| Materials synthesis insights from scientific literature via text extraction and machine learning E Kim, K Huang, A Saunders, A McCallum, G Ceder, E Olivetti Chemistry of Materials 29 (21), 9436-9444, 2017 | 457 | 2017 |
| Data-driven materials research enabled by natural language processing and information extraction EA Olivetti, JM Cole, E Kim, O Kononova, G Ceder, TYJ Han, ... Applied Physics Reviews 7 (4), 2020 | 252 | 2020 |
| A machine learning approach to zeolite synthesis enabled by automatic literature data extraction Z Jensen, E Kim, S Kwon, TZH Gani, Y Román-Leshkov, M Moliner, ... ACS central science 5 (5), 892-899, 2019 | 241 | 2019 |
| Machine-learned and codified synthesis parameters of oxide materials E Kim, K Huang, A Tomala, S Matthews, E Strubell, A Saunders, ... Scientific data 4 (1), 1-9, 2017 | 180 | 2017 |
| Virtual screening of inorganic materials synthesis parameters with deep learning E Kim, K Huang, S Jegelka, E Olivetti npj Computational Materials 3 (1), 53, 2017 | 144 | 2017 |
| Inorganic materials synthesis planning with literature-trained neural networks E Kim, Z Jensen, A van Grootel, K Huang, M Staib, S Mysore, HS Chang, ... Journal of chemical information and modeling 60 (3), 1194-1201, 2020 | 134 | 2020 |
| The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures S Mysore, Z Jensen, E Kim, K Huang, HS Chang, E Strubell, J Flanigan, ... arXiv preprint arXiv:1905.06939, 2019 | 126 | 2019 |
| Distilling a materials synthesis ontology E Kim, K Huang, O Kononova, G Ceder, E Olivetti Matter 1 (1), 8-12, 2019 | 50 | 2019 |
| Automatically extracting action graphs from materials science synthesis procedures S Mysore, E Kim, E Strubell, A Liu, HS Chang, S Kompella, K Huang, ... arXiv preprint arXiv:1711.06872, 2017 | 46 | 2017 |
| Machine-learned metrics for predicting the likelihood of success in materials discovery Y Kim, E Kim, E Antono, B Meredig, J Ling arXiv preprint arXiv:1911.11201, 2019 | 37 | 2019 |
| Elo uncovered: Robustness and best practices in language model evaluation M Boubdir, E Kim, B Ermis, S Hooker, M Fadaee arXiv preprint arXiv:2311.17295, 2023 | 24 | 2023 |
| Using machine learning to explore formulations recipes with new ingredients ML Hutchinson, ES Kim, RM Latture, SP Paradiso, JB Ling US Patent 10,984,145, 2021 | 12 | 2021 |
| Fabrication and characterization of thin film nickel hydroxide electrodes for micropower applications H Falahati, E Kim, DPJ Barz ACS Applied Materials & Interfaces 7 (23), 12797-12808, 2015 | 9 | 2015 |
| Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation M Boubdir, E Kim, B Ermis, M Fadaee, S Hooker arXiv preprint arXiv:2310.14424, 2023 | 7 | 2023 |
| 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, 1-7, 2020 | 7 | 2020 |
| Toward Predictive Chemical Deformulation Enabled by Deep Generative Neural Networks E Sevgen, E Kim, B Folie, V Rivera, J Koeller, E Rosenthal, A Jacobs, ... Industrial & Engineering Chemistry Research 60 (39), 14176-14184, 2021 | 6 | 2021 |
| Germanene-like defects in amorphous germanium revealed by three-dimensional visualization of high-resolution pair-distribution functions B Tomberli, A Rahemtulla, E Kim, S Roorda, S Kycia Physical Review B 92 (6), 064204, 2015 | 5 | 2015 |
| Multiple scattering Debye-Waller factors for arsenate E Kim, N Chen, Z Arthur, J Warner, GP Demopoulos, JW Rowson, ... Journal of Physics: Conference Series 430 (1), 012086, 2013 | 5 | 2013 |
| XAFS study of arsenical nickel hydroxide N Chen, E Kim, Z Arthur, R Daenzer, J Warner, GP Demopoulos, Y Joly, ... Journal of Physics: Conference Series 430 (1), 012092, 2013 | 4 | 2013 |
| Lessons in Reproducibility: Insights from NLP Studies in Materials Science X Lei, E Kim, V Baibakova, S Sun arXiv preprint arXiv:2307.15759, 2023 | 3 | 2023 |
