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

Chem. Sci., 2025, 16, 17542
DOI: 10.1039/D5SC05579B

Dynamic vertical triplet energies: Understanding and predicting triplet energy transfer

Mihai V. Popescu and Robert S. Paton

Chem, 2024, 10, 3428
DOI: 10.1016/j.chempr.2024.07.001

Machine learning the Hohenberg-Kohn map for molecular excited states

Yuanming Bai, Leslie Vogt-Maranto, Mark E. Tuckerman and William J. Glover

Nat Commun, 2022, 13
DOI: 10.1038/s41467-022-34436-w

Ten Problems in Polymer Reactivity Prediction

Nicholas E. Jackson and Brett M. Savoie

Macromolecules, 2025, 58, 1737
DOI: 10.1021/acs.macromol.4c02582

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation

Yifei Zhu, Jiawei Peng, Chao Xu and Zhenggang Lan

J. Phys. Chem. Lett., 2024, 15, 9601
DOI: 10.1021/acs.jpclett.4c01751

Increasing Efficiency of Nonadiabatic Molecular Dynamics by Hamiltonian Interpolation with Kernel Ridge Regression

Yifan Wu, Natalie Prezhdo and Weibin Chu

J. Phys. Chem. A, 2021, 125, 9191
DOI: 10.1021/acs.jpca.1c05105

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning

Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti and Pavlo O. Dral

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01636-z

Analogies between photochemical reactions and ground-state post-transition-state bifurcations shed light on dynamical origins of selectivity

Zhitao Feng, Wentao Guo, Wang-Yeuk Kong, Dongjie Chen, Shunyang Wang and Dean J. Tantillo

Nat. Chem., 2024, 16, 615
DOI: 10.1038/s41557-023-01410-y

Balancing Wigner sampling and geometry interpolation for deep neural networks learning photochemical reactions

Li Wang, Zhendong Li and Jingbai Li

Artificial Intelligence Chemistry, 2023, 1, 100018
DOI: 10.1016/j.aichem.2023.100018

Application of machine learning interatomic potentials in heterogeneous catalysis

Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, Ademola Soyemi and Tibor Szilvási

Journal of Catalysis, 2025, 448, 116202
DOI: 10.1016/j.jcat.2025.116202

Surface Hopping Nested Instances Training Set for Excited-state Learning

Robin Curth, Theodor E. Röhrkasten, Carolin Müller and Julia Westermayr

Sci Data, 2025, 12
DOI: 10.1038/s41597-025-05443-5

The radiative surface hopping (RSH) algorithm: Capturing fluorescence events in molecular systems within a semi-classical non-adiabatic molecular dynamics framework

Manuel Pérez-Escribano, Joanna Jankowska, Giovanni Granucci and Daniel Escudero

The Journal of Chemical Physics, 2023, 158
DOI: 10.1063/5.0139516

Trendbericht Theoretische Chemie 2025 1/2: Maschinelles Lernen für angeregte Zustände

Johannes Dietschreit and Julia Westermayr

Nachr Chem, 2025, 73, 54
DOI: 10.1002/nadc.20254152404

COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics

Felix Plasser, Hans Lischka, Ron Shepard, Péter G. Szalay, Russell M. Pitzer, Rodolpho L. R. Alves, Adelia J. A. Aquino, Jochen Autschbach, Mario Barbatti, Jhonatas R. Carvalho, Julio C. V. Chagas, Leticia González, Andreas Hansen, Bhumika Jayee, Miklos Kertesz, Francisco B. C. Machado, Spiridoula Matsika, Silmar A. do Monte, Saikat Mukherjee, Dana Nachtigallová, Reed Nieman, Vytor P. Oliveira, Markus Oppel, Carol A. Parish, Jiri Pittner, Luan G. F. dos Santos, Armin Scrinzi, Mahesh K. Sit, Rene F. K. Spada, Mushir Thodika, Daniel C. A. Valente, Álvaro Vázquez-Mayagoitia, Elizete Ventura, Julia Westermayr, Aleksandr Zaichenko and Zhiyong Zhang

J. Phys. Chem. A, 2025, 129, 6482
DOI: 10.1021/acs.jpca.5c02047

Excited‐State Distortions Promote the Photochemical 4π‐Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations

Jingbai Li and Steven A. Lopez

Chemistry A European J, 2022, 28
DOI: 10.1002/chem.202200651

A digital twin to overcome long-time challenges in photovoltaics

Larry Lüer, Ian Marius Peters, Ana Sunčana Smith, Eva Dorschky, Bjoern M. Eskofier, Frauke Liers, Jörg Franke, Martin Sjarov, Matthias Brossog, Dirk M. Guldi, Andreas Maier and Christoph J. Brabec

Joule, 2024, 8, 295
DOI: 10.1016/j.joule.2023.12.010

Excited-state methods for molecular systems: Performance, pitfalls, and practical guidance

Zachary J. Knepp, Gil M. Repa and Lisa A. Fredin

Chemical Physics Reviews, 2025, 6
DOI: 10.1063/5.0232302

Assessing Nonadiabatic Dynamics Methods in Long Timescales

Saikat Mukherjee, Yorick Lassmann, Rafael S. Mattos, Baptiste Demoulin, Basile F. E. Curchod and Mario Barbatti

J. Chem. Theory Comput., 2025, 21, 29
DOI: 10.1021/acs.jctc.4c01349

MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics

Matheus de Oliveira Bispo, Rafael Souza Mattos, Max Pinheiro, Bidhan Chandra Garain, Pavlo O. Dral and Mario Barbatti

J. Chem. Theory Comput., 2025, 21, 11390
DOI: 10.1021/acs.jctc.5c01454

Machine-Learning Photodynamics Simulations Uncover the Role of Substituent Effects on the Photochemical Formation of Cubanes

Jingbai Li, Rachel Stein, Daniel M. Adrion and Steven A. Lopez

J. Am. Chem. Soc., 2021, 143, 20166
DOI: 10.1021/jacs.1c07725

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Abdulrahman Aldossary, Jorge Arturo Campos‐Gonzalez‐Angulo, Sergio Pablo‐García, Shi Xuan Leong, Ella Miray Rajaonson, Luca Thiede, Gary Tom, Andrew Wang, Davide Avagliano and Alán Aspuru‐Guzik

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202402369

Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn)

Patrick Reiser, André Eberhard and Pascal Friederich

Software Impacts, 2021, 9, 100095
DOI: 10.1016/j.simpa.2021.100095

Evaluating MS-ANI vs SS-ANI for Surface Hopping Simulations: A Cyclohexadiene Photochemical Ring-Opening Case Study

Biman Medhi, Madhab Morang and Manabendra Sarma

J. Phys. Chem. Lett., 2026, 17, 4133
DOI: 10.1021/acs.jpclett.6c00353

Toward Community-Driven Benchmarking and Ranking of Nonadiabatic Dynamics Methodologies

Alexey V. Akimov

J. Chem. Theory Comput., 2025, 21, 11821
DOI: 10.1021/acs.jctc.5c01635

OpenQP: A Quantum Chemical Platform Featuring MRSF-TDDFT with an Emphasis on Open-Source Ecosystem

Vladimir Mironov, Konstantin Komarov, Jingbai Li, Igor Gerasimov, Hiroya Nakata, Mohsen Mazaherifar, Kazuya Ishimura, Woojin Park, Alireza Lashkaripour, Minseok Oh, Miquel Huix-Rotllant, Seunghoon Lee and Cheol Ho Choi

J. Chem. Theory Comput., 2024, 20, 9464
DOI: 10.1021/acs.jctc.4c01117

Simulations of molecular photodynamics in long timescales

Saikat Mukherjee, Max Pinheiro, Baptiste Demoulin and Mario Barbatti

Phil. Trans. R. Soc. A., 2022, 380
DOI: 10.1098/rsta.2020.0382

Nonadiabatic Simulation of Exciton Dynamics in Organic Semiconductors Using Neural Network-Based Frenkel Hamiltonian and Gradients

Farhad Ghalami, Philipp M. Dohmen, Mila Krämer, Marcus Elstner and Weiwei Xie

J. Chem. Theory Comput., 2024, 20, 6160
DOI: 10.1021/acs.jctc.4c00220

Machine learning accelerated photodynamics simulations

Jingbai Li and Steven A. Lopez

Chemical Physics Reviews, 2023, 4
DOI: 10.1063/5.0159247

Machine learning photodynamics uncover blocked non-radiative mechanisms in aggregation-induced emission

Li Wang, Christian Salguero, Steven A. Lopez and Jingbai Li

Chem, 2024, 10, 2295
DOI: 10.1016/j.chempr.2024.04.017

Machine Learning of Reactive Potentials

Yinuo Yang, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev and Adrian E. Roitberg

Annual Review of Physical Chemistry, 2024, 75, 371
DOI: 10.1146/annurev-physchem-062123-024417

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

Lucien Dupuy and Neepa T. Maitra

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0227523

Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface

Shichen Lin, Daoling Peng, Weitao Yang, Feng Long Gu and Zhenggang Lan

The Journal of Chemical Physics, 2021, 155
DOI: 10.1063/5.0067176

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

Max Pinheiro Jr, Shuang Zhang, Pavlo O. Dral and Mario Barbatti

Sci Data, 2023, 10
DOI: 10.1038/s41597-023-01998-3

Trendbericht Photochemie 2025

Thorsten Bach, Alexander Breder, Tanja Gulder, Jürgen Hauer, Corinna Hess, Burkhard König, Patrick Nuernberger, Christian Ochsenfeld, Julia Rehbein, Oliver Reiser, Christopher J. Stein, Golo Storch, Enrico Tapavicza, Julia Westermayr, Robert Wolf, Kirsten Zeitler and Cathleen Zeymer

Nachr Chem, 2025, 73, 56
DOI: 10.1002/nadc.20254148488

OMNI-P2x universal neural network potential for excited-state simulations

Mikołaj Martyka, Xin-Yu Tong, Joanna Jankowska and Pavlo O. Dral

Nat Commun, 2026
DOI: 10.1038/s41467-026-71380-5

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

Xiaoliang Pan, Ryan Snyder, Jia‐Ning Wang, Chance Lander, Carly Wickizer, Richard Van, Andrew Chesney, Yuanfei Xue, Yuezhi Mao, Ye Mei, Jingzhi Pu and Yihan Shao

J Comput Chem, 2024, 45, 638
DOI: 10.1002/jcc.27269

Machine Learning Approach to Vertical Energy Gap in Redox Processes

Ronit Sarangi, Suman Maity and Atanu Acharya

J. Chem. Theory Comput., 2024, 20, 6747
DOI: 10.1021/acs.jctc.4c00715

NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase

James Gardner, Oscar A. Douglas-Gallardo, Wojciech G. Stark, Julia Westermayr, Svenja M. Janke, Scott Habershon and Reinhard J. Maurer

The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0089436

Multiconfigurational Calculations and Photodynamics Describe Norbornadiene Photochemistry

Federico J. Hernández, Jordan M. Cox, Jingbai Li, Rachel Crespo-Otero and Steven A. Lopez

J. Org. Chem., 2023, 88, 5311
DOI: 10.1021/acs.joc.2c02758

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics

Jakub Martinka, Mikołaj Martyka, Biman Medhi, Jiří Pittner and Pavlo O. Dral

J. Chem. Theory Comput., 2026, 22, 2467
DOI: 10.1021/acs.jctc.5c02137

Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians

Changwei Zhang, Yang Zhong, Zhi-Guo Tao, Xinming Qin, Honghui Shang, Zhenggang Lan, Oleg V. Prezhdo, Xin-Gao Gong, Weibin Chu and Hongjun Xiang

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-57328-1

Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal

Zhendong Li, Federico J. Hernández, Christian Salguero, Steven A. Lopez, Rachel Crespo-Otero and Jingbai Li

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-56480-y

Conical Intersection in Chemiluminescence of Cyclic Peroxides

Ling Yue and Ya-Jun Liu

J. Phys. Chem. Lett., 2022, 13, 10671
DOI: 10.1021/acs.jpclett.2c02783

Modeling Charge Transport in Organic Semiconductors Using Neural Network Based Hamiltonians and Forces

Philipp M. Dohmen, Mila Krämer, Patrick Reiser, Pascal Friederich, Marcus Elstner and Weiwei Xie

J. Chem. Theory Comput., 2023, 19, 3825
DOI: 10.1021/acs.jctc.3c00264

Understanding the origin of stereoselectivity in the photochemical denitrogenation of 2,3-diazabicyclo[2.2.1]heptene and its derivatives with non-adiabatic molecular dynamics

Leticia A Gomes and Steven A Lopez

Beilstein J. Org. Chem., 2025, 21, 2007
DOI: 10.3762/bjoc.21.156

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods

Lina Zhang, Sebastian V. Pios, Mikołaj Martyka, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen, Lipeng Chen, Joanna Jankowska, Mario Barbatti and Pavlo O. Dral

J. Chem. Theory Comput., 2024, 20, 5043
DOI: 10.1021/acs.jctc.4c00468

Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry

Deming Xia, Jingwen Chen, Zhiqiang Fu, Tong Xu, Zhongyu Wang, Wenjia Liu, Hong-bin Xie and Willie J. G. M. Peijnenburg

Environ. Sci. Technol., 2022, 56, 2115
DOI: 10.1021/acs.est.1c05970

Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations

Sascha Mausenberger, Severin Polonius, Sebastian Mai and Leticia González

J. Chem. Theory Comput., 2025, 21, 8994
DOI: 10.1021/acs.jctc.5c00878

Ultrafast Photocontrolled Rotation in a Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations

Haoyang Xu, Boyuan Zhang, Yuanda Tao, Weijia Xu, Bo Hu, Feng Yan and Jin Wen

J. Phys. Chem. A, 2023, 127, 7682
DOI: 10.1021/acs.jpca.3c01036

Photoisomerization of (Cyanomethylene)cyclopropane (C5H5N) to 1-Cyano-2-methylenecyclopropane in an Argon Matrix

Samuel A. Wood, Brian J. Esselman, Samuel M. Kougias, R. Claude Woods and Robert J. McMahon

J. Phys. Chem. A, 2024, 128, 1417
DOI: 10.1021/acs.jpca.3c08001

Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces

Tomislav Piskor, Peter Pinski, Thilo Mast and Vladimir Rybkin

IJMS, 2024, 25, 8530
DOI: 10.3390/ijms25158530

Machine learning accelerated nonadiabatic dynamics simulations of materials with excitonic effects

Sheng-Rui Wang, Qiu Fang, Xiang-Yang Liu, Wei-Hai Fang and Ganglong Cui

The Journal of Chemical Physics, 2025, 162
DOI: 10.1063/5.0248228

Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach

Tzu Yu Wang, Simon P. Neville and Michael S. Schuurman

J. Phys. Chem. Lett., 2023, 14, 7780
DOI: 10.1021/acs.jpclett.3c01649

Machine Learning Potentials for Heterogeneous Catalysis

Amir Omranpour, Jan Elsner, K. Nikolas Lausch and Jörg Behler

ACS Catal., 2025, 15, 1616
DOI: 10.1021/acscatal.4c06717

The Quest to Simulate Excited-State Dynamics of Transition Metal Complexes

J. Patrick Zobel and Leticia González

JACS Au, 2021, 1, 1116
DOI: 10.1021/jacsau.1c00252

A Look Inside the Black Box of Machine Learning Photodynamics Simulations

Jingbai Li and Steven A. Lopez

Acc. Chem. Res., 2022, 55, 1972
DOI: 10.1021/acs.accounts.2c00288

Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

Simon Axelrod, Eugene Shakhnovich and Rafael Gómez-Bombarelli

Nat Commun, 2022, 13
DOI: 10.1038/s41467-022-30999-w

A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors

Jakub Martinka, Lina Zhang, Yi-Fan Hou, Mikołaj Martyka, Jiří Pittner, Mario Barbatti and Pavlo O. Dral

J. Phys. Chem. Lett., 2025, 16, 11732
DOI: 10.1021/acs.jpclett.5c02810

The photophysics of $$^{\textrm{tzSe}}$$A: a sulfur-to-selenium isothiazolo-adenosine ($$^{\textrm{tz}}$$A) nucleobase analogue

Yeny Yaneth Pillco-Valencia, Danillo Valverde and Antonio Carlos Borin

Theor Chem Acc, 2026, 145
DOI: 10.1007/s00214-025-03258-6

Molecular excited states through a machine learning lens

Pavlo O. Dral and Mario Barbatti

Nat Rev Chem, 2021, 5, 388
DOI: 10.1038/s41570-021-00278-1

High-Throughput Screening for Ultrafast Photochemical Reaction Discovery

Matthew Bain, José L. Godínez Castellanos and Stephen E. Bradforth

J. Phys. Chem. Lett., 2023, 14, 9864
DOI: 10.1021/acs.jpclett.3c02389

Nonadiabatic Forward Flux Sampling for Excited-State Rare Events

Madlen Maria Reiner, Brigitta Bachmair, Maximilian Xaver Tiefenbacher, Sebastian Mai, Leticia González, Philipp Marquetand and Christoph Dellago

J. Chem. Theory Comput., 2023, 19, 1657
DOI: 10.1021/acs.jctc.2c01088