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Technol."],"published-print":{"date-parts":[[2021,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We present PyXtal_FF\u2014a package based on Python programming language\u2014for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy\/forces\/stress tensors in comparison with the data from <jats:italic>ab-initio<\/jats:italic> simulations. The trained MLP model from PyXtal_FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal_FF by applying it to investigate several material systems, including the bulk SiO<jats:sub>2<\/jats:sub>, high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal_FF is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/pyxtal-ff.readthedocs.io \" xlink:type=\"simple\">https:\/\/pyxtal-ff.readthedocs.io<\/jats:ext-link>.<\/jats:p>","DOI":"10.1088\/2632-2153\/abc940","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T22:35:58Z","timestamp":1605047758000},"page":"027001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["PyXtal_FF: a python library for automated force field generation"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-1074","authenticated-orcid":false,"given":"Howard","family":"Yanxon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Zagaceta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binh","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David S","family":"Matteson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9892-0344","authenticated-orcid":false,"given":"Qiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2020,12,28]]},"reference":[{"key":"mlstabc940bib1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/nmat700","article-title":"Dislocation processes in the deformation of nanocrystalline aluminium by molecular-dynamics simulation","volume":"1","author":"Yamakov","year":"2002","journal-title":"Nat. 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