Interpolating numerically exact many-body wave functions for accelerated molecular dynamics
Abstract While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which a...
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57134-9 |
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| author | Yannic Rath George H. Booth |
| author_facet | Yannic Rath George H. Booth |
| author_sort | Yannic Rath |
| collection | DOAJ |
| description | Abstract While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces. |
| format | Article |
| id | doaj-art-40a50f03308d4bba8d3182a9151b2144 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-40a50f03308d4bba8d3182a9151b21442025-08-20T03:04:20ZengNature PortfolioNature Communications2041-17232025-02-0116111310.1038/s41467-025-57134-9Interpolating numerically exact many-body wave functions for accelerated molecular dynamicsYannic Rath0George H. Booth1National Physical LaboratoryDepartment of Physics and Thomas Young Centre, King’s College LondonAbstract While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces.https://doi.org/10.1038/s41467-025-57134-9 |
| spellingShingle | Yannic Rath George H. Booth Interpolating numerically exact many-body wave functions for accelerated molecular dynamics Nature Communications |
| title | Interpolating numerically exact many-body wave functions for accelerated molecular dynamics |
| title_full | Interpolating numerically exact many-body wave functions for accelerated molecular dynamics |
| title_fullStr | Interpolating numerically exact many-body wave functions for accelerated molecular dynamics |
| title_full_unstemmed | Interpolating numerically exact many-body wave functions for accelerated molecular dynamics |
| title_short | Interpolating numerically exact many-body wave functions for accelerated molecular dynamics |
| title_sort | interpolating numerically exact many body wave functions for accelerated molecular dynamics |
| url | https://doi.org/10.1038/s41467-025-57134-9 |
| work_keys_str_mv | AT yannicrath interpolatingnumericallyexactmanybodywavefunctionsforacceleratedmoleculardynamics AT georgehbooth interpolatingnumericallyexactmanybodywavefunctionsforacceleratedmoleculardynamics |