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

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

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

(2017) Felix A. Faber 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

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

Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn

(2017) Tarak K. Patra et al.   ACS Combinatorial Science

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

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

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

Report on the sixth blind test of organic crystal structure prediction methods

(2016) Anthony M. Reilly et al.   Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials

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 Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

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

Predicting interface structures: FromSrTiO3to graphene

(2014) Georg Schusteritsch 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

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

(2013) Saswata Bhattacharya et al.   PHYSICAL REVIEW LETTERS

Packing Defects into Ordered Structures: Strands onTiO2

(2012) R. Bechstein 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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Ab initiorandom structure searching

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

Steps on rutile TiO2(110): Active sites for water and methanol dissociation

(2011) Umberto Martinez et al.   PHYSICAL REVIEW B

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