Temporary yet fatal: metastable electronic states as a gateway for electron-attachment induced chemistry.
MIT Building 4, Room 163
Electronic states metastable with respect to electron ejection are ubiquitous in highly energetic environment, chemical, and biological systems, and often lead to chemical destruction. Prediction of the energetics and lifetimes of the metastable states, resonances, is crucial for understanding the processes of electron capture and the resulting chemical conversion.
In this talk I will give a general introduction to the phenomenon of metastable states and to the theoretical tools for treatment of resonances. Specifically, the focus will be on non-Hermitian quantum mechanics approaches for calculating energies and lifetimes of metastable electronic states from the first principles . Non-Hermitian formalisms allow one to exploit quantum chemistry methods developed for conventional bound electronic states for treatment of resonances, which belong to continuous spectrum. I will also discuss the role of the metastable states in chemical and biological processes. In particular, I will present the result of our recent computational studies of electronic structure para-benzoquinone, prototypical biological electron acceptor, and highlight the role of resonances in electron capture by the molecule .
1. D. Zuev, T.-C. Jagau, K.B. Bravaya, E. Epifanovsky, Y. Shao, E. Sundstrom, M. Head-Gordon, and A.I. Krylov. Complex absorbing potentials within eom-cc family of methods: Theory, implementation, and benchmarks. J. Chem. Phys., 141:024102, 2014.
2. A.A. Kunitsa and K.B. Bravaya; First-principles calculations of the energy and width of the 2Au shape resonance in p-benzoquinone, a gateway state for electron transfer. J. Phys. Chem. Lett., 6:1053–1058, 2015.
Accelerating Materials Discovery with Data-Driven Atomistic Computational Tools
MIT Building 4, Room 163
Many of the key technological problems associated with alternative energies (e.g., thermoelectrics, advanced batteries, hydrogen storage, etc.) may be traced back to the lack of suitable materials. Both the materials discovery and materials development processes may be greatly aided by the use of computational methods, particular those atomistic methods based on density functional theory (DFT). Here, we present an overview of our recent work utilizing high-throughput computation and data mining approaches to accelerate materials discovery, specifically highlighting three new approaches: (i) We describe our high-throughput DFT database, the Open Quantum Materials Database (OQMD)1, which contains over 280,000 DFT calculations and is freely available for public use at http://oqmd.org. (ii) We show how computational crystal structure solution may be addressed via a new hybrid approach, the First-Principles Assisted Structure Solution (FPASS) approach2, which combines experimental diffraction data, symmetry information, and first-principles-based evolutionary algorithmic optimization to automatically solve crystal structures. (iii) We also describe a newly-developed machine learning approach3,4 to construct a materials screening model based on an extensive set of thousands of DFT calculations. The resulting model, which has “learned” rules of chemistry from these many examples, can predict the stability of arbitrary compositions without requiring any a priori knowledge of crystal structure, at about six orders of magnitude lower computational expense than the original QM tools. We use this model to scan—in a matter of minutes—roughly 1.6 million candidate compositions for novel ternary compounds (AxByCz), and predict roughly 4,500 new stable materials.
1) J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton "Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Mechanical Database (OQMD)", JOM 65, 1501 (2013).
2) B. Meredig and C. Wolverton, "A Hybrid Computational-Experimental Approach for Crystal Structure Solution" Nature Materials 12, 123 (2013).
3) B. Meredig and C. Wolverton, “Dissolving the Periodic Table in Cubic Zirconia:
Data Mining to Discover Chemical Trends” Chem. Mater. 26, 1985 (2014).
4) B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, "Combinatorial screening for new materials in unconstrained composition space with machine learning", Phys. Rev. B 89, 094104 (2014).