High-Dimensional Operator Learning for Molecular Density Functional Theory
Classical density functional theory (cDFT) provides a systematic framework to predict the structure and thermodynamic properties of chemical systems through molecular density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its applications are often constrained by c...
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| Published in: | Journal of chemical theory and computation Vol. 21; no. 12; p. 5905 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
24.06.2025
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| ISSN: | 1549-9626, 1549-9626 |
| Online Access: | Get more information |
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| Summary: | Classical density functional theory (cDFT) provides a systematic framework to predict the structure and thermodynamic properties of chemical systems through molecular density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established a convolutional operator learning method that effectively separates the high-dimensional molecular density profile into lower-dimensional components, thereby exponentially reducing the vast input space. The operator learning network demonstrates exceptional learning capabilities, accurately mapping the relationship between the molecular density profile and its one-body direct correlation function for an atomistic polarizable model of carbon dioxide. The machine-learning procedure can be generalized to more complex molecular systems, offering high-precision operator-cDFT calculations at a low computational cost. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1549-9626 1549-9626 |
| DOI: | 10.1021/acs.jctc.5c00484 |