Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface

Quantum Machine Learning in Chemical Compound Space

(2018) O. Anatole von Lilienfeld   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

(2018) T. L. Jacobsen et al.   PHYSICAL REVIEW LETTERS

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

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

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

(2017) S. Gabardi et al.   Journal of Physical Chemistry C

Boron clusters with 46, 48, and 50 atoms: competition among the core–shell, bilayer and quasi-planar structures

(2017) Linwei Sai et al.   Nanoscale

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

From planar boron clusters to borophenes and metalloborophenes

(2017) Wan-Lu Li et al.   Nature Reviews Chemistry

CALYPSO structure prediction method and its wide application

(2016) Hui Wang et al.   COMPUTATIONAL MATERIALS SCIENCE

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

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

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Semi-metallic Be5C2 monolayer global minimum with quasi-planar pentacoordinate carbons and negative Poisson’s ratio

(2016) Yu Wang et al.   Nature Communications

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

(2016) Pavel E. Dolgirev et al.   AIP Advances

Inverse design of materials by multi-objective differential evolution

(2015) Yue-Yu Zhang et al.   COMPUTATIONAL MATERIALS SCIENCE

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

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

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

B84: a quasi-planar boron cluster stabilized with hexagonal holes

(2015) Amol B. Rahane et al.   Nanoscale

B28: the smallest all-boron cage from an ab initio global search

(2015) Jijun Zhao et al.   Nanoscale

Stabilization of fullerene-like boron cages by transition metal encapsulation

(2015) Jian Lv et al.   Nanoscale

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Be2C Monolayer with Quasi-Planar Hexacoordinate Carbons: A Global Minimum Structure

(2014) Yafei Li et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Perspective: Crystal structure prediction at high pressures

(2014) Yanchao Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Alternative Approach to Chemical Accuracy: A Neural Networks-Based First-Principles Method for Heat of Formation of Molecules Made of H, C, N, O, F, S, and Cl

(2014) Jian Sun et al.   JOURNAL OF PHYSICAL CHEMISTRY A

B38: an all-boron fullerene analogue

(2014) Jian Lv et al.   Nanoscale

Reactions of xenon with iron and nickel are predicted in the Earth's inner core

(2014) Li Zhu et al.   Nature Chemistry

Observation of an all-boron fullerene

(2014) Hua-Jin Zhai et al.   Nature Chemistry

Stochastic surface walking method for crystal structure and phase transition pathway prediction

(2014) Cheng Shang et al.   PHYSICAL CHEMISTRY 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

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Self-assembled ultrathin nanotubes on diamond (100) surface

(2014) Shaohua Lu et al.   Nature Communications

Planar hexagonal B36 as a potential basis for extended single-atom layer boron sheets

(2014) Zachary A. Piazza et al.   Nature Communications

First-principles structural design of superhard materials

(2013) Xinxin Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

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

CALYPSO: A method for crystal structure prediction

(2012) Yanchao Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm

(2012) Yanchao Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Particle-swarm structure prediction on clusters

(2012) Jian Lv et al.   JOURNAL OF CHEMICAL PHYSICS

B80 and B101–103 clusters: Remarkable stability of the core-shell structures established by validated density functionals

(2012) Fengyu Li 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

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

(2011) Roman M. Balabin et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Predicted Novel High-Pressure Phases of Lithium

(2011) Jian Lv et al.   PHYSICAL REVIEW LETTERS

Energy Landscape of Fullerene Materials: A Comparison of Boron to Boron Nitride and Carbon

(2011) Sandip De et al.   PHYSICAL REVIEW LETTERS

XtalOpt: An open-source evolutionary algorithm for crystal structure prediction

(2010) David C. Lonie et al.   COMPUTER PHYSICS COMMUNICATIONS

B80and Other Medium-Sized Boron Clusters: Core−Shell Structures, Not Hollow Cages

(2010) Jijun Zhao et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Ab Initio Prediction of Amorphous B84

(2010) Bo Shang et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Crystal structure prediction via particle-swarm optimization

(2010) Yanchao Wang 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