Machine learning and excited-state molecular dynamics

Abstract

Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.

References in corpus (66)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Machine learning and the physical sciences
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Training deep neural networks for the inverse design of nanophotonic structures
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Machine learning for molecular simulation
• Less is more: sampling chemical space with active learning
• Deep learning for molecular design - a review of the state of the art
• By-passing the Kohn-Sham equations with machine learning
• Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
• Machine-learning based interatomic potential for amorphous carbon
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• Machine Learning of Molecular Electronic Properties in Chemical Compound Space
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Machine learning a general purpose interatomic potential for silicon
• SchNetPack: A Deep Learning Toolbox For Atomistic Systems
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• FCHL revisited: faster and more accurate quantum machine learning
• Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
• Alchemical and structural distribution based representation for improved QML
• Hierarchical modeling of molecular energies using a deep neural network
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Dissociation of O2 at Al(111): The Role of Spin Selection Rules
• A general method to describe intersystem crossing dynamics in trajectory surface hopping
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Symmetries and many-body excited states with neural-network quantum states
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
• Extending the Accuracy of the SNAP Interatomic Potential Form
• Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
• Machine learning enables long time scale molecular photodynamics simulations
• Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation
• Atom-Density Representations for Machine Learning
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Representing molecule-surface interactions with symmetry-adapted neural networks
• Non-adiabatic Effects in the Dissociation of Oxygen Molecules at the Al(111) Surface
• Solving the Bose-Hubbard model with machine learning
• Machine Learning Exciton Dynamics
• Operators in Machine Learning: Response Properties in Chemical Space
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Bayesian machine learning for quantum molecular dynamics
• Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
• Fingerprints for spin-selection rules in the interaction dynamics of O2 at Al(111)
• Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors
• Signatures of nonadiabatic O2 dissociation at Al(111): First-principles fewest-switches study
• Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
• Chemical diversity in molecular orbital energy predictions with kernel ridge regression
• Neural networks and kernel ridge regression for excited states dynamics of CH${}_2$NH${}_2^{+}$: From single-state to multi-state representations and multi-property machine learning models
• Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes
• Symmetry-Adapted High Dimensional Neural Network Representation of Electronic Friction Tensor of Adsorbates on Metals
• Automatically growing global reactive neural network potential energy surfaces: a trajectory free active learning strategy
• Photoelectron Spectra of 2-Thiouracil, 4-Thiouracil and 2,4-Dithiouracil
• A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
• Machine learning density functional theory for the Hubbard model
• The DNA Nucleobase Thymine in Motion - Intersystem Crossing Simulated with Surface Hopping
• Rapid and accurate molecular deprotonation energies from quantum alchemy
• Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
• Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques

Cited by in corpus (12)

• Machine learning for electronically excited states of molecules
• Perspective on integrating machine learning into computational chemistry and materials science
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• SchNetPack 2.0: A neural network toolbox for atomistic machine learning
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Roaming leads to amino acid photodamage: A deep learning study of tyrosine
• The quest to simulate excited-state dynamics of transition metal complexes
• A comparative study of different machine learning methods for dissipative quantum dynamics
• MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
• Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor
• Exciting DeePMD: Learning excited state energies, forces, and non-adiabatic couplings
• Theory of Moment Propagation for Quantum Dynamics in Single-Particle Description