Orbital-interaction-aware deep learning model for efficient surface chemistry simulations
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915852773752832 |
|---|---|
| author | Zhang, Zhihao Cao, Xiao-Ming |
| author_facet | Zhang, Zhihao Cao, Xiao-Ming |
| contents | Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Orbital-interaction-aware deep learning model for efficient surface chemistry simulations Zhang, Zhihao Cao, Xiao-Ming Disordered Systems and Neural Networks Materials Science Chemical Physics Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data. |
| title | Orbital-interaction-aware deep learning model for efficient surface chemistry simulations |
| topic | Disordered Systems and Neural Networks Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2507.20496 |