Seminarium "Modeling of Complex Systems"

The discovery and design of new materials is critical for advancing carbon-emission reducing technologies such as renewable energy and electric vehicles. Experimental discovery of new materials is typically slow and costly, quantum mechanics (QM) calculations have brought computational materials design within reach. However, QM calculations are often limited to relatively small sets of materials, as their computational costs are too great for large-scale screening, this is the case for calculating properties required for new energy materials. In this talk I will present examples of how we have been adapting concepts from language models to help with building fast and efficient models for materials properties. I will show how we can learn distributed representations of atomic species directly from large databases of crystallographic structures [1, 2]. I will also show how a large language model trained on crystallographic data can help to solve one of the outstanding challenges of solid-state chemistry; the prediction of structure from chemical formula [3].[1] npj Comput Mater 8, 44 (2022)[2] APL Machine Learning 2, (2024)[3] Nature Commun. 15, 10570 (2024)

The seminar will be held in hybrid mode: in room 1.40 (FUW, Pasteura 5).
During the seminar the coffee and cakes are provided.
Join Zoom Meeting
https://uw-edu-pl.zoom.us/j/96378632993?pwd=MVgdPR80oKaE4pjLufb2NCtg6ql4Ax.1
Meeting ID: 963 7863 2993
Passcode: 569551