Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress

Carolin Müller; Štěpán Sršeň; Brigitta Bachmair; Rachel Crespo‐Otero; Jingbai Li; Sascha Mausenberger; Max Pinheiro; Graham A. Worth; Steven A. Lopez; Julia Westermayr

doi:10.1039/d5sc05579b

January 1, 2025Chemical ScienceOpen Access

Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress

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Key Points

Understanding molecular excited states revolutionizes design frameworks in functional molecules.
Critical findings highlight the role of machine learning in enhancing predictions of photophysical properties.
Innovative practices for nonadiabatic molecular dynamics showcase the growing intersection of AI with chemistry.
Application of machine learning in organic chemistry and chemical biology opens new pathways for research.

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Implication

Best practices in utilizing machine learning for molecular excited states, indicating substantial progress.

Abstract

Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with...

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Müller et al. (2025) studied this question.

synapsesocial.com/papers/68c189e09b7b07f3a0613a2d https://doi.org/10.1039/d5sc05579b

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Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress

Key Points

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Abstract

Discussion

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Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress

Key Points

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Implication

Abstract

Discussion

Cite This Study

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