Atom-density representations for machine learning

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber 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

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Insightful classification of crystal structures using deep learning

(2018) Angelo Ziletti et al.   Nature Communications

Chemical shifts in molecular solids by machine learning

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

Statistical Aspects of Wasserstein Distances

(2018) Victor M. Panaretos et al.   Annual Review of Statistics and Its Application

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

The many-body expansion combined with neural networks

(2017) Kun Yao et al.   JOURNAL OF CHEMICAL PHYSICS

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

(2017) Ryosuke Jinnouchi et al.   Journal of Physical Chemistry Letters

Wavelet Scattering Regression of Quantum Chemical Energies

(2017) Matthew Hirn et al.   MULTISCALE MODELING & SIMULATION

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

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Erratum: Neural network potential for Al-Mg-Si alloys [Phys. Rev. Materials 1 , 053604 (2017)]

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks

(2017) Seiji Kajita et al.   Scientific Reports

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

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

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Systematic comparison of crystalline and amorphous phases: Charting the landscape of water structures and transformations

(2015) Fabio Pietrucci et al.   JOURNAL OF CHEMICAL PHYSICS

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

(2014) Bin Jiang et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Machine learning methods in chemoinformatics

(2014) John B. O. Mitchell   Wiley Interdisciplinary Reviews-Computational Molecular Science

Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling

(2014) Jesús Carrete et al.   Physical Review X

Metrics for measuring distances in configuration spaces

(2013) Ali Sadeghi et al.   JOURNAL OF CHEMICAL PHYSICS

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

(2013) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

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

Publisher’s Note: On representing chemical environments [Phys. Rev. B87, 184115 (2013)]

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

\mathcal{O}(N) methods in electronic structure calculations

(2012) D R Bowler et al.   REPORTS ON PROGRESS IN PHYSICS

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

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

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization

(2009) Zhen Xie et al.   Journal of Chemical Theory and Computation

CUR matrix decompositions for improved data analysis

(2009) Michael W. Mahoney et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA