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GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Scientific data, 2022-04, Vol.9 (1), p.185-185, Article 185

2022

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

ACS central science, 2018-02, Vol.4 (2), p.268-276

2018

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Coarse-graining auto-encoders for molecular dynamics

npj computational materials, 2019-12, Vol.5 (1), p.1-9, Article 125

2019

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Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

Nature communications, 2022-06, Vol.13 (1), p.3440-3440, Article 3440

2022

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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Nature communications, 2021-08, Vol.12 (1), p.5104-5104, Article 5104

2021

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Anthraquinone Derivatives in Aqueous Flow Batteries

Advanced energy materials, 2017-04, Vol.7 (8), p.np-n/a

2017

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What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Annual review of materials research, 2015-07, Vol.45 (1), p.195-216

2015

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Supramolecular Recognition in Crystalline Nanocavities through Monte Carlo and Voronoi Network Algorithms

Journal of physical chemistry. C, 2021-02, Vol.125 (5), p.3009-3017

2021

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Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling

Journal of chemical information and modeling, 2023-09, Vol.63 (18), p.5794-5802

2023

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Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials

Journal of physical chemistry. C, 2022-02, Vol.126 (4), p.2264-2275

2022

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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

Nature materials, 2016-10, Vol.15 (10), p.1120-1127

2016

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Mechanisms of Lactone Hydrolysis in Neutral and Alkaline Conditions

Journal of organic chemistry, 2013-07, Vol.78 (14), p.6868-6879

2013

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Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

ACS central science, 2021-05, Vol.7 (5), p.858-867

2021

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Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries

Journal of materials chemistry. A, Materials for energy and sustainability, 2019-05, Vol.7 (2), p.12833-12841

2019

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Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

Journal of chemical theory and computation, 2024-08, Vol.20 (15), p.6559-6568

2024

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Entropy and Energy Profiles of Chemical Reactions

Journal of chemical theory and computation, 2023-08, Vol.19 (16), p.5369-5379

2023

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Data-Driven Design of Biselective Templates for Intergrowth Zeolites

The journal of physical chemistry letters, 2021-11, Vol.12 (43), p.10689-10694

2021

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A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Journal of the American Chemical Society, 2024-08, Vol.146 (31), p.21877-21888

2024

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Learning a reactive potential for silica-water through uncertainty attribution

Nature communications, 2024-07, Vol.15 (1), p.6030-10, Article 6030

2024

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Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

ACS central science, 2023-02, Vol.9 (2), p.166-176

2023

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Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others

Nature communications, 2023-05, Vol.14 (1), p.2878-2878, Article 2878

2023

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Learning Matter: Materials Design with Machine Learning and Atomistic Simulations

Accounts of materials research, 2022-03, Vol.3 (3), p.343-357

2022

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Molecular machine learning with conformer ensembles

Machine learning: science and technology, 2023-09, Vol.4 (3), p.35025

2023

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Repurposing Templates for Zeolite Synthesis from Simulations and Data Mining

Chemistry of materials, 2022-06, Vol.34 (12), p.5366-5376

2022

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Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS

JPhys Energy, 2023-04, Vol.5 (2), p.24004

2023

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GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Coarse-graining auto-encoders for molecular dynamics

Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Anthraquinone Derivatives in Aqueous Flow Batteries

What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Supramolecular Recognition in Crystalline Nanocavities through Monte Carlo and Voronoi Network Algorithms

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling

Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

Mechanisms of Lactone Hydrolysis in Neutral and Alkaline Conditions

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

Entropy and Energy Profiles of Chemical Reactions

Data-Driven Design of Biselective Templates for Intergrowth Zeolites

A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Learning a reactive potential for silica-water through uncertainty attribution

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others

Learning Matter: Materials Design with Machine Learning and Atomistic Simulations

Molecular machine learning with conformer ensembles

Repurposing Templates for Zeolite Synthesis from Simulations and Data Mining

Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS

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