Multi-objective optimization of interatomic potentials with application to MgO
Multi-objective optimization of interatomic potentials with application to MgO
复制标题
原子间势的多目标优化及其在 MgO 中的应用
DOI：
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发表时间：
2019
影响因子：
1.8
通讯作者：
S. Phillpot
中科院分区：
文献类型：
--
作者：
E. J. Ragasa;C J O’Brien;R G Hennig;S. Foiles;S. Phillpot
将原子间相互作用势函数形式的参数化处理为多目标优化问题。一种基于识别预测属性中误差的帕累托超曲面的自主机器学习方法可以开发具有高材料保真度和鲁棒性的参数化集合。这种方法的有效性说明了一个简单的例子，白金汉潜在的MgO。这种方法也提供了一个强大的基础，潜在的参数化的不确定性量化。
The parameterization of a functional form for an interatomic potential is treated as a problem in multi-objective optimization. An autonomous, machine-learning approach based on the identification of the Pareto hypersurface of errors in predicted properties allows the development of an ensemble of parameterizations with high materials fidelity and robustness. The efficacy of this approach is illustrated for the simple example of a Buckingham potential for MgO. This approach also provides a strong foundation for uncertainty quantification of potential parameterizations.
影响因子：
8.6
作者：
Bartok, Albert P.;Payne, Mike C.;Csanyi, Gabor
通讯作者：
Csanyi, Gabor