Brian L. DeCost
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Recent advances and applications of deep learning methods in materials science
K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ...
npj Computational Materials 8 (1), 59, 2022
A computer vision approach for automated analysis and classification of microstructural image data
BL DeCost, EA Holm
Computational materials science 110, 126-133, 2015
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ...
npj computational materials 6 (1), 173, 2020
On-the-fly closed-loop materials discovery via Bayesian active learning
AG Kusne, H Yu, C Wu, H Zhang, J Hattrick-Simpers, B DeCost, S Sarker, ...
Nature communications 11 (1), 5966, 2020
Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis
S Sun, NTP Hartono, ZD Ren, F Oviedo, AM Buscemi, M Layurova, ...
Joule 3 (6), 1437-1451, 2019
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
F Oviedo, Z Ren, S Sun, C Settens, Z Liu, NTP Hartono, S Ramasamy, ...
npj Computational Materials 5 (1), 60, 2019
Atomistic line graph neural network for improved materials property predictions
K Choudhary, B DeCost
npj Computational Materials 7 (1), 185, 2021
Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures
BL DeCost, T Francis, EA Holm
Acta Materialia 133, 30-40, 2017
Autonomous experimentation systems for materials development: A community perspective
E Stach, B DeCost, AG Kusne, J Hattrick-Simpers, KA Brown, KG Reyes, ...
Matter 4 (9), 2702-2726, 2021
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
BL DeCost, B Lei, T Francis, EA Holm
Microscopy and Microanalysis 25 (1), 21-29, 2019
Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
K Choudhary, B DeCost, F Tavazza
Physical review materials 2 (8), 083801, 2018
Computer vision and machine learning for autonomous characterization of am powder feedstocks
BL DeCost, H Jain, AD Rollett, EA Holm
Jom 69 (3), 456-465, 2017
Building data-driven models with microstructural images: Generalization and interpretability
J Ling, M Hutchinson, E Antono, B DeCost, EA Holm, B Meredig
Materials Discovery 10, 19-28, 2017
UHCSDB: ultrahigh carbon steel micrograph database: tools for exploring large heterogeneous microstructure datasets
BL DeCost, MD Hecht, T Francis, BA Webler, YN Picard, EA Holm
Integrating Materials and Manufacturing Innovation 6, 197-205, 2017
Characterizing powder materials using keypoint-based computer vision methods
BL DeCost, EA Holm
Computational Materials Science 126, 438-445, 2017
Scientific AI in materials science: a path to a sustainable and scalable paradigm
BL DeCost, JR Hattrick-Simpers, Z Trautt, AG Kusne, E Campo, ML Green
Machine learning: science and technology 1 (3), 033001, 2020
A high-throughput structural and electrochemical study of metallic glass formation in Ni–Ti–Al
H Joress, BL DeCost, S Sarker, TM Braun, S Jilani, R Smith, L Ward, ...
ACS combinatorial science 22 (7), 330-338, 2020
Phenomenology of abnormal grain growth in systems with nonuniform grain boundary mobility
BL DeCost, EA Holm
Metallurgical and Materials Transactions A 48, 2771-2780, 2017
Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach
A Menon, C Gupta, KM Perkins, BL DeCost, N Budwal, RT Rios, K Zhang, ...
Molecular Systems Design & Engineering 2 (3), 263-273, 2017
Uncertainty prediction for machine learning models of material properties
F Tavazza, B DeCost, K Choudhary
ACS omega 6 (48), 32431-32440, 2021
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