Erin Antono
Erin Antono
Citrine Informatics
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Cited by
Cited by
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
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
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
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
Overcoming data scarcity with transfer learning
ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig
arXiv preprint arXiv:1711.05099, 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
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
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
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
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
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
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
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
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
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
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
Predictive design space metrics for materials development
Y Kim, EMT Antono, ES Kim, BW Meredig, JB Ling
US Patent 10,657,300, 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
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
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
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