Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

(2022) Andrei Tereshchenko et al.   MOLECULES

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

(2021) Varnavas D. Mouchlis et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

(2021) Valentin Vassilev-Galindo et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning for perovskite materials design and discovery

(2021) Qiuling Tao et al.   npj Computational Materials

Four Generations of High-Dimensional Neural Network Potentials

(2021) Jörg Behler   CHEMICAL REVIEWS

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

(2021) Gregory Fonseca et al.   JOURNAL OF CHEMICAL PHYSICS

Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics

(2021) C. D. Rankine et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Quantitative Analysis of the UV–Vis Spectra for Gold Nanoparticles Powered by Supervised Machine Learning

(2021) D. M. Pashkov et al.   Journal of Physical Chemistry C

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

(2021) Dávid Péter Kovács et al.   Nature Communications

Ab Initio Machine Learning in Chemical Compound Space

(2021) Bing Huang et al.   CHEMICAL REVIEWS

Perspective on integrating machine learning into computational chemistry and materials science

(2021) Julia Westermayr et al.   JOURNAL OF CHEMICAL PHYSICS

Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach

(2021) A. Martini et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine Learning Force Fields: Recent Advances and Remaining Challenges

(2021) Igor Poltavsky et al.   Journal of Physical Chemistry Letters

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Artificial Neural Networks

(2021) Bipeng Wang et al.   Journal of Physical Chemistry Letters

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

(2021) Liangrui Pan et al.   JOURNAL OF RAMAN SPECTROSCOPY

Molecular excited states through a machine learning lens

(2021) Pavlo O. Dral et al.   Nature Reviews Chemistry

On the analysis of X-ray absorption spectra for polyoxometallates

(2021) E. Falbo et al.   CHEMICAL PHYSICS LETTERS

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

(2021) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning

(2021) E. G. Kozyr et al.   Journal of Physical Chemistry C

Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning

(2021) Wei Bin How et al.   Journal of Physical Chemistry Letters

Speeding up quantum dissipative dynamics of open systems with kernel methods

(2021) Arif Ullah et al.   NEW JOURNAL OF PHYSICS

Resolving competing conical intersection pathways: time-resolved X-ray absorption spectroscopy of trans-1,3-butadiene

(2021) Issaka Seidu et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

Ultrafast X-ray Photochemistry at European XFEL: Capabilities of the Femtosecond X-ray Experiments (FXE) Instrument

(2020) Dmitry Khakhulin et al.   Applied Sciences-Basel

A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

(2020) C. D. Rankine et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

(2020) Matthew R. Carbone et al.   PHYSICAL REVIEW LETTERS

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

(2020) Joshua L. Lansford et al.   Nature Communications

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

The Role of Structural Representation in the Performance of a Deep Neural Network for X-ray Spectroscopy

(2020) Marwah M.M. Madkhali et al.   MOLECULES

Machine learning accurate exchange and correlation functionals of the electronic density

(2020) Sebastian Dick et al.   Nature Communications

Machine learning approaches for the prediction of materials properties

(2020) Siwar Chibani et al.   APL Materials

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

(2020) Steven B. Torrisi et al.   npj Computational Materials

Exploring chemical compound space with quantum-based machine learning

(2020) O. Anatole von Lilienfeld et al.   Nature Reviews Chemistry

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

(2020) J. Westermayr et al.   JOURNAL OF CHEMICAL PHYSICS

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

(2020) Zhuoran Qiao et al.   JOURNAL OF CHEMICAL PHYSICS

Radial Distribution Function from X-ray Absorption near Edge Structure with an Artificial Neural Network

(2020) Shin Kiyohara et al.   JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Machine Learning for Electronically Excited States of Molecules

(2020) Julia Westermayr et al.   CHEMICAL REVIEWS

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

(2019) Andrew F. Zahrt et al.   SCIENCE

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Tuning the Structure of Pt Nanoparticles Through Support Interactions: An In Situ Polarized X-ray Absorption Study Coupled with Atomistic Simulations

(2019) Mahdi Ahmadi et al.   Journal of Physical Chemistry C

Classification of local chemical environments from x-ray absorption spectra using supervised machine learning

(2019) Matthew R. Carbone et al.   PHYSICAL REVIEW MATERIALS

Deep Learning in Chemistry

(2019) Adam C. Mater et al.   Journal of Chemical Information and Modeling

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

(2019) Annika Stuke et al.   JOURNAL OF CHEMICAL PHYSICS

Is There a Negative Thermal Expansion in Supported Metal Nanoparticles?: An In-Situ X-ray Absorption Study Coupled with Neural Network Analysis

(2019) Janis Timoshenko et al.   Journal of Physical Chemistry C

Machine learning approaches to XANES spectra for quantitative 3D structural determination: The case of CO2 adsorption on CPO-27-Ni MOF

(2019) A.A. Guda et al.   RADIATION PHYSICS AND CHEMISTRY

Learning Retrosynthetic Planning through Simulated Experience

(2019) John S. Schreck et al.   ACS Central Science

Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors

(2019) Janis Timoshenko et al.   ACS Catalysis

Machine Learning for Vibrational Spectroscopic Maps

(2019) Alexei A. Kananenka et al.   Journal of Chemical Theory and Computation

Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning

(2019) Lixue Cheng et al.   Journal of Chemical Theory and Computation

Tracking multiple components of a nuclear wavepacket in photoexcited Cu(I)-phenanthroline complex using ultrafast X-ray spectroscopy

(2019) Tetsuo Katayama et al.   Nature Communications

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

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

Toward time-resolved laser T-jump/X-ray probe spectroscopy in aqueous solutions

(2019) O. Cannelli et al.   Structural Dynamics-US

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

(2019) Wenbo Sun et al.   Science Advances

Data-driven Chemical Reaction Prediction and Retrosynthesis

(2019) Vishnu H Nair et al.   CHIMIA

Ultrafast Spectroscopy: State of the Art and Open Challenges

(2019) Margherita Maiuri et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

Machine Learning in Computer-Aided Synthesis Planning

(2018) Connor W. Coley et al.   ACCOUNTS OF CHEMICAL RESEARCH

SchNet – A deep learning architecture for molecules and materials

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

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

(2018) M. Gastegger 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

Ultrafast X-Ray Spectroscopy of Conical Intersections

(2018) Simon P. Neville et al.   PHYSICAL REVIEW LETTERS

Revealing hole trapping in zinc oxide nanoparticles by time-resolved X-ray spectroscopy

(2018) Thomas J. Penfold et al.   Nature Communications

Automated generation and ensemble-learned matching of X-ray absorption spectra

(2018) Chen Zheng et al.   npj Computational Materials

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

(2018) Matthew Welborn et al.   Journal of Chemical Theory and Computation

Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning

(2018) Janis Timoshenko et al.   Journal of Physical Chemistry C

Nonadiabatic Excited-State Dynamics with Machine Learning

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

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Planning chemical syntheses with deep neural networks and symbolic AI

(2018) Marwin H. S. Segler et al.   NATURE

High-throughput computational X-ray absorption spectroscopy

(2018) Kiran Mathew et al.   Scientific Data

Approximating Quasiparticle and Excitation Energies from Ground State Generalized Kohn–Sham Calculations

(2018) Yuncai Mei et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Deep Learning for Nonadiabatic Excited-State Dynamics

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

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning

(2018) Janis Timoshenko et al.   NANO LETTERS

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

Deep learning for computational chemistry

(2017) Garrett B. Goh et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

(2017) Janis Timoshenko et al.   Journal of Physical Chemistry Letters

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

(2017) A. Sanchez-Gonzalez et al.   Nature Communications

High-throughput screening of bimetallic catalysts enabled by machine learning

(2017) Zheng Li et al.   Journal of Materials Chemistry A

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Probing spin–vibronic dynamics using femtosecond X-ray spectroscopy

(2016) T. J. Penfold et al.   FARADAY DISCUSSIONS

Beyond structure: ultrafast X-ray absorption spectroscopy as a probe of non-adiabatic wavepacket dynamics

(2016) Simon P. Neville et al.   FARADAY DISCUSSIONS

Excited state X-ray absorption spectroscopy: Probing both electronic and structural dynamics

(2016) Simon P. Neville et al.   JOURNAL OF CHEMICAL PHYSICS

Probing spin–vibronic dynamics using femtosecond X-ray spectroscopy

(2016) T. J. Penfold et al.   FARADAY DISCUSSIONS

Beyond structure: ultrafast X-ray absorption spectroscopy as a probe of non-adiabatic wavepacket dynamics

(2016) Simon P. Neville et al.   FARADAY DISCUSSIONS

Probing wavepacket dynamics using ultrafast x-ray spectroscopy

(2015) G Capano et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

Deep learning

(2015) Yann LeCun et al.   NATURE

A Vibronic Coupling Hamiltonian to Describe the Ultrafast Excited State Dynamics of a Cu(I)-Phenanthroline Complex

(2014) Gloria Capano et al.   CHIMIA

Valence Shell Photoelectron Spectroscopy of Pyrene and Fluorene: Photon Energy Dependence in the Far-Ultraviolet Region

(2014) Preeti Manjari Mishra et al.   JOURNAL OF PHYSICAL CHEMISTRY A

A Quantum Dynamics Study of the Ultrafast Relaxation in a Prototypical Cu(I)–Phenanthroline

(2014) G. Capano et al.   JOURNAL OF PHYSICAL CHEMISTRY A

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Photooxidation and photoaquation of iron hexacyanide in aqueous solution: A picosecond X-ray absorption study

(2014) M. Reinhard et al.   Structural Dynamics

Real Time Determination of the Electronic Structure of Unstable Reaction Intermediates during Au2O3 Reduction

(2013) Jakub Szlachetko et al.   Journal of Physical Chemistry Letters

A compact X-ray free-electron laser emitting in the sub-ångström region

(2012) Tetsuya Ishikawa et al.   Nature Photonics

Highly coherent and stable pulses from the FERMI seeded free-electron laser in the extreme ultraviolet

(2012) E. Allaria et al.   Nature Photonics

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Probing the Transition from Hydrophilic to Hydrophobic Solvation with Atomic Scale Resolution

(2011) Van-Thai Pham et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

First lasing and operation of an ångstrom-wavelength free-electron laser

(2010) P. Emma et al.   Nature Photonics

Time-resolved X-ray absorption spectroscopy of infrared-laser-induced temperature jumps in liquid water

(2009) G. Gavrila et al.   APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING

Self-consistent aspects of x-ray absorption calculations

(2009) O Bunău et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Probing the hydrogen-bond network of water via time-resolved soft X-ray spectroscopy

(2009) Nils Huse et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS