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Brian DeCost

national institute of standards and technology

H指数:24

被引数:4,735

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Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry

电化学材料加速平台：自主电化学平台

npj Materials Degradation

IF7.6

2026-06-040

OAAI

Intrinsic direct air capture

固有直接空气捕集

IF7.4

2025-09-110

OAAI

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

短程有序对含FCC高熵铝合金中沉淀相取向关系的影响

IF4.8

2025-05-230

PRE

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

CORROSION SCIENCE

IF8.5

2024-08-011

OAAI

Flexible formulation of value for experiment interpretation and design

IF17.5

2024-02-011

PRE

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

NATURE COMMUNICATIONS

IF15.7

2024-01-040

OAAI

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

NPJ COMPUTATIONAL MATERIALS

IF11.9

2024-01-0211

OA

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

通过机器学习进行基于第一原理的高效建模: 从简单表示到高熵材料

JOURNAL OF MATERIALS CHEMISTRY A

IF9.5

2024-01-012

OA

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

DIGITAL DISCOVERY

IF5.6

2024-01-010

OA

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

DIGITAL DISCOVERY

IF5.6

2024-01-011

OA

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

IF17.5

2023-12-012

PRE

Understanding and leveraging short-range order in compositionally complex alloys

IF4.9

2023-11-205

PRE

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

NATURE COMMUNICATIONS

IF15.7

2023-11-1034

OAAI

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

IF17.5

2023-11-017

OAAI

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

APPLIED PHYSICS REVIEWS

IF11.6

2023-10-189

OA

Editors' Choice-AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy

JOURNAL OF THE ELECTROCHEMICAL SOCIETY

IF3.3

2023-08-099

OA

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

NPJ COMPUTATIONAL MATERIALS

IF11.9

2023-04-0739

OAAI

AtomVision: A Machine Vision Library for Atomistic Images

JOURNAL OF CHEMICAL INFORMATION AND MODELING

IF5.3

2023-03-019

OA

Unified graph neural network force-field for the periodic table: solid state applications

元素周期表的统一图神经网络力场: 固态应用

DIGITAL DISCOVERY

IF5.6

2023-01-0142

OA

Atomistic Line Graph Neural Network for improved materials property predictions (vol 7, 185, 2021)

NPJ COMPUTATIONAL MATERIALS

IF11.9

2022-10-282

OA