Oh hi!
I’m Ana — a chemist/ material scientist working at the intersection of experiment, computation, and machine learning to accelerate materials discovery.
I recently completed my PhD at University College London, where I worked in supramolecular chemistry across both wet-lab experimentation and large-scale computational modelling. My research combined synthesis and materials characterisation with high-throughput molecular simulations and ML-driven structure–property prediction.
I previously graduated from University of Cambridge in 2021 (Natural Sciences, Corpus Christi College) where I specialised in (experimental) organic and medicinal chemistry. I greatly enjoyed my degree and LOVED everything organic synthesis related, but the early '20s were a particularly exciting time int he world of computational chemistry, which prompted me to want to expand my technical skills in this area, while still building on top of my experimental skillset. I was therefore really lucky to find my PhD program at UCL which offered research in both components, and the rest is history.
I have undertaken stints in the startup world, first as an intern for Orbital in 2023, then at Entalpic throughout 2024-2025, where I built scalable data pipelines, contributed to ML model development, and acted as a bridge between experimentalists, ML engineers, and strategic teams. These experiences shaped how I think about science: the current challenges we're facing, especially towards climate change, are all complex, which require multi-faceted and rigorous solutions, driven by interdisciplinary teams.
I’m Ana — a chemist/ material scientist working at the intersection of experiment, computation, and machine learning to accelerate materials discovery.
I recently completed my PhD at University College London, where I worked in supramolecular chemistry across both wet-lab experimentation and large-scale computational modelling. My research combined synthesis and materials characterisation with high-throughput molecular simulations and ML-driven structure–property prediction.
I previously graduated from University of Cambridge in 2021 (Natural Sciences, Corpus Christi College) where I specialised in (experimental) organic and medicinal chemistry. I greatly enjoyed my degree and LOVED everything organic synthesis related, but the early '20s were a particularly exciting time int he world of computational chemistry, which prompted me to want to expand my technical skills in this area, while still building on top of my experimental skillset. I was therefore really lucky to find my PhD program at UCL which offered research in both components, and the rest is history.
I have undertaken stints in the startup world, first as an intern for Orbital in 2023, then at Entalpic throughout 2024-2025, where I built scalable data pipelines, contributed to ML model development, and acted as a bridge between experimentalists, ML engineers, and strategic teams. These experiences shaped how I think about science: the current challenges we're facing, especially towards climate change, are all complex, which require multi-faceted and rigorous solutions, driven by interdisciplinary teams.
I’m particularly interested in:
- ML-driven materials discovery (particularly porous materials and catalysts)
- Closing the loop between experiment and ML (with a focus on predicting synthesisability and reproducibility)
- LLMs for material synthesis prediction
Outside of my research, I am passionate about teaching - I have been part time tutoring for over seven years. Moreover, since 2022 I have been conducting tutorials for first and third year undergraduates at UCL in all departments - organic, inorganic, and physical chemistry - where I get the amazing job of sharing my knowledge with other people as well as refresh my (old?) memory.