UniField fuses discrete atomic graphs with continuous electron density fields via RBF guidance in an SE(3)-equivariant multimodal model, reporting new SOTA results on QM9-ED, QMugs-ED, and ED5-OE benchmarks with gains up to 37%.
DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery, July 2025
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CliffSplit exposes at least 15% higher errors in cliff-heavy regions of QM9 while CliffLoss narrows the cliff-to-smooth error gap by up to 30% and improves overall MAE by 9.7% across several molecular tasks and backbones.
SCOPE-BENCH shows state-of-the-art molecular models suffer up to 8x higher errors under extreme OOD, while POMA reduces mean absolute error by up to 11.2% via target-aware source selection and dual-scale adaptation.
An LLM agent integrated with AVEVA Process Simulation via MCP enables natural language driven flowsheet analysis, optimization, and construction for chemical separation processes.
A new method decomposes property differences between weakly related molecules into minimal chemical edits to train a directional evaluator that guides multi-step optimization with less oracle querying.
citing papers explorer
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UniField: RBF-Guided Electron Density Fusion for Enhanced Molecular Representations
UniField fuses discrete atomic graphs with continuous electron density fields via RBF guidance in an SE(3)-equivariant multimodal model, reporting new SOTA results on QM9-ED, QMugs-ED, and ED5-OE benchmarks with gains up to 37%.
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When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors
CliffSplit exposes at least 15% higher errors in cliff-heavy regions of QM9 while CliffLoss narrows the cliff-to-smooth error gap by up to 30% and improves overall MAE by 9.7% across several molecular tasks and backbones.
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Rethinking Molecular OOD Generalization via Target-Aware Source Selection
SCOPE-BENCH shows state-of-the-art molecular models suffer up to 8x higher errors under extreme OOD, while POMA reduces mean absolute error by up to 11.2% via target-aware source selection and dual-scale adaptation.
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Large Language Model Agent for User-friendly Chemical Process Simulations
An LLM agent integrated with AVEVA Process Simulation via MCP enables natural language driven flowsheet analysis, optimization, and construction for chemical separation processes.
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From Single-Step Edit Response to Multi-Step Molecular Optimization
A new method decomposes property differences between weakly related molecules into minimal chemical edits to train a directional evaluator that guides multi-step optimization with less oracle querying.