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Farshud Sorourifar on Muck Rack

Farshud Sorourifar

As seen in: Wiley Online Library, arXiv, ACS Publications
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Articles

Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes Click to copy article link Article link copied!

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By Abigail M. Houser, Madhav R. Muthyala, Farshud Sorourifar
| dx.doi.org
Introduction Click to copy section linkSection link copied! Organic electrode materials (OEMs), containing C, N, O, S, etc., are promising alternatives to transition-metal-based cathode materials due to their abundance, synthetic tunability, and structural diversity. (1,2) The virtually unlimited design space of organic compounds allows for optimization of cycling stability, solubility, redox potential, etc., for both redox flow battery and solid-state battery applications.
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Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design

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By Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng
| dx.doi.org
Industrial & Engineering Chemistry Research Cite this: Ind. Eng. Chem. Res. 2025, XXXX, XXX Click to copy citationCitation copied! . This publication is licensed under CC-BY 4.0 . You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.
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Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design

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By Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng
| ACS Publications
Special Issue Published as part of Industrial & Engineering Chemistry Research special issue “2025 Class of Influential Researchers”. 1. Introduction Click to copy section linkSection link copied! Designing molecules that satisfy multiple competing criteria remains a grand challenge at the interface of chemistry, materials science, and artificial intelligence (AI).
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