Harnessing large language models for data-scarce learning of polymer properties
Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis and design. Their vast number …
Category: Computational methods
Efficient crystal structure prediction based on the symmetry principle
Crystal structure prediction (CSP) is an evolving field aimed at discerning crystal structures with minimal prior information. Despite the success …
Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials
Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective ut…
Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training
Recent advancements in machine learning have demonstrated its potential to significantly accelerate the discovery of new materials. Central to this…