Atomic cluster expansion: Completeness, efficiency and stability

Recursive evaluation and iterative contraction of N-body equivariant features

(2020) Jigyasa Nigam et al.   JOURNAL OF CHEMICAL PHYSICS

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

(2019) Max Veit 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

De novo exploration and self-guided learning of potential-energy surfaces

(2019) Noam Bernstein et al.   npj Computational Materials

Alchemical and structural distribution based representation for universal quantum machine learning

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

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Solid harmonic wavelet scattering for predictions of molecule properties

(2018) Michael Eickenberg et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

(2018) So Fujikake et al.   JOURNAL OF CHEMICAL PHYSICS

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

(2018) Juraj Mavračić et al.   Journal of Physical Chemistry Letters

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer 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

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

(2018) Felix C. Mocanu et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Machine learning unifies the modeling of materials and molecules

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

Unifying the rotational and permutation symmetry of nuclear spin states: Schur-Weyl duality in molecular physics

(2016) Hanno Schmiedt et al.   JOURNAL OF CHEMICAL PHYSICS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

QM/MM Methods for Crystalline Defects. Part 1: Locality of the Tight Binding Model

(2016) Huajie Chen et al.   MULTISCALE MODELING & SIMULATION

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

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

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

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

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

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

On representing chemical environments

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

Optimized orthogonal tight-binding basis: Application to iron

(2011) Georg K. H. Madsen 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

Permutationally invariant potential energy surfaces in high dimensionality

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