arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2111.02206cond-mat.mtrl-sciphysics.comp-ph
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密度泛函晶体结构优化的机器学习校正

Machine-learning correction to density-functional crystal structure optimization

Robert Hussein, Jonathan Schmidt, Tomás Barros, Miguel A. L. Marques, Silvana Botti

摘要:

密度泛函理论通常用于预测晶体结构。最常用的交换相关泛函是Perdew-Burke-Ernzerhof(PBE)近似及其变体PBEsol。我们研究了这些泛函预测晶格参数的性能,并展示了如何使用机器学习提高其准确性。我们的数据集由无机晶体结构数据库的实验晶体结构与材料项目数据库中存储的PBE优化结构相匹配构成。我们补充这些数据与PBESol计算。我们证明了PBE/PBESol体积预测的准确性和精度可以通过采用简单,可解释的机器学习模型显着提高后验。这些模型可以改善PBE晶胞体积,以匹配PBESol计算的准确性,并将后者相对于实验的误差减少了35%。PBE晶格常数的误差减小了3 ~ 5倍。我们的方法的另一个好处是隐式修正有限的温度效应,而不执行声子计算。

英文摘要:

Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our dataset is constituted by experimental crystal structures of the Inorganic Crystal Structure Database matched with PBE-optmized structures stored in the materials project database. We complement these data with PBEsol calculations. We demonstrate that the accuracy and precision of PBE/PBEsol volume predictions can be noticeably improved a posteriori by employing simple, explainable machine learning models. These models can improve PBE unit cell volumes to match the accuracy of PBEsol calculations, and reduce the error of the latter with respect to experiment by 35%. Further, the error of PBE lattice constants is reduced by a factor of 3--5. A further benefit of our approach is the implicit correction of finite temperature effects without performing phonon calculations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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