63 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA7 papers
- Vector InstituteCA5 papers
- Princeton UniversityUS2 papers
- Schwartz/Reisman Emergency Medicine InstituteCA2 papers
- Argonne National LaboratoryUS1 paper
- Canadian Institute for Advanced ResearchCA1 paper
- Fritz Haber Institute of the Max Planck SocietyDE1 paper
- Material Measurement LaboratoryUS1 paper
- Nanyang Technological UniversitySG1 paper
- National Institute of Standards and TechnologyUS1 paper
- Swiss Federal Laboratories for Materials Science and TechnologyCH1 paper
- Tinbergen InstituteNL1 paper
7 papers
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…
RAISE: A self-driving laboratory for interfacial property formulation discovery
Mohammad Nazeri, Sheldon Mei, Jeffrey Watchorn +7
Surface wettability is a critical design parameter for biomedical devices, coatings, and textiles. Contact angle measurements quantify liquid-surface interactions, which depend str…
El Agente: An Autonomous Agent for Quantum Chemistry
Yunheng Zou, Austin H. Cheng, Abdulrahman Aldossary +13
Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and cha…
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…
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
Austin Cheng, Cher Tian Ser, Marta Skreta +8
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it i…
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,…
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…