4 papers · 1 filter
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
Automatic Identification of Compounds in Molecular Mixtures from Liquid-Phase Infrared Spectra
Yannah J. U. Melle, Thanh Nguyen, Jeffrey Lopez +1
Interpreting spectroscopy data is a critical bottleneck in automating chemical research and industrial characterization. Particularly within infrared (IR) spectroscopy, identifying…
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
Jingru Gan, Peichen Zhong, Yuanqi Du +7
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Langua…
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh +2
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification…