Articlenpj computational materials2025
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The Newton-X platform for mixed quantum-classical dynamics.Physical chemistry chemical physics : PCCP · 2026Article
- Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Investigating the Effects of Electron Correlation and Methylation on theJournal of chemical theory and computation · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin-Orbit Couplings and Dynamics.The journal of physical chemistry. A · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in
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Registered trials
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