Recursive evaluation and iterative contraction of N-body equivariant features

FCHL revisited: Faster and more accurate quantum machine learning

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

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials

(2020) Ryosuke Jinnouchi et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

(2020) V. Zaverkin et al.   Journal of Chemical Theory and Computation

Atom-density representations for machine learning

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

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

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

Extending the accuracy of the SNAP interatomic potential form

(2018) Mitchell A. Wood 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

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Learning molecular energies using localized graph kernels

(2017) Grégoire Ferré et al.   JOURNAL OF CHEMICAL PHYSICS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Machine learning unifies the modeling of materials and molecules

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

Accurate force field for molybdenum by machine learning large materials data

(2017) Chi Chen et al.   PHYSICAL REVIEW MATERIALS

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

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

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

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

Sparse representation for a potential energy surface

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

On representing chemical environments

(2013) Albert P. Bartók 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