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Machine learning for accuracy in density functional approximations
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DOI：10.1002/jcc.27366.png)
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摘要
En 中文
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets. Machine learning techniques allow us, where benchmark data are available, to train electronic structure models that substantially increase the predictive power of density functional theory simulations of chemical reactions and structural and thermodynamic properties of gas, liquid, and solid phases. Not only can quantitative improvements be achieved, but also fundamental limitations of density functional approximations can be corrected for. Here, techniques, benchmark data, and challenges for devising transferable electronic structure machine learning models are reviewed. image
Keyword:
density functional theory
electron delocalization
exchange-correlation functional
machine learning
materials prediction
self-interaction
期刊
IF:
4.8
论文数:
7.1K
被引数:
6.1W
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