Hierarchical quantum embedding by machine learning for large molecular assemblies
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arXiv
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| Format: | Preprint |
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2025
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| author | Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus |
| author_facet | Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus |
| contents | We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and non-equilibrium switching simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical quantum embedding by machine learning for large molecular assemblies Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus Chemical Physics Disordered Systems and Neural Networks Biological Physics Computational Physics We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and non-equilibrium switching simulations. |
| title | Hierarchical quantum embedding by machine learning for large molecular assemblies |
| topic | Chemical Physics Disordered Systems and Neural Networks Biological Physics Computational Physics |
| url | https://arxiv.org/abs/2503.03928 |