Gaussian process regression for geometry optimization

Nudged elastic band calculations accelerated with Gaussian process regression

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

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

(2017) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient Geometry Minimization and Transition Structure Optimization Using Interpolated Potential Energy Surfaces and Iteratively Updated Hessians

(2017) Jingjing Zheng et al.   Journal of Chemical Theory and Computation

Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression

(2016) Jonathan P. Alborzpour et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

An anionic molybdenum amidato bisalkyl alkylidyne complex

(2015) Suman Sen et al.   JOURNAL OF ORGANOMETALLIC 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

The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning

(2014) Timothy L. Fletcher et al.   THEORETICAL CHEMISTRY ACCOUNTS

On representing chemical environments

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

ChemShell-a modular software package for QM/MM simulations

(2013) Sebastian Metz et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging

(2011) Matthew J.L. Mills et al.   Computational and Theoretical Chemistry

DL-FIND: An Open-Source Geometry Optimizer for Atomistic Simulations†

(2009) Johannes Kästner et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning

(2009) Chris M. Handley et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS