Prati
Edward Kim
Edward Kim
Cohere AI
Potvrđena adresa e-pošte na cohere.com - Početna stranica
Naslov
Citirano
Citirano
Godina
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
4122017
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
2122019
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
1822020
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
1672017
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
1542017
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
1132020
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
1072019
Distilling a materials synthesis ontology
E Kim, K Huang, O Kononova, G Ceder, E Olivetti
Matter 1 (1), 8-12, 2019
452019
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
442017
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
342019
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
112021
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
92015
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
62020
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
52015
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
52013
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
42021
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
42013
Predictive design space metrics for materials development
Y Kim, EMT Antono, ES Kim, BW Meredig, JB Ling
US Patent 10,657,300, 2020
32020
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
22023
Lessons in Reproducibility: Insights from NLP Studies in Materials Science
X Lei, E Kim, V Baibakova, S Sun
arXiv preprint arXiv:2307.15759, 2023
12023
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