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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2018

Deep Learning for Nonadiabatic Excited-State Dynamics

Wenkai Chen, Xiangyang Liu, Wei‐Hai Fang, Pavlo O. Dral, Ganglong Cui

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Deep Learning for Nonadiabatic Excited-State DynamicsOriginalSimplified Calibration System for Stereoradiography in Scoliosis6How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry110A multi-layer energy-based fragment method for excited states and nonadiabatic dynamics34Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Machine learning enables long time scale molecular photodynamics simulations214Different flavors of nonadiabatic molecular dynamics117Reproducing global potential energy surfaces with continuous-filter convolutional neural networks16Numerical convergence of the Sinc discrete variable representation for solving molecular vibrational states with a conical intersection in adiabatic representation5Quantum Trajectory Mean-Field Method for Nonadiabatic Dynamics in Photochemistry15UV Photoinduced Dynamics of Conformer-Resolved Aromatic Peptides64Machine Learning for Predicting Electron Transfer Coupling65Nonadiabatic Photochemistry Induced by Inaccessible Conical Intersections9Excited-state dynamics of molecules with classically driven trajectories and Gaussians17Interactions Between Two Many‐Atom Systems0Neural Network Based in Silico Simulation of Combustion Reactions18Integrating Machine Learning with the Multilayer Energy-Based Fragment Method for Excited States of Large Systems46A systematic model study quantifying how conical intersection topography modulates photochemical reactions40Quantum chemistry assisted by machine learning33Molecular Photochemistry: Recent Developments in Theory204Molekulare Photochemie: Moderne Entwicklungen in der theoretischen Chemie5Accurate Neural Network Representation of the Ab Initio Determined Spin–Orbit Interaction in the Diabatic Representation Including the Effects of Conical Intersections39Quantum Chemistry in the Age of Machine Learning417Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach1Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression2Neural networks and kernel ridge regression for excited states dynamics of CH2NH 2+ : From single-state to multi-state representations and multi-property machine learning models62A new diabatization scheme for direct quantum dynamics: Procrustes diabatization23Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics194Hierarchical machine learning of potential energy surfaces110First-principles description of intra-chain exciton migration in an oligo(para-phenylene vinylene) chain. I. Generalized Frenkel–Holstein Hamiltonian13High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning202Efficient Construction of Excited-State Hessian Matrices with Machine Learning Accelerated Multilayer Energy-Based Fragment Method35Machine learning and excited-state molecular dynamics62Modeling H2O/Rutile-TiO2(110) Potential Energy Surfaces with Deep Networks0Machine Learning for Nonadiabatic Molecular Dynamics4Photoinduced Dynamics of Charge Carriers in Metal Halide Perovskites from an Atomistic Perspective55Machine Learning for Absorption Cross Sections77Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments47Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics35Ultrafast Spectroscopy of Photoactive Molecular Systems from First Principles: Where We Stand Today and Where We Are Going7486 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy225Practicing deep learning in materials science: An evaluation for predicting the formation energies17Non-adiabatic molecular dynamics of PAH-related complexes2Machine learning a molecular Hamiltonian for predicting electron dynamics17Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space72Mixed Quantum–Classical Dynamics with Machine Learning-Based Potentials via Wigner Sampling8Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning247Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies33Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation288Machine Learning for Electronically Excited States of Molecules398PySurf: A Framework for Database Accelerated Direct Dynamics18Artificial neural networks for predicting charge transfer coupling50Exploring 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for materials science214Modelling ultrafast dynamics at a conical intersection with regularized diabatic states: An approach based on multiplicative neural networks8New insights into the excited state intramolecular proton transfer (ESIPT) competition mechanism for different intramolecular hydrogen bonds of Kaempferol and Quercetin in solution29Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality145Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential88Exploring Accurate Potential Energy Surfaces via Integrating Variational Quantum Eigensolver with Machine Learning17All-Atom Nonadiabatic Semiclassical Mapping Dynamics for Photoinduced Charge Transfer of Organic Photovoltaic Molecules in Explicit Solvents28Excited-state dynamics with machine learning15How Good Is the Vibronic Hamiltonian Repetition Approach for Long-Time Nonadiabatic Molecular Dynamics?16Deep learning interatomic potential for Ca-O system at high pressure6Fewest-Switches Surface Hopping with Long Short-Term Memory Networks23Conical Intersection in Chemiluminescence of Cyclic Peroxides17Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets1Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization40Photochemistry of Biological Systems: Excited-State Electronic Structure Calculations and Nonadiabatic Dynamics Simulations with QM/MM Methods1Recent advances in machine learning for electronic excited state molecular dynamics simulations2Machine learning methods in photochemistry and photophysics10Excited-state molecular dynamics simulation based on variational quantum algorithms12The radiative surface hopping (RSH) algorithm: Capturing fluorescence events in molecular systems within a semi-classical non-adiabatic molecular dynamics framework8Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces23Non-Adiabatic Dynamics in Condensed Matter and Nanoscale Systems0Large-scale surface hopping simulation of charge transport in hexagonal molecular crystals: role of electronic coupling signs11Nonadiabatic Derivative Couplings Calculated Using Information of Potential Energy Surfaces without Wavefunctions: Ab Initio and Machine Learning Implementations7Ultrafast Photo-Controlled Rotation in Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations0Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure22DeePMD-kit v2: A software package for deep potential 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virtual screening of organic second-order nonlinear optical chromophores within the donor–π-bridge–acceptor framework12AI in computational chemistry through the lens of a decade-long journey30Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules25Neural network potentials facilitating accurate complex scaling for molecular resonances: from a model to high dimensional realistic systems3Theoretical trends in the dynamics simulations of molecular machines across multiple scales5Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials9Estimating Nonradiative Excited-State Lifetimes in Photoactive Semiconducting Nanostructures5Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra25Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions30Exploring the Relationship between Composition and Li-Ion Conductivity in the Amorphous Li–La–Zr–O System15Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics29Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy12MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods32Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets16Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy15Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation15Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings9Machine Learning Mapping Approach for Computing Spin Relaxation Dynamics12Learning radical excited states from sparse data6Machine learning for nonadiabatic molecular dynamics: best practices and recent progress20Neuroevolution potential-driven accurate and efficient discovery of mechanical behavior and nanocluster dynamics in TiAl alloys0The evolution of machine learning potentials for molecules, reactions and materials71Machine learning accelerated nonadiabatic dynamics simulations of materials with excitonic effects12Machine Learning Potentials for Heterogeneous Catalysis109Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach9Diabatic States of Charge Transfer with Constrained Charge Equilibration2

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