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SUMMARY:TYC Seminar: Machine Learning for Periodic and Framework Materials
DESCRIPTION:TYC Seminar: Machine Learning for Periodic and Framework Materials Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nDr Ganna Gryn’ova\, University of Birmingham\n\n\n\nSignificant 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.\, SPAHM 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. \n\n\n\nReferences \n\n\n\nM. Ernst\, R. Fedorov\, A. Calzolari\, F. F. Grieser\, S. Ber\, G. Gryn’ova\, preprint DOI: 10.26434/chemrxiv-2025-zbc8x. \n\n\n\nS. Llenga\, G. Gryn’ova\, J. Chem. Phys. 2023\, 158\, 214116.
URL:https://thomasyoungcentre.org/event/tyc-seminar-machine-learning-for-periodic-and-framework-materials/
LOCATION:Royal School of Mines\, Room G05\, Royal School of Mines\, London\, South Kensington\, SW7 2AZ\, United Kingdom
CATEGORIES:Main event
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