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.
arXiv preprint arXiv:2308.13565 , year=
5 Pith papers cite this work, alongside 33 external citations. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Two-stage LLM framework infers stellar parameters and ~20 elemental abundances from spectra, showing performance gains with increasing data volume.
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.
LLM pipeline for joint post-level assessment and user-level temporal modeling of mental health from ordered social media posts in a shared task.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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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.
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Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
Two-stage LLM framework infers stellar parameters and ~20 elemental abundances from spectra, showing performance gains with increasing data volume.
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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.
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Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
LLM pipeline for joint post-level assessment and user-level temporal modeling of mental health from ordered social media posts in a shared task.
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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.