Surface Hopping Nested Instances Training Set for Excited-state Learning
Abstract Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) mod...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05443-5 |
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| _version_ | 1849767352969723904 |
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| author | Robin Curth Theodor E. Röhrkasten Carolin Müller Julia Westermayr |
| author_facet | Robin Curth Theodor E. Röhrkasten Carolin Müller Julia Westermayr |
| author_sort | Robin Curth |
| collection | DOAJ |
| description | Abstract Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) models trained on this data. To address this, we introduce SHNITSEL, a data repository containing 418,870 ab-initio data points of nine organic molecules in their ground and electronically excited states. Each data point includes high-accuracy quantum chemical properties such as energies, forces, and dipole moments in the ground state and electronically excited singlet or triplet states as well as properties that arise from the coupling of electronic states, namely nonadiabatic couplings, transition dipoles, or spin-orbit couplings. Generated with state-of-the-art methods, SHNITSEL provides a robust benchmark for ML models and facilitates the development of ML-based approaches for excited state properties. |
| format | Article |
| id | doaj-art-252c6b8469cf4aea8a70f3bfc5aba116 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-252c6b8469cf4aea8a70f3bfc5aba1162025-08-20T03:04:14ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05443-5Surface Hopping Nested Instances Training Set for Excited-state LearningRobin Curth0Theodor E. Röhrkasten1Carolin Müller2Julia Westermayr3Leipzig University, Wilhelm Ostwald Institute for Physical and Theoretical ChemistryFriedrich-Alexander-Universität Erlangen-Nürnberg, Computer-Chemistry-CenterFriedrich-Alexander-Universität Erlangen-Nürnberg, Computer-Chemistry-CenterLeipzig University, Wilhelm Ostwald Institute for Physical and Theoretical ChemistryAbstract Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) models trained on this data. To address this, we introduce SHNITSEL, a data repository containing 418,870 ab-initio data points of nine organic molecules in their ground and electronically excited states. Each data point includes high-accuracy quantum chemical properties such as energies, forces, and dipole moments in the ground state and electronically excited singlet or triplet states as well as properties that arise from the coupling of electronic states, namely nonadiabatic couplings, transition dipoles, or spin-orbit couplings. Generated with state-of-the-art methods, SHNITSEL provides a robust benchmark for ML models and facilitates the development of ML-based approaches for excited state properties.https://doi.org/10.1038/s41597-025-05443-5 |
| spellingShingle | Robin Curth Theodor E. Röhrkasten Carolin Müller Julia Westermayr Surface Hopping Nested Instances Training Set for Excited-state Learning Scientific Data |
| title | Surface Hopping Nested Instances Training Set for Excited-state Learning |
| title_full | Surface Hopping Nested Instances Training Set for Excited-state Learning |
| title_fullStr | Surface Hopping Nested Instances Training Set for Excited-state Learning |
| title_full_unstemmed | Surface Hopping Nested Instances Training Set for Excited-state Learning |
| title_short | Surface Hopping Nested Instances Training Set for Excited-state Learning |
| title_sort | surface hopping nested instances training set for excited state learning |
| url | https://doi.org/10.1038/s41597-025-05443-5 |
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