ArticleProceedings of the National Academy of Sciences of the United States of America2022
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- Internal Benchmarking of Semi-Empirical Methods: Bromine-Containing Crystals as a Sensitive Test Case.Molecules (Basel, Switzerland) · 2026Article
- Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction.Digital discovery · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation.ACS medicinal chemistry letters · 2024Article
- Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Quantum chemical "Aufbau" principles: how to estimate the shape of highly flexible (bio-)polymers? A recursively extendable "chemion picture" of Euler-Hückel-type.Journal of molecular modeling · 2024Article
- OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.The journal of physical chemistry. B · 2024Article
- Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results.Journal of chemical theory and computation · 2023Article
- Efficient Modeling of Quantum Dynamics of Charge Carriers in Materials Using Short Nonequilibrium Molecular Dynamics.The journal of physical chemistry letters · 2023Article
- Extending machine learning beyond interatomic potentials for predicting molecular properties.Nature reviews. Chemistry · 2022Review
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5 authors.
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Abstract
Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.
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