelerating the Discovery of Materials: Machine-Learning ApproachThis was an invited seminar to the Carnegie Institution for Science.T12-P01 University of Alberta | Activity | 2019-02-25 | | | 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.T12-P01 University of Alberta | Activity | 2019-04-05 | | | Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency OptimizationInvited talk at the annual American Physical Society meeting, Boston, MA
https://meetings.aps.org/Meeting/MAR19/Session/B55.7T12-P04 University of Alberta | Activity | 2019-03-04 | | | Invited talk: ACS national meeting, Boston Invited seminar at NASA symposium on space exploration. Seminar was about solar energy optimization. Talk by Buriak.T12-P04 University of Alberta | Activity | 2019-04-18 | | | Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)Conference poster.T12-Z01 University of Alberta | Activity | 2018-05-28 | Dong Zhang, Oliynyk, A., G M Duarte,, A K Iyer,, "Ghadbeigi, L. ", T D Sparks,, Mar, A. | | Ternary Germanides in Ce-M-Ge System (M=Rh, Co)Conference presentation.T12-Z01 University of Alberta | Activity | 2018-05-30 | | | Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting BiomineralizationT02-Z01 University of Alberta | Publication | 2019-08-01 | Michelle Ha, M A Matlinska,, "Hughton, B. ", Oliynyk, A., A K Iyer,, Guy Bernard, "Lambkin, G. ", M C Lawrence,, M J Katz,, Mar, A., Vladimir K Michaelis | | Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)T12-P01 University of Alberta | Publication | 2019-08-15 | "Yuqiao Zhou ", Abishek K Iyer, Oliynyk, A., Manon Heyberger, "Yixuan Lin ", "Yu Qiu ", Mar, A. | | Rare-earth transition-metal oxyselenidesContributed talk.T12-P01 University of Alberta | Activity | 2019-06-06 | | | Exploring the colours of gold alloys with machine learningContributed posterT12-P01 University of Alberta | Activity | 2019-06-06 | | | Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proofContributed oral presentationT12-P01 University of Alberta | Activity | 2019-06-07 | | | Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome ThemContributed oral presentationT12-P01 University of Alberta | Activity | 2019-06-07 | | | Accelerated discovery of perovskites and prediction of band gaps using machine-learning methodsContributed oral presentationT12-P01 University of Alberta | Activity | 2019-06-07 | | | Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency OptimizationOrganic solar cells (OSCs) represent a cost-effective way to transform solar energy into electricity due to their potential for low-cost and high-throughput roll-to-roll production.[1] Improving OSC efficiency and stability are two of the most important tasks on the way toward commercialization. While much effort has been focused on developing new materials, there is enormous room with respect to optimization of the processing of OSCs to achieve optimal performance of a particular photoactive material. Morphology of the bulk heterojunction, the most important layer within an OSC, results from nanoscale phase segregation that depends extensively upon processing. However, the optimization process for an OSC is tedious, time-consuming and expensive involving many parameters. Conventional optimization uses unimodal approach where one parameter is optimized while all others are kept constant. This can take months or years. Moreover, there is an associated risk of missing the optimal results because all experimental combinations for all parameters cannot be performed. Herein, we report an approach that uses Design of Experiments (DOE) along with machine learning statistical data analysis to effectively and efficiently optimize solar cell efficiency. Machine learning algorithms are trained to find patterns in datasets which could greatly assist data analysis and parameter importance evaluation, hence predict the results for future experiments.[2] DOE methods allow experimentalists to explore a larger parameter space with fewer experimental trials while obtaining valid and objective conclusions. We show that, using the principles of DOE to plan experiments combined with using machine learning assist in the prediction of optimized solar cells. Specific examples of concrete improvement of the power conversion efficiency of OSCs will be described. T12-P04 University of Alberta | Activity | 2018-05-25 | Bing Cao, Adutwum, L., Brian Olsen, Oliynyk, A., Erik Luber, Tate Hauger, Mar, A., Jillian Mary Buriak | | 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.T12-P01, T12-Z01 Manhattan College, University of Alberta | Publication | 2020-09-01 | | | 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). T12-P01 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. | | Machine learning in solid-state chemistry: Heusler compoundsT12-P01, T12-Z01 University of Alberta, Manhattan College | Publication | 2021-04-27 | | | Solving the Colouring Problem in Half- Heusler Structures: Machine-Learning Predictions and Experimental ValidationPoster presentation at the North American Solid State Chemistry Conference.T12-P01 University of Alberta | Activity | 2019-07-31 | | | Ternary rare-earth-metal nickel indides RE23Ni7In4 (RE = Gd, Tb, Dy) with Yb23Cu7Mg4-type structureT12-P01 University of Alberta, Manhattan College | Publication | 2021-11-21 | | | 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. T12-P01 Manhattan College, University of Alberta | Publication | 2021-09-11 | | | Predicting noncentrosymmetric quaternary tellurides using machine learningT12-P01 University of Alberta, Manhattan College, University of Ghana | Activity | 2021-07-28 | | | Ternary phases in the Yb-Cu-Ga and Yb-Ni-Ga systemsT12-P01 Manhattan College, University of Alberta | Activity | 2021-07-28 | | | Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RXT12-P01 Nanode Battery Technologies, Manhattan College, University of Alberta | Publication | 2023-01-30 | | | Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RXT12-P01 University of Alberta, Manhattan College | Activity | 2022-07-27 | | | Interpretable machine learning in solid state chemistry, with applications to perovskites, spinels, and rare-earth intermetallics: Finding descriptors using decision treesT12-P01 Nanode Battery Technologies, Manhattan College, University of Alberta | Publication | 2023-06-30 | | | Materials discovery through machine learning: Experimental validation and interpretable modelsT12-P01 Nanode Battery Technologies, Manhattan College, University of Alberta | Activity | 2023-06-07 | | | 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.T12-P01 Hunter College, University of Alberta | Activity | 2023-07-31 | | | Materials discovery of intermetallics through machine learning: Experimental validation and interpretable modelsT12-P01 Nanode Battery Technologies, Hunter College, University of Alberta | Activity | 2023-09-25 | |
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