| Machine learning in materials science: Recent progress and emerging applications T Mueller, AG Kusne, R Ramprasad Reviews in computational chemistry 29, 186-273, 2016 | 501 | 2016 |
| Machine learning modeling of superconducting critical temperature V Stanev, C Oses, AG Kusne, E Rodriguez, J Paglione, S Curtarolo, ... npj Computational Materials 4 (1), 29, 2018 | 444 | 2018 |
| 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 | 333 | 2020 |
| On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets AG Kusne, T Gao, A Mehta, L Ke, MC Nguyen, KM Ho, V Antropov, ... Scientific reports 4 (1), 6367, 2014 | 313 | 2014 |
| Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies ML Green, CL Choi, JR Hattrick-Simpers, AM Joshi, I Takeuchi, SC Barron, ... Applied Physics Reviews 4 (1), 2017 | 312 | 2017 |
| 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 | 303 | 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 | 268 | 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 | 263 | 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 | 230 | 2021 |
| Inkjet printed chemical sensor array based on polythiophene conductive polymers B Li, S Santhanam, L Schultz, M Jeffries-El, MC Iovu, G Sauvé, J Cooper, ... Sensors and Actuators B: Chemical 123 (2), 651-660, 2007 | 228 | 2007 |
| Volatile organic compound detection using nanostructured copolymers B Li, G Sauvé, MC Iovu, M Jeffries-El, R Zhang, J Cooper, S Santhanam, ... Nano Letters 6 (8), 1598-1602, 2006 | 227 | 2006 |
| Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics RK Vasudevan, K Choudhary, A Mehta, R Smith, G Kusne, F Tavazza, ... MRS communications 9 (3), 821-838, 2019 | 156 | 2019 |
| Machine-learning guided discovery of a new thermoelectric material Y Iwasaki, I Takeuchi, V Stanev, AG Kusne, M Ishida, A Kirihara, K Ihara, ... Scientific reports 9 (1), 2751, 2019 | 127 | 2019 |
| Perspective: composition–structure–property mapping in high-throughput experiments: turning data into knowledge JR Hattrick-Simpers, JM Gregoire, AG Kusne APL Materials 4 (5), 2016 | 121 | 2016 |
| Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries Y Iwasaki, AG Kusne, I Takeuchi npj Computational Materials 3 (1), 1-9, 2017 | 102 | 2017 |
| Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering V Stanev, VV Vesselinov, AG Kusne, G Antoszewski, I Takeuchi, ... npj Computational Materials 4 (1), 43, 2018 | 101 | 2018 |
| High-throughput determination of structural phase diagram and constituent phases using GRENDEL AG Kusne, D Keller, A Anderson, A Zaban, I Takeuchi Nanotechnology 26 (44), 444002, 2015 | 92 | 2015 |
| 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 | 77 | 2020 |
| Artificial intelligence for search and discovery of quantum materials V Stanev, K Choudhary, AG Kusne, J Paglione, I Takeuchi Communications Materials 2 (1), 105, 2021 | 56 | 2021 |
| CRYSPNet: Crystal structure predictions via neural networks H Liang, V Stanev, AG Kusne, I Takeuchi Physical Review Materials 4 (12), 123802, 2020 | 52 | 2020 |
