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Bryce Meredig

University of California Berkeley

H指数:32

被引数:9,492

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A multiobjective closed-loop approach towards autonomous discovery of electrocatalysts for nitrogen reduction

DIGITAL DISCOVERY

IF5.6

2024-01-014

OA

Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD

PHYSICAL REVIEW MATERIALS

IF3.4

2023-05-3017

OA

Quantifying the performance of machine learning models in materials discovery

DIGITAL DISCOVERY

IF5.6

2023-01-0116

OA

By how much can closed-loop frameworks accelerate computational materials discovery?

DIGITAL DISCOVERY

IF5.6

2023-01-015

OA

Interpretable models for extrapolation in scientific machine learning

DIGITAL DISCOVERY

IF5.6

2023-01-0127

OA

AutoMat: Automated materials discovery for electrochemical systems

IF4.9

2022-12-228

PRE

High-throughput characterization of Lu-doped zirconia

SOLID STATE IONICS

IF3.3

2021-10-012

OA

Utilization of machine learning to accelerate colloidal synthesis and discovery

JOURNAL OF CHEMICAL PHYSICS

IF3.1

2021-06-0820

OAAI

Machine-learned metrics for predicting the likelihood of success in materials discovery

NPJ COMPUTATIONAL MATERIALS

IF11.9

2020-08-2620

OAAI

Five High-Impact Research Areas in Machine Learning for Materials Science

CHEMISTRY OF MATERIALS

IF7

2019-12-1050

OAAI

Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

JOURNAL OF THE AMERICAN CERAMIC SOCIETY

IF3.8

2019-07-3021

PREAI

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

INORGANIC CHEMISTRY

IF4.7

2019-07-0313

PREAI

The 2019 materials by design roadmap

2019材料的设计路线图

JOURNAL OF PHYSICS D-APPLIED PHYSICS

IF3.2

2018-10-24265

OAAI

Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy; Mitas, Lubos; Curtarolo, Stefano; Jain, Anubhav; Fornari, Marco; Marzari, Nicola; Takeuchi, Ichiro; Green, Martin L.; Kanatzidis, Mercouri; Toney, Mike F.; Butenko, Sergiy; Meredig, Bryce; Lany, Stephan; Kattner, Ursula; Davydov, Albert; Toberer, Eric S.; Stevanovic, Vladan; Walsh, Aron; Park, Nam-Gyu; Aspuru-Guzik, Alan; Tabor, Daniel P.; Nelson, Jenny; Murphy, James; Setlur, Anant; Gregoire, John; Li, Hong; Xiao, Ruijuan; Ludwig, Alfred; Martin, Lane W.; Rappe, Andrew M.; Wei, Su-Huai; Perkins, John

Strategies for accelerating the adoption of materials informatics

IF4.9

2018-09-1032

PREAI

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

机器学习能识别下一个高温超导体吗？检查材料发现的外推性能

MOLECULAR SYSTEMS DESIGN & ENGINEERING

IF3.2

2018-01-01188

OAAI

Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

工业材料信息学: 分析大规模数据以解决研发，制造和供应链中的应用问题

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE

IF13.4

2017-06-0126

PREAI

Robust FCC solute diffusion predictions from ab-initio machine learning methods

COMPUTATIONAL MATERIALS SCIENCE

IF3.3

2017-06-0162

OAAI

Revisiting the revised Ag-Pt phase diagram

ACTA MATERIALIA

IF9.3

2017-02-0135

OAAI

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

CHEMISTRY OF MATERIALS

IF7

2016-10-04289

OAAI

Beyond bulk single crystals: A data format for all materials structure-property-processing relationships

IF4.9

2016-08-0232

PREAI