James Chapman

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Assistant Professor

Boston University

United States

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About

James Chapman is an Assistant Professor at Boston University, USA. His research focuses on the development and application of advanced spectroscopy techniques to explore disordered materials, ionic diffusivity, and atomistic structural transitions. Recent contributions include the use of machine learning and graph theory for insights into atomic structures and properties in materials science.

Recent Grants

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Finite Temperature Structure Prediction

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Articles (13)

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2020

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2020

Collaborators (5)

Nicole Adelstein

San Francisco State University

UNITED STATES

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Morgan Stefik

Associate Professor

University of South Carolina

UNITED STATES

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Nir Goldman

UNITED STATES

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Lauren Jarocha

Assistant Professor

Furman University

UNITED STATES

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Yang Jiao

Assistant Professor

Arizona State University

UNITED STATES

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James Chapman

Research Interests

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About

Recent Grants

Articles (13)

Score-based denoising for atomic structure identification

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Hydrogen in disordered titania: connecting local chemistry, structure, and stoichiometry through accelerated exploration

Amorphization of Pseudocapacitive T−Nb2O5 Accelerates Lithium Diffusivity as Revealed Using Tunable Isomorphic Architectures

Cover Feature: Amorphization of Pseudocapacitive T−Nb2O5 Accelerates Lithium Diffusivity as Revealed Using Tunable Isomorphic Architectures (Batteries & Supercaps 6/2022)

Quantifying the atomistic free-volume morphology of materials with graph theory

Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy

Efficient and universal characterization of atomic structures through a topological graph order parameter

Multiscale Modeling of Defect Phenomena in Platinum Using Machine Learning of Force Fields

Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields

Collaborators (5)

Nicole Adelstein

Morgan Stefik

Nir Goldman

Lauren Jarocha

Yang Jiao

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