Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space

Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials

(2020) Tammie R. Nelson et al.   CHEMICAL REVIEWS

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Accurate Neural Network Representation of the Ab Initio Determined Spin–Orbit Interaction in the Diabatic Representation Including the Effects of Conical Intersections

(2020) Yafu Guan et al.   Journal of Physical Chemistry Letters

A new diabatization scheme for direct quantum dynamics: Procrustes diabatization

(2020) Gareth W. Richings et al.   JOURNAL OF CHEMICAL PHYSICS

An On-the-fly Approach to Construct Generalized Energy‒Based Fragmentation Machine Learning Force Fields of Complex Systems

(2020) Zheng Cheng et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

(2020) Julia Westermayr et al.   Journal of Physical Chemistry Letters

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

(2020) Max Veit et al.   JOURNAL OF CHEMICAL PHYSICS

Non-conventional force fields for applications in spectroscopy and chemical reaction dynamics

(2020) Debasish Koner et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning for Absorption Cross Sections

(2020) Bao-Xin Xue et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties

(2020) Yaolong Zhang et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

(2019) Henry Chan et al.   Journal of Physical Chemistry C

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Accurate Probabilities for Highly Activated Reaction of Polyatomic Molecules on Surfaces Using a High-Dimensional Neural Network Potential: CHD3 + Cu(111)

(2019) Nick Gerrits et al.   Journal of Physical Chemistry Letters

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Atom-density representations for machine learning

(2019) Michael J. Willatt et al.   JOURNAL OF CHEMICAL PHYSICS

A Quasi-Diabatic Representation of the 1,21A States of Methylamine

(2019) Yuchen Wang et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections

(2019) Yafu Guan et al.   JOURNAL OF CHEMICAL PHYSICS

A neural network protocol for electronic excitations of N-methylacetamide

(2019) Sheng Ye et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

(2019) Roman Zubatyuk et al.   Science Advances

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

(2019) Anders S. Christensen et al.   CHIMIA

Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,21A States of Ammonia

(2019) Yafu Guan et al.   Journal of Chemical Theory and Computation

Spin-Vibronic Mechanism for Intersystem Crossing

(2018) Thomas J. Penfold et al.   CHEMICAL REVIEWS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

MCTDH on-the-fly: Efficient grid-based quantum dynamics without pre-computed potential energy surfaces

(2018) Gareth W. Richings et al.   JOURNAL OF CHEMICAL PHYSICS

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

(2018) Deping Hu et al.   Journal of Physical Chemistry Letters

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

Nonadiabatic dynamics: The SHARC approach

(2018) Sebastian Mai et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

(2018) Wiktor Pronobis et al.   EUROPEAN PHYSICAL JOURNAL B

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

(2018) Benjamin Nebgen et al.   Journal of Chemical Theory and Computation

Discovering a Transferable Charge Assignment Model Using Machine Learning

(2018) Andrew E. Sifain et al.   Journal of Physical Chemistry Letters

Nonadiabatic Excited-State Dynamics with Machine Learning

(2018) Pavlo O. Dral et al.   Journal of Physical Chemistry Letters

Finite-temperature Wigner phase-space sampling and temperature effects on the excited-state dynamics of 2-nitronaphthalene

(2018) J. Patrick Zobel et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

(2018) Hongxiang Zong et al.   npj Computational Materials

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices

(2018) Changjian Xie et al.   JOURNAL OF CHEMICAL PHYSICS

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Improved on-the-Fly MCTDH Simulations with Many-Body-Potential Tensor Decomposition and Projection Diabatization

(2018) Gareth W. Richings et al.   Journal of Chemical Theory and Computation

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network

(2018) Hao Wang et al.   Journal of Chemical Theory and Computation

A Simple Phase Correction Makes a Big Difference in Nonadiabatic Molecular Dynamics

(2018) Alexey V. Akimov   Journal of Physical Chemistry Letters

Deep Learning for Nonadiabatic Excited-State Dynamics

(2018) Wen-Kai Chen et al.   Journal of Physical Chemistry Letters

Excited-State Gradients in Polarizable QM/MM Models: An Induced Dipole Formulation

(2017) Maximilian F. S. J. Menger et al.   Journal of Chemical Theory and Computation

Nonadiabatic Ab Initio Molecular Dynamics with the Floating Occupation Molecular Orbital-Complete Active Space Configuration Interaction Method

(2017) Daniel Hollas et al.   Journal of Chemical Theory and Computation

Quantum design of photosynthesis for bio-inspired solar-energy conversion

(2017) Elisabet Romero et al.   NATURE

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach

(2016) Bin Jiang et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

The small impact of various partial charge distributions in ground and excited state on the computational Stokes shift of 1-methyl-6-oxyquinolinium betaine in diverse water models

(2016) Esther Heid et al.   JOURNAL OF CHEMICAL PHYSICS

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Cyclobutane Thymine Photodimerization Mechanism Revealed by Nonadiabatic Molecular Dynamics

(2016) Clemens Rauer et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

Electronic spectra from TDDFT and machine learning in chemical space

(2015) Raghunathan Ramakrishnan et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Ultrafast internal conversion in ethylene. II. Mechanisms and pathways for quenching and hydrogen elimination

(2012) T. K. Allison et al.   JOURNAL OF CHEMICAL PHYSICS

Nonadiabatic ab initio molecular dynamics including spin–orbit coupling and laser fields

(2011) Philipp Marquetand et al.   FARADAY DISCUSSIONS

Ultrafast internal conversion in ethylene. I. The excited state lifetime

(2011) H. Tao et al.   JOURNAL OF CHEMICAL PHYSICS

SHARC:ab InitioMolecular Dynamics with Surface Hopping in the Adiabatic Representation Including Arbitrary Couplings

(2011) Martin Richter et al.   Journal of Chemical Theory and Computation

Role of Rydberg States in the Photochemical Dynamics of Ethylene

(2011) Toshifumi Mori et al.   JOURNAL OF PHYSICAL CHEMISTRY A

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

The ORCA program system

(2011) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

Signatures of nonadiabaticO2dissociation at Al(111): First-principles fewest-switches study

(2010) Christian Carbogno et al.   PHYSICAL REVIEW B

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Nonadiabatic coupling vectors within linear response time-dependent density functional theory

(2009) Ivano Tavernelli et al.   JOURNAL OF CHEMICAL PHYSICS

Non-adiabatic dynamics using time-dependent density functional theory: Assessing the coupling strengths

(2009) Ivano Tavernelli et al.   JOURNAL OF MOLECULAR STRUCTURE-THEOCHEM

Multiphoton Absorbing Materials:  Molecular Designs, Characterizations, and Applications

(2008) Guang S. He et al.   CHEMICAL REVIEWS

On the nature of the π → π* ionic excited states: The V state of ethene as a prototype

(2008) Celestino Angeli   JOURNAL OF COMPUTATIONAL CHEMISTRY