Chandramouli Nyshadham
Chandramouli Nyshadham
Kebotix Inc.
Verified email at kebotix.com
Title
Cited by
Cited by
Year
A computational high-throughput search for new ternary superalloys
C Nyshadham, C Oses, JE Hansen, I Takeuchi, S Curtarolo, GLW Hart
Acta Materialia 122, 438-447, 2017
472017
Machine-learned multi-system surrogate models for materials prediction
C Nyshadham, M Rupp, B Bekker, AV Shapeev, T Mueller, ...
npj Computational Materials 5 (1), 1-6, 2019
242019
Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications
A Hunter, BA Moore, M Mudunuru, V Chau, R Tchoua, C Nyshadham, ...
Computational Materials Science 157, 87-98, 2019
16*2019
The AFLOW fleet for materials discovery
C Toher, C Oses, D Hicks, E Gossett, F Rose, P Nath, D Usanmaz, ...
Handbook of Materials Modeling: Methods: Theory and Modeling, 1785-1812, 2020
102020
Superalloys compositions including at least one ternary intermetallic compound and applications thereof
C Nyshadham, JE Hansen, GLW Hart
US Patent App. 15/765,952, 2019
12019
Insights on materials space
C Nyshadham, K Lincoln, G Hart
APS 2019, K18. 008, 2019
12019
DFT-45B---a fertile soil (data) for your seeds (machine learning algorithms)
C Nyshadham, C Kreisbeck, G Hart
Bulletin of the American Physical Society 65, 2020
2020
Predicting γ′-Phase Stability in Co-Based Superalloys
H Oliver, C Nyshadham, B Bekker, G Hart
Bulletin of the American Physical Society 65, 2020
2020
Accelerating superalloy discovery using moment tensor potentials
H Oliver, B Bekker, C Nyshadham, CA Leon Chinchay, G Hart
APS 2019, X19. 003, 2019
2019
Exploring Materials Space with Machine Learning
B Bekker, H Oliver, C Nyshadham, A Shapeev, G Hart
APS 2019, X19. 005, 2019
2019
Materials prediction using high-throughput and machine learning techniques
C Nyshadham
Brigham Young University, 2019
2019
Studying Cobalt Based Superalloys with Machine Learning
B Bekker, C Nyshadham, G Hart
Bulletin of the American Physical Society 63, 2018
2018
General machine learning models for materials prediction
C Nyshadham, M Rupp, B Bekker, A Shapeev, T Mueller, C Rosenbrock, ...
Bulletin of the American Physical Society 63, 2018
2018
Restricted Boltzmann Machines for Learning Multiple Observables
P Hamilton, C Nyshadham, G Hart
Bulletin of the American Physical Society 63, 2018
2018
Investigating superalloys using machine learning
H Oliver, C Nyshadham, G Hart
Bulletin of the American Physical Society 63, 2018
2018
The best features to know when you first meet a material
K Lincoln, C Nyshadham, G Hart
Bulletin of the American Physical Society 63, 2018
2018
3D Scattering Transform Representation of Materials: From Molecules to Crystals
A Nguyen, C Nyshadham, C Rosenbrock, G Hart
APS 2018, R12. 008, 2018
2018
Materials prediction using machine learning: comparing MBTR, MTP and deep learning
C Nyshadham, W Morgan, B Bekker, G Hart
APS 2018, E34. 011, 2018
2018
Machine Learning for Materials Discovery
B Bekker, C Nyshadham, G Hart
Bulletin of the American Physical Society 62, 2017
2017
Invariance to deformations: A new representation for materials space
C Nyshadham, GLW Hart
APS 2017, A1. 008, 2017
2017
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