DeePaTB: A Deep Learning-Powered Semi-Empirical Quantum Mechanical Method
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2026
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| _version_ | 1866901892555079680 |
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| author | Xiao, Jin |
| author_facet | Xiao, Jin |
| contents | <p>Data-driven methods can reproduce the accuracy of high-level quantum methods with reduced computational costs by learning features from low-level electronic structure calculations. We introduce the Deep Atomic Density-Based Tight-Binding (DeePaTB) framework, a novel machine-learning semiempirical quantum mechanical (MLSQM) framework built on our atomic density-based tight-binding (aTB) method. DeePaTB generates the electronic structure feature “eigenvalue of the local density matrix” computed using Amesp software as inputs to a deep model that predicts energies and related properties. Across diverse chemical systems, DeePaTB attains density functional theory (DFT)-level accuracy while retaining SQM computational effi ciency. This is applicable to closed-shell and open-shell species and systems under external electric fi elds, demonstrating the strong transferability of DeePaTB. Together, these results establish DeePaTB as a general, scalable approach that eff ectively balances accuracy and efficiency for large and complex quantum chemical computations.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19155250 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DeePaTB: A Deep Learning-Powered Semi-Empirical Quantum Mechanical Method Xiao, Jin Machine learning <p>Data-driven methods can reproduce the accuracy of high-level quantum methods with reduced computational costs by learning features from low-level electronic structure calculations. We introduce the Deep Atomic Density-Based Tight-Binding (DeePaTB) framework, a novel machine-learning semiempirical quantum mechanical (MLSQM) framework built on our atomic density-based tight-binding (aTB) method. DeePaTB generates the electronic structure feature “eigenvalue of the local density matrix” computed using Amesp software as inputs to a deep model that predicts energies and related properties. Across diverse chemical systems, DeePaTB attains density functional theory (DFT)-level accuracy while retaining SQM computational effi ciency. This is applicable to closed-shell and open-shell species and systems under external electric fi elds, demonstrating the strong transferability of DeePaTB. Together, these results establish DeePaTB as a general, scalable approach that eff ectively balances accuracy and efficiency for large and complex quantum chemical computations.</p> |
| title | DeePaTB: A Deep Learning-Powered Semi-Empirical Quantum Mechanical Method |
| topic | Machine learning |
| url | https://doi.org/10.5281/zenodo.19155250 |