Machine learned interatomic potentials for ternary carbides trained on the AFLOW database

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Josiah Roberts, Biswas Rijal, Simon Divilov, Jon-Paul Maria, William G. Fahrenholtz, Douglas E. Wolfe, Donald W. Brenner, Stefano Curtarolo, Eva Zurek
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Abstract

Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: CHfTa, CHfZr, CMoW, and CTaTi. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the CHfTa, CHfZr, and CTaTi systems, and in the case of the CMoW system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred (Mo1−xWx)C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT.

Abstract Image

基于 AFLOW 数据库训练的三元碳化物的机器学习原子间位势
大密度泛函理论(DFT)数据库是能量、力和应力的宝库,可用于训练原子建模的机器学习原子间势。在这里,我们利用 AFLOW 数据库中的结构松弛来训练四个碳化物体系的力矩张量势 (MTP):CHfTa、CHfZr、CMoW 和 CTaTi。得到的 MTPs 用于弛豫 ~6300 个随机对称结构,随后通过主动学习进行改进,生成可弛豫多种结构的鲁棒势能 (RP) 和专为弛豫低能系统设计的精确势能 (AP)。结果表明,在 CHfTa、CHfZr 和 CTaTi 系统中,该方案生成的凸壳与 AFLOW 预测的凸壳无异,而在 CMoW 系统中,该方案预测的热力学稳定结构在 AFLOW 中是找不到的,这凸显了该方案在晶体结构预测中的潜力。先用 RP 再用 AP 对三百多个 (Mo1-xWx)C 原子序数晶体进行松弛,得到的形成焓与通过 DFT 得到的形成焓非常一致。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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