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RESEARCH PAPER · The Journal of Chemical Physics · 2018

A new approach for the prediction of partition functions using machine learning techniques

Caroline Desgranges, Jérôme Delhommelle

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42 direct citing works

A new approach for the prediction of partition functions using machine learning techniquesOriginalDetermination of mixture properties via a combined Expanded Wang-Landau simulations-Machine Learning approach8Prediction of the boiling and critical points of polycyclic aromatic hydrocarbons via Wang-Landau simulations and machine learning11Crystal nucleation along an entropic pathway: Teaching liquids how to transition18Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties3Deep neural network learning of complex binary sorption equilibria from molecular simulation data54From DFT to machine learning: recent approaches to materials science–a review849Structural Prediction and Inverse Design by a Strongly Correlated Neural Network20Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks27Quantum chemistry assisted by machine learning33Quantum Chemistry in the Age of Machine Learning417Flat-histogram extrapolation as a useful tool in the age of big data11Studying polymer physics by machine learning.0Evolutionary reinforcement learning of dynamical large deviations24Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles6Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids0Neural-network-based multistate solver for a static Schrödinger equation29Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning0Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores3A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy11GiFE: A Molecular-Size Agnostic and Understandable Gibbs Free Energy Function0Prediction of Vapor Liquid Equilibrium of Binary CO2-Contained Mixtures for Carbon Capture and Sequestration using Artificial Intelligence2Machine Learning for Fluid Thermodynamics: State of the Art and Open Challenges5Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Advancing the design of gold nanomaterials with machine-learned potentials5

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  1. Determination of mixture properties via a combined Expanded Wang-Landau simulations-Machine Learning approach

    Chemical Physics Letters · 2018-11-08

    Caroline Desgranges, Jérôme Delhommelle

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  2. Prediction of the boiling and critical points of polycyclic aromatic hydrocarbons via Wang-Landau simulations and machine learning

    Fluid Phase Equilibria · 2018-11-28

    Steven D. Groven, Caroline Desgranges, Jérôme Delhommelle

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  3. Crystal nucleation along an entropic pathway: Teaching liquids how to transition

    Physical review. E · 2018-12-10

    Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·

  4. Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

    Modern Physics · 2019-01-01

    果 卢

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  5. Deep neural network learning of complex binary sorption equilibria from molecular simulation data

    Chemical Science · 2019-01-01

    Yangzesheng Sun, Robert F. DeJaco, Joern Ilja Siepmann

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  6. From DFT to machine learning: recent approaches to materials science–a review

    Journal of Physics Materials · 2019-02-19

    Gabriel Ravanhani Schleder, A. C. M. Padilha, Carlos Mera Acosta, Marcio Costa, A. Fazzio

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  7. Structural Prediction and Inverse Design by a Strongly Correlated Neural Network

    Physical Review Letters · 2019-09-04

    Jianfeng Li, Hongdong Zhang, Jeff Z. Y. Chen

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  8. Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks

    The Journal of Physical Chemistry C · 2019-12-31

    Caroline Desgranges, Jérôme Delhommelle

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  9. Quantum chemistry assisted by machine learning

    Advances in quantum chemistry · 2020-01-01

    Pavlo O. Dral

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  10. Quantum Chemistry in the Age of Machine Learning

    The Journal of Physical Chemistry Letters · 2020-03-03

    Pavlo O. Dral

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  11. Flat-histogram extrapolation as a useful tool in the age of big data

    Molecular Simulation · 2020-04-13

    Nathan A. Mahynski, Harold W. Hatch, Matthew David Witman, David A. Sheen, Jeffrey R. Errington, Vincent K. Shen

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  12. Studying polymer physics by machine learning.

    UWSpace (University of Waterloo) · 2020-04-16

    Qianshi Wei

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  13. Evolutionary reinforcement learning of dynamical large deviations

    The Journal of Chemical Physics · 2020-07-27

    Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn

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  14. Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

    Machine Learning Science and Technology · 2020-11-05

    Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn

    Read original paper ↗ ·

  15. Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

    Molecular Systems Design & Engineering · 2020-11-23

    Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·

  16. Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids

    Entropy · 2021-01-26

    Stephen Whitelam

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  17. Neural-network-based multistate solver for a static Schrödinger equation

    Physical Review A · 2021-03-08

    Hong Li, Qilong Zhai, Jeff Z. Y. Chen

    Read original paper ↗ ·

  18. Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning

    International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) · 2021-10-15

    Yabin Xu, Shengjie Sun, Zhuang Wu

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  19. Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores

    Entropy · 2022-01-07

    Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·

  20. A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy

    Scientific Reports · 2023-04-11

    Juejing Liu, Xiaodong Zhao, Ke Zhao, Vitaliy G. Goncharov, Jérôme Delhommelle, Jian Ping Lin, Xiaofeng Guo

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  21. GiFE: A Molecular-Size Agnostic and Understandable Gibbs Free Energy Function

    ChemRxiv · 2023-05-10

    Jessica G. Freeze, Víctor S. Batista

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  22. Prediction of Vapor Liquid Equilibrium of Binary CO2-Contained Mixtures for Carbon Capture and Sequestration using Artificial Intelligence

    Petroleum & Petrochemical Engineering Journal · 2023-10-05

    Rostami S

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  23. Machine Learning for Fluid Thermodynamics: State of the Art and Open Challenges

    IEEE Access · 2025-01-01

    Federico Succetti, Massimo Panella, Paolo Giannitrapani, Jean-Christophe Rigo, Stefania Colonnese

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  24. Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

    International Journal of Quantum Chemistry · 2025-03-19

    Guo‐Jin Cao

    Read original paper ↗ ·

  25. Advancing the design of gold nanomaterials with machine-learned potentials

    Nano Express · 2025-05-09

    Kithma Sajini, Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·
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