Chandramouli Nyshadham

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Gus L. W. HartBrigham Young University, Physics and AstronomyVerified email at byu.edu
Brayden BekkerBYU, Brigham Young University, PhysicsVerified email at byu.edu
Conrad RosenbrockBrigham Young UniversityVerified email at byu.edu
Alexander ShapeevSkolkovo Institute of Science and TechnologyVerified email at shapeev.com
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
David WingateAssociate professor of Computer Science, Brigham Young UniversityVerified email at cs.byu.edu
Curtarolo, StefanoDuke UniversityVerified email at nietzsche.mems.duke.edu
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Corey OsesJohns Hopkins UniversityVerified email at jhu.edu
Abigail HunterLos Alamos National LaboratoryVerified email at lanl.gov
Esteban RougierResearch Scientist, Los Alamos National LaboratoryVerified email at lanl.gov
Maruti Kumar MudunuruPacific Northwest National LaboratoryVerified email at pnnl.gov
Viet ChauPosdoctoral Research Fellow, Los Alamos National LaboratoryVerified email at u.northwestern.edu
Roselyne Barreto TchouaDePaul UniversityVerified email at depaul.edu
Hari ViswanathanSenior Scientist, Los Alamos National LaboratoryVerified email at lanl.gov
Gowri SrinivasanLos Alamos National LaboratoryVerified email at lanl.gov
Satish KarraEMSL/PNNLVerified email at pnnl.gov
Ichiro TakeuchiUniversity of MarylandVerified email at umd.edu
Daniel O'MalleyLos Alamos National LaboratoryVerified email at lanl.gov
Robyn MillerLos Alamos National LaboratoryVerified email at lanl.gov

Chandramouli Nyshadham

Kebotix Inc.

Verified email at kebotix.com

Physics Machine learning Computational material science Condensed matter physics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Machine-learned multi-system surrogate models for materials prediction C Nyshadham, M Rupp, B Bekker, AV Shapeev, T Mueller, ... npj Computational Materials 5 (1), 51, 2019	137	2019
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	89	2017
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	55*	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	35	2020
In Handbook of Materials Modeling: Methods: Theory and Modeling; Andreoni, W., Yip, S., Eds C Toher, C Oses, D Hicks, E Gossett, F Rose, P Nath, D Usanmaz, ... Springer International Publishing: Cham, 2018	7	2018
Systems and methods for template-free reaction predictions D Sheberla, C Kreisbeck, K Ryan, C Nyshadham, XU Hengyu US Patent App. 17/579,844, 2022	1	2022
Superalloys compositions including at least one ternary intermetallic compound and applications thereof C Nyshadham, JE Hansen, GLW Hart US Patent 11,214,851, 2022	1	2022
Insights on materials space C Nyshadham, K Lincoln, G Hart APS March Meeting Abstracts 2019, K18. 008, 2019	1	2019
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 March Meeting Abstracts 2019, X19. 003, 2019		2019
Exploring Materials Space with Machine Learning B Bekker, H Oliver, C Nyshadham, A Shapeev, G Hart APS March Meeting Abstracts 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 March Meeting Abstracts 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 March Meeting Abstracts 2018, E34. 011, 2018		2018

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