Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

Improving B3LYP heats of formation with three-dimensional molecular descriptors

(2016) Yuwei Zhou et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

(2009) Roman M. Balabin et al.   JOURNAL OF CHEMICAL PHYSICS

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

(2009) Matthew D. Wodrich et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Improving the B3LYP bond energies by using the X1 method

(2008) Jianming Wu et al.   JOURNAL OF CHEMICAL PHYSICS

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS