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Richard Messerly

Computational Scientist

Oak Ridge National Laboratory

United States

Research Interests

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Articles (7)

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2023

Collaborators (6)

Mario Barbatti

Professor

Aix-Marseille University

FRANCE

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Mikołaj Martyka

University of Warsaw

POLAND

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Ben Nebgen

Scientist

Los Alamos National Laboratory

UNITED STATES

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Małgorzata Z Makoś

Assistant Professor

The University of Alabama

UNITED STATES

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Olexandr Isayev

Assistant Professor

Carnegie Mellon University

UNITED STATES

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Brenden W Hamilton

Staff Scientist

Los Alamos National Laboratory

UNITED STATES

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Richard Messerly

Research Interests

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Articles (7)

A data-efficient machine-learning approach for modeling the photodynamics of all-trans hexatriene based on multireference configuration interaction calculations

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need*

Toward machine learning interatomic potentials for modeling uranium mononitride

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Synergy of semiempirical models and machine learning in computational chemistry

Collaborators (6)

Mario Barbatti

Mikołaj Martyka

Ben Nebgen

Małgorzata Z Makoś

Olexandr Isayev

Brenden W Hamilton

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