Balaji Sesha Sarath Pokuri

Iowa State University — United States

Materials Science · Physical Sciences

11h-index388citations36works0.02yr avg
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Research Topics

Machine Learning in Materials Science(15), Organic Electronics and Photovoltaics(10), Block Copolymer Self-Assembly(5), Advanced Memory and Neural Computing(4), Rheology and Fluid Dynamics Studies(4)

Publications36 total

Constructing generalizable microstructure–property maps across diverse microstructure classes
MRS Communications·2025Open Access
Optimization framework for <scp>patient‐specific</scp> modeling under uncertainty
International Journal for Numerical Methods in Biomedical Engineering·2022Open Access
Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
Chemical Science·2022· 38 citedOpen Access
Construction and high throughput exploration of phase diagrams of multi-component organic blends
Computational Materials Science·2022· 5 cited
Multi-fidelity machine learning models for structure–property mapping of organic electronics
Computational Materials Science·2022· 12 cited
Algorithm 1025: PARyOpt: A Software for <u>P</u> arallel <u>A</u> synchronous <u>R</u> emote Ba <u>y</u> esian <u>Opt</u> imization
ACM Transactions on Mathematical Software·2022· 3 citedOpen Access
A graph based approach to model charge transport in semiconducting polymers
npj Computational Materials·2022· 13 citedOpen Access
Computational characterization of charge transport resiliency in molecular solids
Molecular Systems Design & Engineering·2022· 1 citedOpen Access
Following the crystal growth of anthradithiophenes through atomistic molecular dynamics simulations and graph characterization
Molecular Systems Design & Engineering·2021· 1 cited
Process-structure-property map for organic solar cells
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Process-structure-property map for organic solar cells
2021· 2 citedOpen Access
Fast inverse design of microstructures via generative invariance networks
Nature Computational Science·2021· 47 cited
Fast Inverse Design of Microstructures via Generative Invariance Networks
2020· 2 citedOpen Access
InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models
Proceedings of the AAAI Conference on Artificial Intelligence·2020· 13 citedOpen Access
Determination of the Free Energies of Mixing of Organic Solutions through a Combined Molecular Dynamics and Bayesian Statistics Approach
Journal of Chemical Information and Modeling·2020· 11 cited
Contributors
Elsevier eBooks·2020
Quantifying the effects of noise on early states of spinodal decomposition:
Elsevier eBooks·2020· 2 cited
Interpretable deep learning for guided microstructure-property explorations in photovoltaics
npj Computational Materials·2019· 76 citedOpen Access
Encoding Invariances in Deep Generative Models
arXiv (Cornell University)·2019· 20 citedOpen Access
GRATE: A framework and software for GRaph based Analysis of Transmission Electron Microscopy images of polymer films
Computational Materials Science·2019· 7 cited
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Frequent Co-authors

Baskar Ganapathysubramanian(35), Soumik Sarkar(12), Chinmay Hegde(10), Ameya Joshi(8), Olga Wodo(8), Viraj Shah(7), Chad Risko(6), Sambuddha Ghosal(4), Xian Yeow Lee(3), Chih-Hsuan Yang(3), Jarosław Żola(3), Spencer Pfeifer(3), Ramin Noruzi(3), Michael L. Chabinyc(3), Sean M. Ryno(3), Alec Lofquist(3), Apurva Kokate(2), Joshua R. Waite(2), Aditya Balu(2), Pengfei Du(2)