Computational Engineering at Lemnisca | Ex-@NorthwesternU | PhD from @PurdueChemE | B. Chem. Engg. from @ICTMumbai1933 šŸ‡®šŸ‡³

Bengaluru, India
Joined July 2018
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Congratulations Tristan! Looks very interesting, looking forward to reading it.
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Presenting a talk on the application of deep learning towards the study and design of high-entropy alloy catalysts at @NAM29NACS in the "Using AI to Navigate Large Catalyst Design Spaces" session on June 10 (Tuesday) at 3:00 PM. Happy to chat and connect with those attending NAM!
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We develop a computational framework to analyze key relationships between surface structure, composition, and thermodynamic stability of alloy catalysts and using this, we describe the origin and formation of "Pt skins" on Pt3Ni that are responsible for its exceptional activity.
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Glad to share my latest manuscript published in J. Phys. Chem. C on an atomic scale study of the stability of bimetallic Pt alloys for electrochemical oxygen reduction using density functional theory, thermodynamics, and machine learning.
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In this work, we go beyond Cu catalysts, and explore oxide-derived Co catalysts for producing long-chain hydrocarbons! The experiments from @catalysis_eth have been followed up with simulations at @TheorHetCatICIQ, pin-pointing the role of polarized sites at the interface.
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Replying to @YinanXu2
Congrats Yinan!
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Thanks to @GroupGreeley for their unwavering support over the years. It's been a fun ride!
We recently celebrated Gaurav's (@ChemAndCode ) successful PhD defense at the Greeley Group favorite Bru Burger. We wish him all the best as he embarks his postdoc journey @NorthwesternU!
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We also utilize Diffusion Maps (courtesy of the Kevrekidis group), a non-linear dimensionality reduction technique, to provide more interpretability to the network's predictions and serve as a data-driven coordinate for optimization.
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We perform Bayesian optimization with a novel acquisition function inspired by statistical mechanics and show how to improve predictions on ternary alloys by relying primarily on binary alloy data and a few carefully sampled ternary data points.
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Excited to share our work on developing an active learning workflow to extrapolate formation energies of binary alloys to ternary alloys using a dropout graph convolutional network (dGCN), that provides associated uncertainty estimates as well.
Congrats to Gaurav (@ChemAndCode), Noah, and Nikolaos for their recent work on developing an active learning workflow to predict formation free energies of ternary alloys using a dropout graph neural network in NPJ computational materials (@Nature_NPJ). nature.com/articles/s41524-0…
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Congratulations Riddhesh!
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Replying to @YashLaxmanKamb1
Congratulations!!
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Gaurav Deshmukh retweeted
We recently bid farewell to @Kaustubh_Savant over a hearty group dinner. We wish him luck in all future endeavors! This also provided us with an opportunity to welcome and introduce new members, Brady, Masa, and Dhruv, to the group.
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Gaurav Deshmukh retweeted
Congratulations to Hee-Joon and Ankita (@Ankita_Morankar) for their latest work in @JPhysChem on developing a theory for the solvation of aqueous-phase reaction intermediates based on structure sampling facilitated by ab-initio molecular dynamics (AIMD). pubs.acs.org/doi/full/10.102…
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Gaurav Deshmukh retweeted
Prof. Greeley has been recognized for excellence in teaching by @PurdueChemE. Proud to have him as our advisor!
Congratulations to our 8 Chemical Engineering faculty members, recognized by @PurdueEngineers for their outstanding dedication to teaching students in the fall 2023 semester! engineering.purdue.edu/ChE/n…
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