| Combinatorial search of thermoelastic shape-memory alloys with extremely small hysteresis width J Cui, YS Chu, OO Famodu, Y Furuya, J Hattrick-Simpers, RD James, ... Nature materials 5 (4), 286-290, 2006 | 735 | 2006 |
| Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments F Ren, L Ward, T Williams, KJ Laws, C Wolverton, J Hattrick-Simpers, ... Science advances 4 (4), eaaq1566, 2018 | 490 | 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 | 306 | 2020 |
| 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 | 303 | 2017 |
| Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials ML Green, I Takeuchi, JR Hattrick-Simpers Journal of Applied Physics 113 (23), 2013 | 290 | 2013 |
| 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 | 287 | 2020 |
| Giant magnetostriction in annealed Co1−xFex thin-films D Hunter, W Osborn, K Wang, N Kazantseva, J Hattrick-Simpers, ... Nature communications 2 (1), 518, 2011 | 253 | 2011 |
| Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, ... Molecular Systems Design & Engineering 3 (5), 819-825, 2018 | 245 | 2018 |
| 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 | 215 | 2021 |
| 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 | 166* | 2019 |
| Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis CJ Long, J Hattrick-Simpers, M Murakami, RC Srivastava, I Takeuchi, ... Review of Scientific Instruments 78 (7), 2007 | 140 | 2007 |
| Exploration of artificial multiferroic thin-film heterostructures using composition spreads KS Chang, MA Aronova, CL Lin, M Murakami, MH Yu, J Hattrick-Simpers, ... Applied Physics Letters 84 (16), 3091-3093, 2004 | 127 | 2004 |
| Tunable multiferroic properties in nanocomposite PbTiO–CoFeO epitaxial thin films M Murakami, KS Chang, MA Aronova, CL Lin, HY Ming, JH Simpers, ... Applied Physics Letters 87, 112901, 2005 | 123 | 2005 |
| 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 | 117 | 2016 |
| 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 | 73 | 2020 |
| Multimode quantitative scanning microwave microscopy of in situ grown epitaxial BaSrTiO composition spreads KS Chang, M Aronova, O Famodu, I Takeuchi, SE Lofland, ... Applied Physics Letters 79, 4411, 2001 | 71 | 2001 |
| Data management and visualization of x-ray diffraction spectra from thin film ternary composition spreads I Takeuchi, CJ Long, OO Famodu, M Murakami, J Hattrick-Simpers, ... Review of Scientific Instruments 76 (6), 2005 | 61 | 2005 |
| Combinatorial investigation of magnetostriction in Fe–Ga and Fe–Ga–Al JR Hattrick-Simpers, D Hunter, CM Craciunescu, KS Jang, M Murakami, ... Applied Physics Letters 93 (10), 2008 | 54 | 2008 |
| A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials JR Hattrick-Simpers, K Choudhary, C Corgnale Molecular Systems Design & Engineering 3 (3), 509-517, 2018 | 49 | 2018 |
| Semi-supervised approach to phase identification from combinatorial sample diffraction patterns JK Bunn, J Hu, JR Hattrick-Simpers Jom 68, 2116-2125, 2016 | 49 | 2016 |
Ichiro TakeuchiUniversity of MarylandVerified email at umd.edu
