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Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

ChemCatChem, 2024-05, Vol.16 (10), p.n/a

2024

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Many-body interactions and deep neural network potentials for water

The Journal of chemical physics, 2024-04, Vol.160 (14)

2024

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Engineering large nanoporous networks with size and shape selected by appropriate precursors

Carbon (New York), 2024-03, Vol.221, Article 118945

2024

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Trace Water in Lead Iodide Affecting Perovskite Crystal Nucleation Limits the Performance of Perovskite Solar Cells

Advanced materials (Weinheim), 2024-02, Vol.36 (7), p.e2310237-n/a

2024

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DP/MM: A Hybrid Model for Zinc–Protein Interactions in Molecular Dynamics

The journal of physical chemistry letters, 2024-01, Vol.15 (2), p.616-627

2024

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Atomic-level insights into single-layer Co-promoted MoS2 clusters supported on a rutile TiO2(110) model oxide support

Surface science, 2024-01, Vol.739, p.122385, Article 122385

2024

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Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models

ACS catalysis, 2023-12, Vol.13 (24), p.16032-16044

2023

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Multifidelity Machine Learning for Molecular Excitation Energies

Journal of chemical theory and computation, 2023-11, Vol.19 (21), p.7658-7670

2023

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Electrical and thermal conductivity in graphene-enhanced carbon-fibre/PEEK: The effect of interlayer loading

Carbon (New York), 2023-11, Vol.215, p.118463, Article 118463

2023

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Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

Journal of chemical theory and computation, 2023-10, Vol.19 (20), p.7358-7370

2023

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Developments and further applications of ephemeral data derived potentials

The Journal of chemical physics, 2023-10, Vol.159 (14)

2023

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Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces

Physical chemistry chemical physics : PCCP, 2023-09, Vol.25 (37), p.25828-25837

2023

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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

The Journal of chemical physics, 2023-07, Vol.159 (4)

2023

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Generating candidates in global optimization algorithms using complementary energy landscapes

The Journal of chemical physics, 2023-07, Vol.159 (2)

2023

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A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar

The Journal of chemical physics, 2023-07, Vol.159 (1)

2023

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Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2023-06, Vol.127 (24), p.5252-5263

Rappoport, Dmitrij

2023

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Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems

The Journal of chemical physics, 2023-06, Vol.158 (23)

2023

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Photoswitchable Copper(I) and Copper(II) Complexes of Phenylazo‐3,5‐dimethylpyrazole Incorporated Ligands

ChemPhotoChem, 2023-06, Vol.7 (6), p.n/a

2023

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Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Journal of chemical theory and computation, 2023-05, Vol.19 (10), p.2985-2995

2023

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NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule

The Journal of chemical physics, 2023-05, Vol.158 (19)

2023

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M-Chem: a modular software package for molecular simulation that spans scientific domains

Molecular physics, 2023-05, Vol.121 (9-10)

2023

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HOAX: a hyperparameter optimisation algorithm explorer for neural networks

Molecular physics, 2023-05, Vol.121 (9-10)

2023

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A linear surrogate for optimising functions of an orthogonal matrix with applications in wave function theory

Molecular physics, 2023-05, Vol.121 (9-10)

2023

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Lightweight and effective tensor sensitivity for atomistic neural networks

The Journal of chemical physics, 2023-05, Vol.158 (18)

2023

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Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Many-body interactions and deep neural network potentials for water

Engineering large nanoporous networks with size and shape selected by appropriate precursors

Trace Water in Lead Iodide Affecting Perovskite Crystal Nucleation Limits the Performance of Perovskite Solar Cells

DP/MM: A Hybrid Model for Zinc–Protein Interactions in Molecular Dynamics

Atomic-level insights into single-layer Co-promoted MoS2 clusters supported on a rutile TiO2(110) model oxide support

Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models

Multifidelity Machine Learning for Molecular Excitation Energies

Electrical and thermal conductivity in graphene-enhanced carbon-fibre/PEEK: The effect of interlayer loading

Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

Developments and further applications of ephemeral data derived potentials

Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

Generating candidates in global optimization algorithms using complementary energy landscapes

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar

Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models

Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems

Photoswitchable Copper(I) and Copper(II) Complexes of Phenylazo‐3,5‐dimethylpyrazole Incorporated Ligands

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule

M-Chem: a modular software package for molecular simulation that spans scientific domains

HOAX: a hyperparameter optimisation algorithm explorer for neural networks

A linear surrogate for optimising functions of an orthogonal matrix with applications in wave function theory

Lightweight and effective tensor sensitivity for atomistic neural networks

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