| 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 | 576 | 2022 |
| Atomistic line graph neural network for improved materials property predictions K Choudhary, B DeCost npj Computational Materials 7 (1), 185, 2021 | 363 | 2021 |
| 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 | 362 | 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 | 331 | 2020 |
| A computer vision approach for automated analysis and classification of microstructural image data BL DeCost, EA Holm Computational materials science 110, 126-133, 2015 | 309 | 2015 |
| 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 | 278 | 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 | 273 | 2019 |
| 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 | 242 | 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 | 235 | 2017 |
| 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 | 222 | 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 | 173 | 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 | 154 | 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 | 93 | 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 | 90 | 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 | 82 | 2020 |
| Characterizing powder materials using keypoint-based computer vision methods BL DeCost, EA Holm Computational Materials Science 126, 438-445, 2017 | 69 | 2017 |
| Unified graph neural network force-field for the periodic table: solid state applications K Choudhary, B DeCost, L Major, K Butler, J Thiyagalingam, F Tavazza Digital Discovery 2 (2), 346-355, 2023 | 62 | 2023 |
| A critical examination of robustness and generalizability of machine learning prediction of materials properties K Li, B DeCost, K Choudhary, M Greenwood, J Hattrick-Simpers npj Computational Materials 9 (1), 55, 2023 | 56 | 2023 |
| Exploiting redundancy in large materials datasets for efficient machine learning with less data K Li, D Persaud, K Choudhary, B DeCost, M Greenwood, ... Nature Communications 14 (1), 7283, 2023 | 52 | 2023 |
| Recent advances and applications of deep learning methods in materials science. npj Computational Materials, 8 (1): 59 K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... URL: https://doi. org/10.1038/s41524-022-00734-6, doi 10, 2022 | 52 | 2022 |
