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Erin Antono

Observed name: Antono, Erin
ORCID: 0009-0004-2485-1579
Observed ORCID: 0009-0004-2485-1579
OpenAlex ID: A5087591669
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
2016 · Anton O. Oliynyk, Erin Antono, et al. · Chemistry of Materials · 384
The chemical and structural origin of efficient p-type doping in P3HT
2013 · Duc T. Duong, Chenchen Wang, et al. · Organic Electronics · 335
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
2018 · Bryce Meredig, Erin Antono, et al. · Molecular Systems Design & Engineering · 284
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Overcoming data scarcity with transfer learning
2017 · Hutchinson, Maxwell L., Antono, Erin, et al. · arXiv (Cornell University) · 115
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Building data-driven models with microstructural images: Generalization and interpretability
2017 · Julia Ling, Maxwell Hutchinson, et al. · Materials Discovery · 98
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Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization
2020 · Zachary del Rosario, Matthias Rupp, et al. · The Journal of Chemical Physics · 53
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Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD
2023 · Vinay I. Hegde, Christopher K. H. Borg, et al. · Physical Review Materials · 44
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Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials
2020 · Erin Antono, Nobuyuki N. Matsuzawa, et al. · The Journal of Physical Chemistry A · 41
Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization
2018 · Dilip Krishnamurthy, Hasso Weiland, et al. · ACS Energy Letters · 41
Machine-learned metrics for predicting the likelihood of success in materials discovery
2020 · Yoolhee Kim, Edward Kim, et al. · npj Computational Materials · 37
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Machine Learning for Alloy Composition and Process Optimization
2018 · Julia Ling, Erin Antono, et al. · ASME Turbo Expo: Turbomachinery Technical Conference and Exposition (GT) · 33
Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet
2016 · Xiaoyue Duan, Feifei Yang, et al. · Scientific Reports · 28
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Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures
2019 · Gayatri Viswanathan, Anton O. Oliynyk, et al. · Inorganic Chemistry · 16
Design space visualization for guiding investments in biodegradable and sustainably sourced materials
2020 · James S. Peerless, Emre Sevgen, et al. · MRS Communications · 7
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Solving industrial materials problems by using machine learning across diverse computational and experimental data
2018 · Maxwell Hutchinson, Erin Antono, et al. · Bulletin of the American Physical Society · 4
High-throughput characterization of Lu-doped zirconia
2021 · Ruiyun Huang, Erin Antono, et al. · Solid State Ionics · 3
Machine Learning Techniques for Predicting Properties of Formulations
2024 · Maxwell Hutchinson, Erin Antono, et al. · CRC Press eBooks
CSD 1879789: Experimental Crystal Structure Determination
2019 · Viswanathan, Gayatri, Oliynyk, Anton O., et al. · The Cambridge Structural Database
CSD 1879791: Experimental Crystal Structure Determination
2019 · Viswanathan, Gayatri, Oliynyk, Anton O., et al. · The Cambridge Structural Database
CSD 1879788: Experimental Crystal Structure Determination
2019 · Viswanathan, Gayatri, Oliynyk, Anton O., et al. · The Cambridge Structural Database
CSD 1879790: Experimental Crystal Structure Determination
2019 · Viswanathan, Gayatri, Oliynyk, Anton O., et al. · The Cambridge Structural Database
Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures
2019 · Gayatri Viswanathan, Anton Oliynyk, et al. · ChemRxiv
Works count: 22
Citation count: 1,523
H-index: 13
I10-index: 13
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