报告人: Alexey Kolmogorov 教授

报告人单位: Binghamton University, SUNY

时间: 2016年4月11日(周一), 14:00

地点：师昌绪楼403室

Materials prediction and discovery accelerated with bio-inspired algorithms

A.N. Kolmogorov

Binghamton University, SUNY
Department of Physics, Binghamton, NY 13902-6000, USA

Evolutionary search, particle swarm optimization, and other recently introduced structure prediction methods have shown great promise for accelerating materials discovery. Yet, the number of confirmed predictions remains relatively low, especially in the field of superconductivity. We have employed a combination of high-throughput screening, targeted evolutionary search [1], and rational design to systematically examine over 12,000 metal-boron compounds [2]. This largest ab initio scan of the complex materials class has uncovered dozens of new exotic materials, some of which have already been synthesized in joint experiments [3-5]. In particular, the proposed and confirmed FeB4 compound with a previously unknown crystal structure appears to be the first superconductor developed fully in silico [5-7]. I will overview the prediction strategies and describe our on-going work using an emerging neural network methodology to accelerate materials development.

[1] A.N. Kolmogorov, http://maise-guide.org (2009)
[2] A.G. Van Der Geest and A.N. Kolmogorov, CALPHAD 46, 184 (2014)
[3] A. N. Kolmogorov, S. Shah, E. R. Margine, A. K. Kleppe, and A. P. Jephcoat, PRL 109, 075501 (2012)
[4] A.N. Kolmogorov, S. Hajinazar, C. Angyal, V.L. Kuznetsov, and A.P. Jephcoat, PRB 92, 144110 (2015)
[5] H. Gou, N. Dubrovinskaia, E. Bykova, A. A. Tsirlin, D. Kasinathan, A. Richter, M. Merlini, M. Hanfland, A. M. Abakumov, D. Batuk, G. Van Tendeloo, Y. Nakajima, A. N. Kolmogorov, L. Dubrovinsky, PRL 111, 157002 (2013)
[6] A.N. Kolmogorov, S. Shah, E.R. Margine, A.F. Bialon, T. Hammerschmidt, R. Drautz,  PRL 105, 217003 (2010)
[7] F. Ronning and J.L. Sarrao, Physics 6, 109 (2013)