Brian L. DeCost
Titel
Zitiert von
Zitiert von
Jahr
A computer vision approach for automated analysis and classification of microstructural image data
BL DeCost, EA Holm
Computational materials science 110, 126-133, 2015
1742015
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
1112017
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
672017
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
662019
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
642019
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), 1-9, 2019
622019
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
572018
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
522017
Characterizing powder materials using keypoint-based computer vision methods
BL DeCost, EA Holm
Computational Materials Science 126, 438-445, 2017
292017
Uhcsdb: ultrahigh carbon steel micrograph database
BL DeCost, MD Hecht, T Francis, BA Webler, YN Picard, EA Holm
Integrating Materials and Manufacturing Innovation 6 (2), 197-205, 2017
242017
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
182017
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ARH Walker, ...
npj Computational Materials 6 (1), 1-13, 2020
132020
Phenomenology of abnormal grain growth in systems with nonuniform grain boundary mobility
BL DeCost, EA Holm
Metallurgical and Materials Transactions A 48 (6), 2771-2780, 2017
132017
A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures
BL DeCost, EA Holm
Data in brief 9, 727-731, 2016
112016
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), 1-11, 2020
92020
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
62020
An inter-laboratory study of Zn–Sn–Ti–O thin films using high-throughput experimental methods
JR Hattrick-Simpers, A Zakutayev, SC Barron, ZT Trautt, N Nguyen, ...
ACS combinatorial science 21 (5), 350-361, 2019
62019
Ultrahigh carbon steel micrographs
MD Hecht, BL DeCost, T Francis, EA Holm, YN Picard, BA Webler
42017
Accelerating photovoltaic materials development via high-throughput experiments and machine-learning-assisted diagnosis
S Sun, NTP Hartono, ZD Ren, F Oviedo, AM Buscemi, M Layurova, ...
arXiv preprint arXiv:1812.01025, 2018
32018
JARVIS: An Integrated Infrastructure for Data-driven Materials Design
K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ARH Walker, ...
arXiv preprint arXiv:2007.01831, 2020
22020
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