Tej Choksi

Assistant Professor

Nanyang Technological University (NTU)

Singapore

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About

Tej Choksi is an Assistant Professor at Nanyang Technological University in Singapore. His research areas encompass machine learning applications in catalyst design, CO2 reduction technologies, and the optimization of heterogeneous catalysts. Recent publications highlight his work on nanocluster catalysts, electrocatalysts, and the computational methods used to predict catalytic behaviors.

Articles (18)

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2025

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2024

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2024

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2022

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2022

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2022

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2022

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2021

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2021

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2020

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2020

Collaborators (6)

Siva Krishna Karuturi

Associate Professor

Australian National University

AUSTRALIA

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Andrew T. S. Wee

National University of Singapore (NUS)

SINGAPORE

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Philomena Schlexer Lamoureux

Stanford University

UNITED STATES

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Markus Kraft

Professor

University of Cambridge

UNITED KINGDOM

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Alexei Lapkin

University of Cambridge

UNITED KINGDOM

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Parvathala Reddy Narangari

Australian National University

AUSTRALIA

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Tej Choksi

Research Interests

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About

Articles (18)

Harnessing physics-inspired machine learning to design nanocluster catalysts for dehydrogenating liquid organic hydrogen carriers

Advances in CO2 reduction on bulk and two-dimensional electrocatalysts: From first principles to experimental outcomes

Robust Oxygen Evolution on Ni-Doped MoO3: Overcoming Activity–Stability Trade-Off in Alkaline Water Splitting

The design and optimization of heterogeneous catalysts using computational methods

Tuning the Work Function of MXene via Surface Functionalization

Generalized Principles for the Descriptor-Based Design of Supported Gold Catalysts

Accelerated Approach toward Predicting Coverage-Dependent Surface Free Energies on Transition-Metal Surfaces

Covalency-aided electrochemical CO2 reduction to CO on sulfide-derived Cu–Sb

Investigating the impact of dynamic structural changes of Au/rutile catalysts on the catalytic activity of CO oxidation

Predicting the work function of 2D MXenes using machine-learning methods

Data-driven methods to predict the stability metrics of catalytic nanoparticles

Strong Metal–Support Interaction Boosts Activity, Selectivity, and Stability in Electrosynthesis of H2O2

Microkinetic Modeling of Propene Combustion on a Stepped, Metallic Palladium Surface and the Importance of Oxygen Coverage

H2O and CO2 surface contamination of the lithium garnet Li7La3Zr2O12 solid electrolyte

Manipulating Intermediates at the Au–TiO2 Interface over InP Nanopillar Array for Photoelectrochemical CO2 Reduction

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

Revealing the structure of a catalytic combustion active-site ensemble combining uniform nanocrystal catalysts and theory insights

Predicting metal–metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities

Collaborators (6)

Siva Krishna Karuturi

Andrew T. S. Wee

Philomena Schlexer Lamoureux

Markus Kraft

Alexei Lapkin

Parvathala Reddy Narangari

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