Machine learning model for non-equilibrium structures and energies of simple molecules

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

(2018) Matthias Rupp et al.   JOURNAL OF CHEMICAL PHYSICS

Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations

(2017) Jacob R. Boes et al.   Journal of Physical Chemistry C

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

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

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

(2017) Kun Yao et al.   Journal of Physical Chemistry Letters

Is the Accuracy of Density Functional Theory for Atomization Energies and Densities in Bonding Regions Correlated?

(2017) Kurt R. Brorsen et al.   Journal of Physical Chemistry Letters

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

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

Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites

(2016) Chiho Kim et al.   Journal of Physical Chemistry C

Machine Learning Force Fields: Construction, Validation, and Outlook

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

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

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(2015) Kevin Vu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Electronic spectra from TDDFT and machine learning in chemical space

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

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

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

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

(2013) O. Anatole von Lilienfeld   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Parameterization of a reactive force field using a Monte Carlo algorithm

(2013) E. Iype et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Machine learning of molecular electronic properties in chemical compound space

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

On representing chemical environments

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

Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?

(2012) Alexandre Varnek et al.   Journal of Chemical Information and Modeling

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS