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University of Illinois at Urbana-Champaign

Designing efficient, interpretable, and generalizable machine learning interatomic potentials

dc:creator Vita, Joshua Alexander

thesis:degree_name Ph.D.

dc:date2023

Abstract

dc:description

Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms

Degree

thesis:*

Name thesis:degree_name: Ph.D.
Level thesis:degree_level: Dissertation
Discipline thesis:degree_discipline: Materials Science & Engr
Grantor: University of Illinois at Urbana-Champaign
Year dc:date: 2023

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Vita, Joshua Alexander
Contributors dc:contributor: Trinkle, Dallas
Bellon, Pascal
Tadmor, Ellad
Schleife, Andre

Subjects

dc:subject × 3

Rights

dc:rights

Statement dc:rights: Copyright 2023 Joshua Vita
Language dc:language: en, eng

Identifiers

dc:identifier.*

Handle dc:identifier: https://hdl.handle.net/2142/121918
OAI identifier oai:identifier: oai:www.ideals.illinois.edu:2142/121918

Chain of custody

source

Harvested from: University of Illinois - Urbana-Champaign
Base URL: www.ideals.illinois.edu/oai-pmh
Last updated: 2026-07-22
Source record: OAI-PMH GetRecord

related terms

citation

Vita, Joshua Alexander. Designing efficient, interpretable, and generalizable machine learning interatomic potentials. Dissertation thesis, University of Illinois at Urbana-Champaign, 2023. https://hdl.handle.net/2142/121918

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