Christopher Sutton

Materials Science · Physical Sciences

5h-index317citations17works4.32yr avg
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Research Topics

Machine Learning in Materials Science(11), X-ray Diffraction in Crystallography(5), Solid-state spectroscopy and crystallography(3), Electron and X-Ray Spectroscopy Techniques(3), Perovskite Materials and Applications(2)

Publications17 total

CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
arXiv (Cornell University)·2025· 3 citedOpen Access
Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
The Journal of Chemical Physics·2024· 11 citedOpen Access
Beast DB: A Computational Electrocatalysis Database with Solvation, Grand-Canonical Applied Potential, and Beyond-DFT Reaction Energetics
ECS Meeting Abstracts·2024
BEAST DB: Grand-Canonical Database of Electrocatalyst Properties
arXiv (Cornell University)·2024· 1 citedOpen Access
A foundation model for atomistic materials chemistry
arXiv (Cornell University)·2023· 229 citedOpen Access
Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains
Small·2023· 39 citedOpen Access
Research data supporting "Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Research data supporting "Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains"
2023Open Access
From structure mining to unsupervised exploration of atomic octahedral networks
arXiv (Cornell University)·2023Open Access
Improving the reliability of machine learned potentials for modeling inhomogenous liquids
arXiv (Cornell University)·2023Open Access
Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains
arXiv (Cornell University)·2023· 2 citedOpen Access
Identifying Domains of Applicability of Machine Learning Models for Materials Science
2019· 11 citedOpen Access
Identifying Domains of Applicability of Machine Learning Models for Materials Science
2019· 19 citedOpen Access
Identifying Domains of Applicability of Machine Learning Models for Materials Science
2019Open Access
Ongoing Projects at the FHI
MPG.PuRe (Max Planck Society)·2018
Considerations in the Stability of Oxide Alloys
MPG.PuRe (Max Planck Society)·2017
Low trap density of states in solution-deposited organic semiconductors by Vibration Assisted Crystallization
Bulletin of the American Physical Society·2014

Frequent Co-authors

Gábor Csányi(5), William J. Baldwin(4), Johan Klarbring(4), Miloš Dubajić(4), David Dell’Angelo(4), Claudia Caddeo(4), Samuel D. Stranks(4), Alessandro Mattoni(4), Aron Walsh(4), Ravishankar Sundararaman(4), Nima Karimitari(3), Mario Boley(3), Luca M. Ghiringhelli(3), Matthias Rupp(3), Matthias Scheffler(3), Xia Liang(2), Jilles Vreeken(2), Joshua Quinton(2), Kamron Fazel(2), Mauro Del Ben(2)