output
20232026
most citedProbing out-of-distribution generalization in machine learning for materials

63 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cond-mat.mtrl-sciShow all

4 papers · 1 filter

cond-mat.mtrl-sci2026

Physics-informed time-series forecasting of perovskite photoluminescence stability

Alexander Wieczorek, Manuel Kober-Czerny, Fábio Lopes +5

Accelerated ageing using elevated temperatures and illumination is one of the most common methods to rapidly study the stability of novel semiconductor materials. However, as the p…

cond-mat.mtrl-sci2024★ 16 cited

LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction

Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1

Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…

cond-mat.mtrl-sci2024★ 63 cited

Probing out-of-distribution generalization in machine learning for materials

Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5

Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…

cond-mat.mtrl-sci2023★ 23 cited

Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'

Daniel Persaud, Logan Ward, Jason Hattrick-Simpers

The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards ope…