Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.14665 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918459787444224 |
|---|---|
| author | Luise, Giulia Huang, Chin-Wei Vogels, Thijs Kooi, Derk P. Ehlert, Sebastian Lanius, Stephanie Giesbertz, Klaas J. H. Karton, Amir Gunceler, Deniz Battaglia, Stefano Simm, Gregor N. C. Szabó, P. Bernát Stanley, Megan Bruinsma, Wessel P. Huang, Lin Wei, Xinran Torres, José Garrido Katbashev, Abylay Zavaleta, Rodrigo Chavez Máté, Bálint Kaba, Sékou-Oumar Sordillo, Roberto Chen, Yingrong Williams-Young, David B. Bishop, Christopher M. Hermann, Jan Berg, Rianne van den Gori-Giorgi, Paola |
| author_facet | Luise, Giulia Huang, Chin-Wei Vogels, Thijs Kooi, Derk P. Ehlert, Sebastian Lanius, Stephanie Giesbertz, Klaas J. H. Karton, Amir Gunceler, Deniz Battaglia, Stefano Simm, Gregor N. C. Szabó, P. Bernát Stanley, Megan Bruinsma, Wessel P. Huang, Lin Wei, Xinran Torres, José Garrido Katbashev, Abylay Zavaleta, Rodrigo Chavez Máté, Bálint Kaba, Sékou-Oumar Sordillo, Roberto Chen, Yingrong Williams-Young, David B. Bishop, Christopher M. Hermann, Jan Berg, Rianne van den Gori-Giorgi, Paola |
| contents | Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accurate and scalable exchange-correlation with deep learning Luise, Giulia Huang, Chin-Wei Vogels, Thijs Kooi, Derk P. Ehlert, Sebastian Lanius, Stephanie Giesbertz, Klaas J. H. Karton, Amir Gunceler, Deniz Battaglia, Stefano Simm, Gregor N. C. Szabó, P. Bernát Stanley, Megan Bruinsma, Wessel P. Huang, Lin Wei, Xinran Torres, José Garrido Katbashev, Abylay Zavaleta, Rodrigo Chavez Máté, Bálint Kaba, Sékou-Oumar Sordillo, Roberto Chen, Yingrong Williams-Young, David B. Bishop, Christopher M. Hermann, Jan Berg, Rianne van den Gori-Giorgi, Paola Chemical Physics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Computational Physics Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive. |
| title | Accurate and scalable exchange-correlation with deep learning |
| topic | Chemical Physics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2506.14665 |