Deep Learning-Based Increment Theory for Formation Enthalpy Predictions

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

(2022) Peikun Zheng et al.   Journal of Physical Chemistry Letters

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds

(2021) Qiyuan Zhao et al.   Journal of Chemical Information and Modeling

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

(2021) Mengjie Liu et al.   Journal of Chemical Information and Modeling

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models

(2021) Logan Ward et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Self-Consistent Component Increment Theory for Predicting Enthalpy of Formation

(2020) Qiyuan Zhao et al.   Journal of Chemical Information and Modeling

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

(2020) Christian Devereux et al.   Journal of Chemical Theory and Computation

SingleNN: Modified Behler–Parrinello Neural Network with Shared Weights for Atomistic Simulations with Transferability

(2020) Mingjie Liu et al.   Journal of Physical Chemistry C

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

(2019) Yi-Pei Li et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Analyzing Learned Molecular Representations for Property Prediction

(2019) Kevin Yang et al.   Journal of Chemical Information and Modeling

Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach

(2019) Colin A. Grambow et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Charge Optimized Many Body (COMB) potentials for simulation of nuclear fuel and clad

(2018) Simon R. Phillpot et al.   COMPUTATIONAL MATERIALS SCIENCE

An Extended Group Additivity Method for Polycyclic Thermochemistry Estimation

(2018) Kehang Han et al.   INTERNATIONAL JOURNAL OF CHEMICAL KINETICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber 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

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

(2018) Denis Kuzminykh et al.   MOLECULAR PHARMACEUTICS

Automated computational thermochemistry for butane oxidation: A prelude to predictive automated combustion kinetics

(2018) Murat Keçeli et al.   PROCEEDINGS OF THE COMBUSTION INSTITUTE

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

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

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks

(2017) Seiji Kajita et al.   Scientific Reports

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

(2016) Connie W. Gao et al.   COMPUTER PHYSICS COMMUNICATIONS

Uncertainty quantification for quantum chemical models of complex reaction networks

(2016) Jonny Proppe et al.   FARADAY DISCUSSIONS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Thermodynamics of Anharmonic Systems: Uncoupled Mode Approximations for Molecules

(2016) Yi-Pei Li et al.   Journal of Chemical Theory and Computation

Systematic Error Estimation for Chemical Reaction Energies

(2016) Gregor N. Simm et al.   Journal of Chemical Theory and Computation

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Uncertainty quantification for quantum chemical models of complex reaction networks

(2016) Jonny Proppe et al.   FARADAY DISCUSSIONS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

An expanded calibration study of the explicitly correlated CCSD(T)-F12b method using large basis set standard CCSD(T) atomization energies

(2013) David Feller et al.   JOURNAL OF CHEMICAL PHYSICS

ωB97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy

(2013) Narbe Mardirossian et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

On representing chemical environments

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

Further benchmarks of a composite, convergent, statistically calibrated coupled-cluster-based approach for thermochemical and spectroscopic studies

(2012) David Feller et al.   MOLECULAR PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Chemical accuracy in ab initio thermochemistry and spectroscopy: current strategies and future challenges

(2012) Kirk A. Peterson et al.   THEORETICAL CHEMISTRY ACCOUNTS

Challenges for Density Functional Theory

(2011) Aron J. Cohen et al.   CHEMICAL REVIEWS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS