{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:14:30Z","timestamp":1770779670253,"version":"3.50.0"},"reference-count":63,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Fossil Energy and Carbon Management Program, Advanced Coal Processing Program","award":["C4WARD"],"award-info":[{"award-number":["C4WARD"]}]},{"name":"Laboratory Directed Research and Development Program of Oak Ridge National Laboratory"},{"name":"Office of Science of the U.S. Department of Energy","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP\u2019s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress\u2013strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.<\/jats:p>","DOI":"10.1088\/2632-2153\/accd45","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T22:42:37Z","timestamp":1681512157000},"page":"035001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals\n                  <sup>**<\/sup>"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8047-6505","authenticated-orcid":true,"given":"Gang Seob","family":"Jung","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hunjoo","family":"Myung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4995-4991","authenticated-orcid":true,"given":"Stephan","family":"Irle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"mlstaccd45bib1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1038\/nature04408","article-title":"Dynamical fracture instabilities due to local hyperelasticity at crack tips","volume":"439","author":"Buehler","year":"2006","journal-title":"Nature"},{"key":"mlstaccd45bib2","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1038\/nature07297","article-title":"Low-speed fracture instabilities in a brittle crystal","volume":"455","author":"Kermode","year":"2008","journal-title":"Nature"},{"key":"mlstaccd45bib3","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1126\/science.1180476","article-title":"The near-tip fields of fast cracks","volume":"327","author":"Livne","year":"2010","journal-title":"Science"},{"key":"mlstaccd45bib4","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1016\/j.matt.2021.08.017","article-title":"Frank-van der Merwe growth in bilayer graphene","volume":"4","author":"Wang","year":"2021","journal-title":"Matter"},{"key":"mlstaccd45bib5","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"mlstaccd45bib6","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/s41524-019-0221-0","article-title":"Recent advances and applications of machine learning in solid-state materials science","volume":"5","author":"Schmidt","year":"2019","journal-title":"npj Comput. 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