Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

(2020) Jiang Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

(2020) Christoph Scherer et al.   Journal of Chemical Theory and Computation

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies

(2020) Jenke Scheen et al.   Journal of Chemical Information and Modeling

Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients

(2020) Julia Gebhardt et al.   Journal of Chemical Information and Modeling

Hydration free energies from kernel-based machine learning: Compound-database bias

(2020) Clemens Rauer et al.   JOURNAL OF CHEMICAL PHYSICS

Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms

(2020) Martín Leandro Paleico et al.   JOURNAL OF CHEMICAL PHYSICS

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

(2020) Sergei Manzhos et al.   CHEMICAL REVIEWS

Evidence for supercritical behaviour of high-pressure liquid hydrogen

(2020) Bingqing Cheng et al.   NATURE

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

(2020) Bing Huang et al.   Nature Chemistry

Machine Learning for Electronically Excited States of Molecules

(2020) Julia Westermayr et al.   CHEMICAL REVIEWS

Ab initio thermodynamics of liquid and solid water

(2019) Bingqing Cheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Calculation of solvation free energy utilizing a constrained QM/MM approach combined with a theory of solutions

(2019) Hideaki Takahashi et al.   JOURNAL OF CHEMICAL PHYSICS

Performance of 3D-RISM-KH in Predicting Hydration Free Energy: Effect of Solute Parameters

(2019) Dipankar Roy et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Resolution limit of data-driven coarse-grained models spanning chemical space

(2019) Kiran H. Kanekal et al.   JOURNAL OF CHEMICAL PHYSICS

Advances and challenges in modeling solvated reaction mechanisms for renewable fuels and chemicals

(2019) Yasemin Basdogan et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Machine learning for protein folding and dynamics

(2019) Frank Noé et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Machine Learning-Guided Approach for Studying Solvation Environments

(2019) Yasemin Basdogan et al.   Journal of Chemical Theory and Computation

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Approaches for Calculating Solvation Free Energies and Enthalpies Demonstrated with an Update of the FreeSolv Database

(2017) Guilherme Duarte Ramos Matos et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Comparison of Implicit and Explicit Solvent Models for the Calculation of Solvation Free Energy in Organic Solvents

(2017) Jin Zhang 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

Proton-Transfer-Driven Water Exchange Mechanism in the Na+ Solvation Shell

(2017) Matti Hellström et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

(2017) Vanessa Quaranta et al.   Journal of Physical Chemistry Letters

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

(2017) Peter Eastman et al.   PLoS Computational Biology

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

The adaptive buffered force QM/MM method in the CP2K and AMBER software packages

(2015) Letif Mones et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Calculation of Solvation Free Energies with DCOSMO-RS

(2015) Andreas Klamt et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Critical Evaluation of Implicit Solvent Models for Predicting Aqueous Oxidation Potentials of Neutral Organic Compounds

(2013) Jennifer J. Guerard et al.   Journal of Chemical Theory and Computation

Generalized Born Solvation Model SM12

(2012) Aleksandr V. Marenich et al.   Journal of Chemical Theory and Computation

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

On the Performance of Continuum Solvation Methods. A Comment on “Universal Approaches to Solvation Modeling”

(2009) Andreas Klamt et al.   ACCOUNTS OF CHEMICAL RESEARCH

Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions

(2009) Aleksandr V. Marenich et al.   JOURNAL OF PHYSICAL CHEMISTRY B

The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06 functionals and 12 other functionals

(2008) Yan Zhao et al.   THEORETICAL CHEMISTRY ACCOUNTS