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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
Polymorph-Induced Reducibility and Electron Trapping Energetics of Nb and W Dopants in TiO2
Published in The Journal of Physical Chemistry C, 2025
We investigate the atomic-level origin of the experimentally observed formation of paramagnetic Nb4+ and W5+ species in Nb- and W-doped rutile TiO2 vs. non-magnetic Nb5+ and W6+ species in doped anatase TiO2. This is achieved using a combination of theory (Hubbard corrected density functional theory) and experiment (powder electron paramagnetic resonance spectroscopy).
Citation: A. Chaudhari, A. J. Logsdail and A. Folli, Polymorph-Induced Reducibility and Electron Trapping Energetics of Nb and W Dopants in TiO2, The Journal of Physical Chemistry C, 2025, 129 (34), 15453-15461, DOI: 10.1021/acs.jpcc.5c04364
Machine Learning Generalised DFT+U Projectors in a Numerical Atom-Centred Orbital Framework
Published in Digital Discovery, 2025
We present machine learning-based workflows using symbolic regression and support vector machines to simultaneously optimise Hubbard U values and projectors, enabling accurate and efficient simulations of defects and polarons in transition metal and rare-earth oxides.
Citation: A. Chaudhari, K. Agrawal and A. J. Logsdail, Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework, Digital Discovery, 2025, DOI: 10.1039/D5DD00292C
Mixture-of-Experts Transformers for Faithfully Deorbitalized Meta-GGA Density Functionals
Published in ChemRxiv, 2025
We adopt a physics-informed deep learning approach to reparameterise popular semi-local meta-GGA density functionals into non-local deorbitalized surrogates that more faithfully mimic their orbital-dependent parent functionals. This is achieved using a Mixture-of-Experts transformers architecture that uses multi-head self-attention and automatic differentiation to accurately predict exchange energy densities and partial derivatives across a diverse set of molecules and materials. The architecture is designed to replace the expensive feature-based non-locality from the orbital-dependent kinetic energy density with architectural non-locality implemented using attention.
Citation: A. Chaudhari and A. J. Logsdail, Mixture-of-Experts Transformers for Faithfully Deorbitalized Meta-GGA Density Functionals, ChemRxiv, 2025, DOI: 10.26434/chemrxiv-2025-mrgzj-v2
Ab Initio Insights into Support-Induced Sulfur Resistance of Ni-Based Reforming Catalysts
Published in Catalysis Science & Technology, 2026
We investigate the metal oxide support effects controlling the sulfur tolerance of Ni-based catalysts for methane steam reforming. This is achieved using a range of theoretical methods; including DFT-parameterised Monte Carlo sampling of a lattice model of S and O adsorption on Ni(111), adlayer validation with a fine-tuned machine learned interatomic potential and DFT+U simulations of the defect chemistry in common support materials. The predictions are used to rationalise the results of experimental characterisation and methane steam reforming activity testing for H2S-poisoned Ni nanoparticle catalysts.
Citation: A. Chaudhari, P. Stishenko, A. Hiregange, C. Hawkins, M. Sarwar, S. Poulston and A. J. Logsdail, Ab Initio Insights into Support-Induced Sulfur Resistance of Ni-Based Reforming Catalysts, Catalysis Science & Technology, 2026, DOI: 10.1039/D5CY01279A
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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