Amp : A modular approach to machine learning in atomistic simulations

标题

作者

关键词

Potential energy surface, Neural networks, Atomic Simulation Environment (ASE), Density functional theory, Zernike polynomials

出版物

COMPUTER PHYSICS COMMUNICATIONS

Volume 207, Issue -, Pages 310-324

出版商

Elsevier BV

发表日期

2016-06-03

DOI

10.1016/j.cpc.2016.05.010

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