Dr. Alpha Lee delivered an A3MD Distinguished Seminar entitled: “Accelerating the materials design-make-test cycle with machine learning and coarse graining”
Unlike molecular chemistry, where valance-bond theory provides a robust framework to represent molecules as a graph, representing inorganic materials is significantly more intricate. On the one hand, representing a material using composition alone removes the possibility of identifying polymorphs. On the other hand, a representation based on distances between atoms is challenging to deploy in a computational high throughput screening workflow for novel materials because it is a priori unclear whether a particular composition/structure is thermodynamically stable. In my talk, I will discuss our journey in applying concepts in coarse graining to devise machine learning models for materials properties prediction and materials synthesis prediction. I will first discuss the lowest level of coarse graining – stoichiometry – and discuss a representation learning framework that predicts materials properties using stoichiometry as input. I will then show how this framework can be extended to predict the outcomes of materials synthesis, whilst also providing an interpretable “reaction similarity” metric that enables rapid search for literature precedents in materials synthesis. Finally, I will move up the coarse graining ladder and discuss how we can incorporate structure, albeit in a coarse-grained way, using the concept of Wyckoff representation. This approach allows us to effectively amortise crystal structure searching, turning an infinite search space into a combinatorially enumerable search problem.
Professor John Gregoire delivered an A3MD Distinguished Seminar entitled: “Accelerating discovery of solar fuels materials with high throughput experiments and artificial intelligence”
The large data flux of high throughput experiments naturally presents opportunities for data science. Addressing the most pressing materials discovery challenges with these techniques requires design of experiments and algorithms in the context of the materials physics and chemistry that are pertinent to the target technology. The corresponding quest for materials that harvest solar energy to generate chemical fuels has led to research problems that challenge the state of the art in artificial intelligence. The ensuing efforts to generate new algorithms and modalities of research include 1) the development of deep reasoning networks that incorporate physics rules in machine learning, which is critical for automating crystal structure phase mapping; 2) the incorporation of hierarchical correlation learning in multi-property prediction, which conditions models to facilitate prediction in never-before-seen composition spaces; and 3) probabilistic modeling of data in high order composition spaces, which identifies materials most likely to exhibit exceptional properties for any target technology. While these efforts arose from specific research challenges, the methods and concepts are intended to be generally applicable for accelerating scientific discovery via adaption and improvement by the research community.
Professor Laura Gagliardi delivered an A3MD Distinguished Seminar entitled: “Theoretical and Computational Challenges in Modeling MOF-Based Catalysis and Water Harvesting”
Metal-organic frameworks (MOFs) are versatile platforms with tunable properties ranging from high selectivity in gas separations, to catalytic activity for complex reactions, to unique magnetic properties. In collaboration with experimentalists, we try to understand the activity of MOF-based catalysts for reactions related to natural gas conversion, e.g., catalytic oligomerization of abundant C1, C2, and C3 hydrocarbons to longer congeners, or their selective oxidation to alcohols or other fuel molecules. Modeling these species poses enormous challenges from a theoretical and computational perspective. I will describe our latest results in modeling light-alkane hydroxylation over Fe-based MOFs. I will also describe our combined computational and data-science approach to explore MOFs quantum-chemical properties.
A3MD welcomes Prof. Jason (Jae) Hattrick-Simpers as a new academic co-Principal Investigator.
Jason Hattrick-Simpers is a Professor at the Department of Materials Science and Engineering, University of Toronto and a Research Scientist at CanmetMATERIALS. He graduated with a B.S. in Mathematics and a B.S. in Physics from Rowan University and a Ph.D. in Materials Science and Engineering from the University of Maryland. Prof. Hattrick-Simpers’s research interests focus on the use of AI and experimental automation to discover new functional alloys and oxides that can survive in extreme environments and materials for energy conversion and storage. Specific topics of interest to the group include corrosion resistant ultra-hard alloys, oxides, nitrides, and carbides; thermoelectric materials for heat to energy conversion; novel metals for hydrogen fueling stations; and oxides for CO2 conversion.
Prior to joining UofT Prof. Hattrick-Simpers was a staff scientist at the National Institute of Standards and Technology (NIST) in Gaithersburg, MD where he co-developed tools for discovering novel corrosion resistance of alloys, developed active learning approaches to guide thin film and additive manufacturing alloy studies, and developed tools and best practices to enable trust in AI within the materials science community. He has published over 80 papers and given more than 50 invited seminars and talks. He was an associate editor of ACS Combinatorial Science from 2017 – 2020 and is part of the organizing committee for the International Workshop on Combinatorial Materials Science and Technology.
A3MD welcomes Prof. Zheng-Hong Lu as a new academic co-Principal Investigator.
Dr. Zheng-Hong Lu is a full professor and a Tier I Canada Research Chair in Organic Optoelectronics at the University of Toronto. He received a PhD degree in engineering physics in 1990 from Ecole Polytechnique of the University of Montreal, Canada. Prior to his current appointment, he was employed by the National Research Council (NRC) as an assistant and then an associate research officer. While at NRC he developed a number of materials and processes for microelectronics and optoelectronics, in particular, light-emitting silicon superlattices, dielectrics for silicon transistors, and surface passivation for solid-state lasers. In 1998, he moved to the University of Toronto to create an Organic Optoelectronics Research Group. His group’s research includes OLED materials and device engineering for flat-panel display and solid-state lighting applications.
A3MD and Microsoft have partnered to advance research in AI for clean energy materials discovery.
As a committed leader in low-carbon energy transitions, Microsoft is investing in renewable electricity, carbon removal, and research in new carbon management technology.
Microsoft and A3MD will work together to tackle grand challenges in global decarbonization through the development of new clean energy materials for carbon utilization.
Professor Yousung Jung delivered an A3MD Distinguished Seminar entitled: “AI-Accelerated Materials Structure-Property-Synthesizability Prediction”
The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, materials informatics deals with the use of data, informatics, and machine learning (complementary to expert’s intuitions) to establish the materials structure-property relationships and to make a new functional discovery in a rate that is significantly accelerated. In forward mapping, one aims to predict the materials properties using their structures as input, encoded in various ways such as simple attributes of constituent atoms, compositions, structures in graph forms, and etc, while in inverse mapping, one defines the desired property first and attempts to find the materials with such property. In this talk, I will mainly focus on the method of machine learning for fast and reliable evaluation of materials properties with a particular emphasis on catalysis applications, and also describe a method to predict the synthesizability of inorganic crystals based on structural encoding.
Professor Geoffroy Hautier delivered an A3MD Distinguished Seminar entitled: “Finding the needle in the haystack: accelerated identification of materials with exceptional opto-electronic properties”
Essential materials properties can now be assessed through ab initio methods. When coupled with the exponential rise in computational power, this predictive power provides an opportunity for large-scale computational searches for new materials. We can now screen thousands of materials by their computed properties even before starting any experimental work. This computational paradigm allows experimentalists to focus on the most promising candidates and enable researchers to efficiently and rapidly explores new chemical spaces. In this talk, I will present how this approach has been used to identify unexpected materials with exceptional opto-electronic properties. I will especially focus on some of our latest work in several fields from new p-type transparent conducting materials to electrides. I will outline the challenges and opportunities in the field of high-throughput materials discovery and discuss the growing use of machine learning and materials databases such as the Materials Project.
Professor Jan Jensen of U. Copenhagen delivered an A3MD Distinguished Seminar entitled: “Chemical Space Exploration”
I’ll talk about how we use quantum chemistry, genetic algorithms, and machine learning to search chemical space for molecules with specific properties. Examples include molecules that absorb light at specific wavelengths, bind to protein targets, or catalyse reactions. I’ll also discuss how we help ensure that the molecules are synthetically accessible.