Selected Projects

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Algorithms for Chemical Space Exploration

Built a Bayesian optimisation-based framework (PhaseBO) to efficiently navegate complex compositional spaces. PhaseBO leverages Expected Improvement acquisition and Thompson Sampling for batch selection, combined with uncertainty reduction strategies to balance exploration and exploitation. Compared to conventional grid-searches, PhaseBO enables fine-grained coverage of the compositional space and achieved a 50% increase in the probability of discovering new stable compositions. The implementation is modular and open-source, designed to be extensible for other scientific discovery tasks, such as multi-optimisation or design of experiments.
👉 View code on GitHub


A. Vasylenko et. al, The Journal of Chemical Physics 160, 5 (2024)

Generative AI for crystal structure design

Devised PIGEN - a physics-informed generative framework for crystal structure design based on Denoising Diffusion Probabilistic Models, implemented in PyTorch with multi-GPU support. PIGEN enables generation of crystal structures out-of-distribution, beyond known training data and integrates classifier-free guidance to control generation toward chemically meaningful and stable structures without requiring explicit conditioning labels. The implementation is modular, extensible, and designed for integration with downstream screening or optimisation pipelines.


👉 View code on GitHub