Amit Chaudhari's Research

Research Interests and Experience

Electronic structure modelling of complex oxides, including DFT+U and hybrid-DFT
Accelerating materials modelling using deep learning, including DFT functionals and interatomic potentials
Integrated workflows for high-dimensional optimisation in materials modelling, incorporating symbolic regression, support vector machines and Bayesian optimisation
Applied large language models for scientific machine learning and generative molecular modelling
High performance computing on CPU and GPU nodes; validating theoretical predictions and extracting useful information from experimental characterisation (EPR, XANES, TEM, XPS)

Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework. Chaudhari A, Agrawal K, Logsdail A. ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2025-332z0
Polymorph-Induced Reducibility and Electron Trapping Energetics of Nb and W Dopants in TiO₂. Chaudhari A, Logsdail A, Folli A. J. Phys. Chem. C, 2025, https://doi.org/10.1021/acs.jpcc.5c04364

Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework: Psi-k and NCCR MARVEL Workshop on the Determination of Hubbard Parameters, 2025
AI-driven modelling for sustainable catalysis - from screening to generative design: Cardiff University Meeting for Applied Simulation Sciences, 2025
Machine learning the DFT+U projectors to model polarons in energy materials: FHI-aims UK Developers’ and Users’ Meeting, 2024 and the Materials Chemistry Consortium Conference, 2024
Designing catalysts for Net-Zero 2050 using first-principles computational modelling: Cardiff Chemistry Conference, 2024
Accurate modelling of n-type doped TiO₂ polymorphs using DFT+U with occupation matrix control: Materials Chemistry Consortium Workshop on the Modelling Point Defects, 2024
A computational study of the adsorption and activation of H₂, CO and CO₂ on stoichiometric CeO₂(110) supported Cu_1–3 nanoclusters: World Congress in Oxidation Catalysis, 2022

AI for efficient quantum chemical simulations- from DFT+U to orbital-free meta-GGAs: BIOVIA (Dassault Systèmes) ASTS Seminar, 2025
Machine learning algorithms for simulating realistic catalytic reaction environments: Johnson Matthey and bp (Prosperity Partnership), 2025
Combining DFT, global optimisation and machine learning to understand metal oxide support effects in catalysis: Johnson Matthey Computational Modelling Group, 2024
Sustainable Catalysis for Clean Growth - Advanced Methods: bp International Centre for Advanced Materials Annual Conference, 2023

Towards a transferable kinetic energy density functional using symbolic regression and large language models: CECAM Machine Learning for Materials Discovery workshop, 2025
Machine learning generalised DFT+U projectors to model polarons in catalyst and battery materials: Thomas Young Centre 7th Energy Materials workshop, 2024
Ab initio adsorption phase diagrams using DFT-parameterised Monte Carlo methods: STFC CCP5 Annual General Meeting, 2023