Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

(2018) Gunnar Schmitz 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

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information

(2018) Oliver T. Unke 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

Accelerating atomic structure search with cluster regularization

(2018) K. H. Sørensen et al.   JOURNAL OF CHEMICAL PHYSICS

Tight-Binding Approximation-Enhanced Global Optimization

(2018) Maxime Van den Bossche et al.   Journal of Chemical Theory and Computation

Atomic Energies from a Convolutional Neural Network

(2018) Xin Chen et al.   Journal of Chemical Theory and Computation

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

(2018) Volker L. Deringer 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

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

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

Learning molecular energies using localized graph kernels

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

Nudged elastic band calculations accelerated with Gaussian process regression

(2017) Olli-Pekka Koistinen et al.   JOURNAL OF CHEMICAL PHYSICS

Combining Evolutionary Algorithms with Clustering toward Rational Global Structure Optimization at the Atomic Scale

(2017) Mathias S. Jørgensen et al.   Journal of Chemical Theory and Computation

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

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

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Structure of the SnO2(110)−(4×1) Surface

(2017) Lindsay R. Merte et al.   PHYSICAL REVIEW LETTERS

Quantum-chemical insights from deep tensor neural networks

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

A universal strategy for the creation of machine learning-based atomistic force fields

(2017) Tran Doan Huan et al.   npj Computational Materials

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Novel mixture model for the representation of potential energy surfaces

(2016) Tien Lam Pham et al.   JOURNAL OF CHEMICAL PHYSICS

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 Force Fields: Construction, Validation, and Outlook

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

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

A genetic algorithm for first principles global structure optimization of supported nano structures

(2014) Lasse B. Vilhelmsen et al.   JOURNAL OF CHEMICAL PHYSICS

B38: an all-boron fullerene analogue

(2014) Jian Lv et al.   Nanoscale

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

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

An adaptive genetic algorithm for crystal structure prediction

(2013) S Q Wu et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

On representing chemical environments

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

Stability and Metastability of Clusters in a Reactive Atmosphere: Theoretical Evidence for Unexpected Stoichiometries ofMgMOx

(2013) Saswata Bhattacharya et al.   PHYSICAL REVIEW LETTERS

Systematic Study ofAu6toAu12Gold Clusters on MgO(100)FCenters Using Density-Functional Theory

(2012) Lasse B. Vilhelmsen et al.   PHYSICAL REVIEW LETTERS

Comment on “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”

(2012) Jonathan E. Moussa   PHYSICAL REVIEW LETTERS

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

Ab initiorandom structure searching

(2011) Chris J Pickard et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Energy density in density functional theory: Application to crystalline defects and surfaces

(2011) Min Yu et al.   PHYSICAL REVIEW B

Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method

(2010) J Enkovaara et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

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

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

An evolutionary algorithm for global minimum search of binary atomic clusters

(2009) J.M.C. Marques et al.   CHEMICAL PHYSICS LETTERS

How to quantify energy landscapes of solids

(2009) Artem R. Oganov et al.   JOURNAL OF CHEMICAL PHYSICS