Gerbrand Ceder, University of California, Berkeley, and Lawrence Berkeley National Laboratory
The Prediction of Stable and Metastable Compounds
The Materials Theory Award, endowed by Toh-Ming Lu and Gwo Ching Wang, recognizes exceptional advances made by materials theory to the fundamental understanding of the structure and behavior of materials.
Ceder is being honored “for seminal contributions to the emerging field of computationally guided materials exploiting high-throughput computation and promoting the development of open databases to enable widespread use."
A goal of ab initio
computational materials science is to predict novel compounds with interesting properties. This problem contains several fundamental challenges, from predicting whether a stoichiometry is stable, to predicting its structure. The lack of accurate structure prediction, in particular, has been termed a “continuous scandal” in materials science.1
Today several approaches to rather reliably predict structure exist, and many cases of computationally predicted compounds, verified by experiments, are documented. One approach to predict structure is to turn it into a mathematical minimization problem. I will demonstrate some recent advances to solve this minimization exactly within the framework of lattice models, thereby allowing the prediction of extremely complicated ground states. A radically different perspective on structure prediction is to use machine learning to train models on the tens of thousands of known crystal structures so that they can “suggest” the structure of novel compounds. This approach has been particularly powerful in predicting novel compounds in the energy space. Finally, an outstanding challenge is to address the fact that both nature and synthetic methods create many compounds that are metastable, but have tremendous use in practical applications. I will end with a discussion on how ab initio
methods can predict which non-ground-state compounds may exist, and how synthesis routes for them can be developed.
J. Maddox, Nature
About Gerbrand Ceder
is the Chancellor’s Professor of Materials Science and Engineering at the University of California, Berkeley, and a faculty scientist at the Lawrence Berkeley National Laboratory. He received an engineering degree from the University of Leuven, and a PhD degree in Materials Science from the University of California, Berkeley, in 1991.
His research interests focus on materials design through first-principles computations and experiments. He has developed methods for high-throughput computation, and integrates data mining and statistical learning in materials science. Current interests include materials design in energy storage, and learning how materials nucleate and form.
Ceder has published over 375 scientific papers, and holds several U.S. patents. He is a Fellow of the Materials Research Society and a member of the Royal Flemish Academy of Arts and Sciences. He has received the TMS Cohen Award, the MRS Gold Medal, the Battery Research Award from The Electrochemical Society, the Career Award from the National Science Foundation, and the Robert Lansing Hardy Award from The Minerals, Metals and Materials Society, as well as several teaching awards. Ceder is a co-founder of Computational Modeling Consultants, Pellion Technologies, and The Materials Project.