| The chemical and structural origin of efficient p-type doping in P3HT DT Duong, C Wang, E Antono, MF Toney, A Salleo Organic Electronics 14 (5), 1330-1336, 2013 | 346 | 2013 |
| High-throughput machine-learning-driven synthesis of full-Heusler compounds AO Oliynyk, E Antono, TD Sparks, L Ghadbeigi, MW Gaultois, B Meredig, ... Chemistry of Materials 28 (20), 7324-7331, 2016 | 335 | 2016 |
| 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 | 219 | 2018 |
| High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates J Ling, M Hutchinson, E Antono, S Paradiso, B Meredig Integrating Materials and Manufacturing Innovation 6, 207-217, 2017 | 198 | 2017 |
| Overcoming data scarcity with transfer learning ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig arXiv preprint arXiv:1711.05099, 2017 | 98 | 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 | 86 | 2017 |
| Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization Z Del Rosario, M Rupp, Y Kim, E Antono, J Ling The Journal of Chemical Physics 153 (2), 2020 | 55 | 2020 |
| Machine-learned metrics for predicting the likelihood of success in materials discovery Y Kim, E Kim, E Antono, B Meredig, J Ling npj Computational Materials 6 (1), 131, 2020 | 34 | 2020 |
| Machine-learning guided quantum chemical and molecular dynamics calculations to design novel hole-conducting organic materials E Antono, NN Matsuzawa, J Ling, JE Saal, H Arai, M Sasago, E Fujii The Journal of Physical Chemistry A 124 (40), 8330-8340, 2020 | 32 | 2020 |
| Unsupervised data mining in nanoscale X-ray spectro-microscopic study of NdFeB magnet X Duan, F Yang, E Antono, W Yang, P Pianetta, S Ermon, A Mehta, Y Liu Scientific reports 6 (1), 34406, 2016 | 29 | 2016 |
| Machine learning based approaches to accelerate energy materials discovery and optimization D Krishnamurthy, H Weiland, A Barati Farimani, E Antono, J Green, ... ACS energy letters 4 (1), 187-191, 2018 | 26 | 2018 |
| Machine learning for alloy composition and process optimization J Ling, E Antono, S Bajaj, S Paradiso, M Hutchinson, B Meredig, ... Turbo Expo: Power for Land, Sea, and Air 51128, V006T24A005, 2018 | 25 | 2018 |
| Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD VI Hegde, CKH Borg, Z del Rosario, Y Kim, M Hutchinson, E Antono, ... Physical Review Materials 7 (5), 053805, 2023 | 17* | 2023 |
| Single-Crystal Automated Refinement (SCAR): A data-driven method for determining inorganic structures G Viswanathan, AO Oliynyk, E Antono, J Ling, B Meredig, J Brgoch Inorganic chemistry 58 (14), 9004-9015, 2019 | 13 | 2019 |
| Design space visualization for guiding investments in biodegradable and sustainably sourced materials JS Peerless, E Sevgen, SD Edkins, J Koeller, E Kim, Y Kim, A Garg, ... MRS Communications 10 (1), 18-24, 2020 | 6 | 2020 |
| Solving industrial materials problems by using machine learning across diverse computational and experimental data M Hutchinson, E Antono, B Gibbons, S Paradiso, J Ling, B Meredig APS March Meeting Abstracts 2018, K32. 002, 2018 | 4 | 2018 |
| Predictive design space metrics for materials development Y Kim, EMT Antono, ES Kim, BW Meredig, JB Ling US Patent 10,657,300, 2020 | 3 | 2020 |
| High-throughput characterization of Lu-doped zirconia R Huang, E Antono, B Meredig, GJ Mulholland, TC Davenport, SM Haile Solid State Ionics 368, 115698, 2021 | 2 | 2021 |
| Assessing the frontier: active learning, model accuracy, and multi-objective materials discovery and optimization Z del Rosario, M Rupp, Y Kim, E Antono, J Ling arXiv preprint arXiv:1911.03224, 2019 | 2 | 2019 |
| Product design and materials development integration using a machine learning generated capability map JB Ling, AWA Van Grootel, JS Koeller, JS Peerless, EMT Antono, ... US Patent 11,004,037, 2021 | 1 | 2021 |
