Perfecting Liquid-State Theories with Machine Intelligence.
J Phys Chem Lett
; 14(47): 10545-10552, 2023 Nov 30.
Article
em En
| MEDLINE
| ID: mdl-37975624
ABSTRACT
Recent years have seen a significant increase in the use of machine intelligence for predicting the electronic structure, molecular force fields, and physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging recent advancements in functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction, and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability, and computational efficiency, enabling their broader applications across diverse materials and chemical systems.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
J Phys Chem Lett
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos