Addressing uncertainty in atomistic machine learning

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

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

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

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

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

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

(2010) M. Valiev et al.   COMPUTER PHYSICS COMMUNICATIONS

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

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

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

(2009) A. Pukrittayakamee et al.   JOURNAL OF CHEMICAL PHYSICS