669 citations · 3.8k across the 43 of their papers we have counts for
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PyXtal FF: a Python Library for Automated Force Field Generation
Howard Yanxon, David Zagaceta, Binh Tang +2
We present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomi…
Spectral Neural Network Potentials for Binary Alloys
David Zagaceta, Howard Yanxon, Qiang Zhu
In this work, we present a numerical implementation to compute the atom centered descriptors introduced by Bartok et al (Phys. Rev. B, 87, 184115, 2013) based on the harmonic analy…
Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon
Howard Yanxon, David Zagaceta, Brandon C. Wood +1
In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to us…
Prediction of new thermodynamically stable aluminum oxides
Yue Liu, Artem R. Oganov, Shengnan Wang +3
Recently, it has been shown that under pressure, unexpected and counterintuitive chemical compounds become stable. Laser shock experiments (A. Rode, unpublished) on alumina (Al2O3)…