Crystal structure generation with autoregressive large language modeling
The generation of plausible crystal structures is often the first step in predicting the structure
and properties of a material from its chemical composition. However, most current methods …
and properties of a material from its chemical composition. However, most current methods …
[PDF][PDF] Open materials 2024 (omat24) inorganic materials dataset and models
Date: October 18, 2024 Correspondence: L. Barroso-Luque (lbluque@ meta. com), CL
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …
Discovery of high-performance dielectric materials with machine-learning-guided search
Materials with high dielectric constants polarize easily under external electric fields, making
them essential in modern electronics. However, their utility is often limited by narrow band …
them essential in modern electronics. However, their utility is often limited by narrow band …
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Materials with high-dielectric constant easily polarize under external electric fields, allowing
them to perform essential functions in many modern electronic devices. Their practical utility …
them to perform essential functions in many modern electronic devices. Their practical utility …
Towards Machine Learning Foundation Models for Materials Chemistry
J Riebesell - 2024 - repository.cam.ac.uk
This thesis demonstrates how recent advances in machine learning (ML) for materials can
accelerate our search for new stable inorganic crystals. We show how best to measure and …
accelerate our search for new stable inorganic crystals. We show how best to measure and …