TYC Seminar: Machine Learning for Periodic and Framework Materials

TYC Seminar: Machine Learning for Periodic and Framework Materials Share on X

Dr Ganna Gryn’ova, University of Birmingham

Significant recent advances in chemical machine learning allow predictions of structures and physico-chemical properties of molecular systems with high accuracy and at a fraction of the computational cost of conventional quantum-chemical modelling. However, the associated tools, such as foundational models (e.g., MACE) or quantum-inspired representations (e.g., SPA^HM and MAOC¹) are not easily and directly transferrable to periodic materials due to the need to fine-tune the models on target materials, sparsity of high-quality experimental training data, and the higher costs of generating the presentations. In this talk, we will discuss our recent efforts to address these limitations. Focusing on metal-organic and covalent organic frameworks, we will present a new quantum-inspired representation for machine learning tasks and a new fragmentation algorithm² enabling rational design of these systems. We will also demonstrate how persistent homology can be employed to coarse-grain the representation reducing the computational effort without sacrificing the accuracy of the predictions.

References

M. Ernst, R. Fedorov, A. Calzolari, F. F. Grieser, S. Ber, G. Gryn’ova, preprint DOI: 10.26434/chemrxiv-2025-zbc8x.

S. Llenga, G. Gryn’ova, J. Chem. Phys. 2023, 158, 214116.

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