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Brian DeCost

Materials Scientist

National Institute of Standards and Technology

United States

Research Interests

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Professors in United States Professors in Condensed Matter Physics Condensed Matter Physics professors in United States

Articles (20)

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2026

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2025

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2025

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2025

Generalization performance of machine learning models: (upper panel) generalization from small ordered to large disordered structures (SQS); (lower panel) generalization from low-order to high-order systems.

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2024

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2024

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2024

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2024

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2024

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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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2023

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2023

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2023

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2023

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2023

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2023

Collaborators (14)

Kamal Choudhary

National Institute of Standards and Technology

UNITED STATES

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Adji Bousso Dieng

Assistant Professor

Princeton University

UNITED STATES

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Ankit Agrawal

Research Professor

Northwestern University

UNITED STATES

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Vahid Attari

Research Assistant Professor

Texas A&M University

UNITED STATES

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RUNZE ZHANG

University of Toronto

CANADA

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Kangming Li

at King Abdullah University of Science and Technology

King Abdullah University of Science and Technology

SAUDI ARABIA

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Jason Hattrick-Simpers

Professor

University of Toronto

CANADA

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Daniel Wines

National Institute of Standards and Technology

UNITED STATES

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Debashish Sur

University of Virginia

UNITED STATES

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Ghanshyam Pilania

Staff Scientist

Los Alamos National Laboratory

UNITED STATES

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Vishu Gupta

Princeton University

UNITED STATES

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Arun Kumar Mannodi-Kanakkithodi

Assistant Professor

Purdue University

UNITED STATES

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Anatoly I. Frenkel

Stony Brook University

UNITED STATES

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Carelyn Campbell

National Institute of Standards and Technology

UNITED STATES

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Brian DeCost

Research Interests

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Articles (20)

Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry

Influence of short-range order on precipitate orientation relationships in aluminum containing FCC high entropy alloys

Probing out-of-distribution generalization in machine learning for materials

Intrinsic direct air capture

Efficient first principles based modeling <i>via</i> machine learning: from simple representations to high entropy materials

Accelerating defect predictions in semiconductors using graph neural networks

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

High-throughput aqueous passivation behavior of thin-film vs. bulk multi-principal element alloys in sulfuric acid

A reproducibility study of atomistic line graph neural networks for materials property prediction

Exploiting redundancy in large materials datasets for efficient machine learning with less data (vol 14, 7283, 2023)

Flexible formulation of value for experiment interpretation and design

Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning

A critical examination of robustness and generalizability of machine learning prediction of materials properties

AtomVision: A Machine Vision Library for Atomistic Images

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

Editors’ Choice—AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy

A call for caution in the era of AI-accelerated materials science

Exploiting redundancy in large materials datasets for efficient machine learning with less data

Perspective Why is EXAFS for complex concentrated alloys so hard? Challenges and opportunities for measuring ordering with X-ray absorption spectroscopy

Understanding and leveraging short-range order in compositionally complex alloys

Collaborators (14)

Kamal Choudhary

Adji Bousso Dieng

Ankit Agrawal

Vahid Attari

RUNZE ZHANG

Kangming Li

Jason Hattrick-Simpers

Daniel Wines

Debashish Sur

Ghanshyam Pilania

Vishu Gupta

Arun Kumar Mannodi-Kanakkithodi

Anatoly I. Frenkel

Carelyn Campbell

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