Chiho Kim
Cited by
Cited by
Machine learning in materials informatics: recent applications and prospects
R Ramprasad, R Batra, G Pilania, A Mannodi-Kanakkithodi, C Kim
npj Computational Materials 3 (1), 54, 2017
Polymer genome: a data-powered polymer informatics platform for property predictions
C Kim, A Chandrasekaran, TD Huan, D Das, R Ramprasad
The Journal of Physical Chemistry C 122 (31), 17575-17585, 2018
Solving the electronic structure problem with machine learning
A Chandrasekaran, D Kamal, R Batra, C Kim, L Chen, R Ramprasad
npj Computational Materials 5 (1), 22, 2019
From organized high-throughput data to phenomenological theory using machine learning: the example of dielectric breakdown
C Kim, G Pilania, R Ramprasad
Chemistry of Materials 28 (5), 1304-1311, 2016
Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites
C Kim, G Pilania, R Ramprasad
The Journal of Physical Chemistry C 120 (27), 14575-14580, 2016
A polymer dataset for accelerated property prediction and design
TD Huan, A Mannodi-Kanakkithodi, C Kim, V Sharma, G Pilania, ...
Scientific data 3 (1), 1-10, 2016
Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond
A Mannodi-Kanakkithodi, A Chandrasekaran, C Kim, TD Huan, G Pilania, ...
Materials Today 21 (7), 785-796, 2018
Finding new perovskite halides via machine learning
G Pilania, PV Balachandran, C Kim, T Lookman
Frontiers in Materials 3, 19, 2016
A hybrid organic-inorganic perovskite dataset
C Kim, TD Huan, S Krishnan, R Ramprasad
Scientific data 4 (1), 1-11, 2017
Polymer informatics: Current status and critical next steps
L Chen, G Pilania, R Batra, TD Huan, C Kim, C Kuenneth, R Ramprasad
Materials Science and Engineering: R: Reports 144, 100595, 2021
Critical assessment of the Hildebrand and Hansen solubility parameters for polymers
S Venkatram, C Kim, A Chandrasekaran, R Ramprasad
Journal of chemical information and modeling 59 (10), 4188-4194, 2019
Machine-learning predictions of polymer properties with Polymer Genome
H Doan Tran, C Kim, L Chen, A Chandrasekaran, R Batra, S Venkatram, ...
Journal of Applied Physics 128 (17), 2020
Polymer design using genetic algorithm and machine learning
C Kim, R Batra, L Chen, H Tran, R Ramprasad
Computational Materials Science 186, 110067, 2021
Electrochemical stability window of polymeric electrolytes
L Chen, S Venkatram, C Kim, R Batra, A Chandrasekaran, R Ramprasad
Chemistry of Materials 31 (12), 4598-4604, 2019
Machine learning models for the lattice thermal conductivity prediction of inorganic materials
L Chen, H Tran, R Batra, C Kim, R Ramprasad
Computational Materials Science 170, 109155, 2019
Active-learning and materials design: the example of high glass transition temperature polymers
C Kim, A Chandrasekaran, A Jha, R Ramprasad
Mrs Communications 9 (3), 860-866, 2019
Frequency-dependent dielectric constant prediction of polymers using machine learning
L Chen, C Kim, R Batra, JP Lightstone, C Wu, Z Li, AA Deshmukh, ...
npj Computational Materials 6 (1), 61, 2020
Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
A Jha, A Chandrasekaran, C Kim, R Ramprasad
Modelling and Simulation in Materials Science and Engineering 27 (2), 024002, 2019
A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap
A Patra, R Batra, A Chandrasekaran, C Kim, TD Huan, R Ramprasad
Computational Materials Science 172, 109286, 2020
Polymer informatics with multi-task learning
C Kuenneth, AC Rajan, H Tran, L Chen, C Kim, R Ramprasad
Patterns 2 (4), 2021
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