Developments and further applications of ephemeral data derived potentials.
Developments and further applications of ephemeral data derived potentials.
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DOI：
10.1063/5.0158710
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Pascal T. Salzbrenner;S. Joo;L. Conway;Peter I C Cooke;Bonan Zhu;Milosz P. Matraszek;W. Witt;C. Pickard
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文献类型：
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作者：
Pascal T. Salzbrenner;S. Joo;L. Conway;Peter I C Cooke;Bonan Zhu;Milosz P. Matraszek;W. Witt;C. Pickard
机器学习的原子间相互作用势正迅速成为计算材料科学中不可或缺的工具。一种方法是短暂的数据导出势（EDDP），它被设计用于加速原子结构预测。EDDP简单且具有成本效益。它依赖于在小单元格中生成的训练数据，并使用轻量级神经网络进行拟合，从而实现平滑的交互，表现出对结构预测至关重要的鲁棒可转移性。在这里，我们介绍了EDDP的各种应用，这些应用是由开源EDDP软件的最新发展实现的。新功能包括界面声子和分子动力学代码，以及部署的合奏偏差估计的信心EDDP预测。通过从元素碳和铅的二元氢化钪和三元氰化锌的案例研究，我们证明，EDDP可以训练，以涵盖广泛的压力和化学计量，并用于评估声子，相图，超离子性和热膨胀。这些发展补充了加速结构预测的持续成功。
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.