omm Davis Thomas Daniel

Davis Thomas Daniel

Computational Chemist · Material Scientist · PhD in Chemistry

Bridging molecular modelling, machine learning, and analytical spectroscopy to understand, design and optimise materials.

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I am a material scientist with experience spanning molecular modelling [↗], software development[↗], electrochemistry[↗] and magnetic resonance spectroscopy.[↗]

My work sits at the intersection of atomistic simulations, machine learning[↗], and in operando EPR/NMR spectroscopy to characterise materials.

I am interested in ML-assisted molecular modelling and exploring how exerimental workflows can be made more efficient and autonomous with the help of computational tools. I enjoy scientific programming and have developed multiple open-source libraries including ILTpy and EPRpy, and believe good research software engineering practices are crucial for reproducible research.

Some of my software projects are showcased below.

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Computational Modelling
Experience with DFT, ab initio molecular dynamics, nudged elastic band calculations, and classical MD applied to energy materials, polymers, and electrolytes on HPC systems.
DFTAIMDORCAHPCASE
ML
Machine Learning for Materials
Experience with molecular representation learning and design of active-learning workflows integrating ML models with atomistic simulations and experimental data to accelerate materials discovery.
scikit-learnGRUMLIPsPyTorchDeep Learning
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EPR & NMR Spectroscopy
In operando EPR and NMR to track real-time redox states, lithium solvation, and degradation mechanisms in organic radical batteries and solid electrolytes.
EPR spectroscopyNMR spectroscopyIn Operando electrochemistry
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Scientific Software
Lead developer of ILTpy (regularised multi-dimensional inverse Laplace transform) and EPRpy (Bruker EPR data processing).
PythonAlgorithm developmentILTpyEPRpyNumerical methods