Search Results for author:
Found 4 papers, 3 papers with code
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences where reproducibility is crucial.
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2. 27 eV and a permittivity of 20. 5, meeting all target metrics of our multi-objective search.
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
