Edward Kim
Edward Kim
Citrine Informatics
Verified email at citrine.io
Cited by
Cited by
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
Machine-learned and codified synthesis parameters of oxide materials
E Kim, K Huang, A Tomala, S Matthews, E Strubell, A Saunders, ...
Scientific data 4, 170127, 2017
Virtual screening of inorganic materials synthesis parameters with deep learning
E Kim, K Huang, S Jegelka, E Olivetti
npj Computational Materials 3 (1), 1-9, 2017
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
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
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
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
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
Distilling a materials synthesis ontology
E Kim, K Huang, O Kononova, G Ceder, E Olivetti
Matter 1 (1), 8-12, 2019
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
E Kim, Z Jensen, A Grootel, K Huang, M Staib, S Mysore, HS Chang, ...
Journal of Chemical Information and Modeling, 2018
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
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
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
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
Data-mining natural language materials syntheses
ES Kim
Massachusetts Institute of Technology, 2019
Text and data mining for material synthesis
E Olivetti, E Kim
APS Meeting Abstracts, 2019
Germanene-like defects in Reverse Monte Carlo model of amorphous germanium revealed through new visualization method
A Rahemtulla, B Tomberli, E Kim, S Roorda, S Kycia
APS Meeting Abstracts, 2016
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Articles 1–17