A composition-weighted symbolic regression framework learns analytical expressions and elemental weightings from composition to predict materials properties with accuracy competitive to black-box models while producing explicit, constraint-enforcing formulas.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
ChemDFM-R is a chemical reasoning LLM trained via a four-stage pipeline on the ChemFG dataset of functional-group annotations for molecules and reactions, reaching performance comparable to or better than commercial models on chemical benchmarks.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
-
Composition-Weighted Symbolic Regression for General-Purpose Property Prediction
A composition-weighted symbolic regression framework learns analytical expressions and elemental weightings from composition to predict materials properties with accuracy competitive to black-box models while producing explicit, constraint-enforcing formulas.
-
ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge
ChemDFM-R is a chemical reasoning LLM trained via a four-stage pipeline on the ChemFG dataset of functional-group annotations for molecules and reactions, reaching performance comparable to or better than commercial models on chemical benchmarks.
-
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
- Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference