2 citations · 2 across the 4 of their papers we have counts for
4 papers
Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules
Weichi Yao, Cameron Gruich, Bryan R. Goldsmith +1
Molecular size is intrinsically linked to composition, structure, and properties, but most 3D molecular generators treat size as a predefined input rather than a quantity to be gen…
Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction
Cameron Gruich, Weichi Yao, Yixin Wang +1
A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules, a retraining-free proc…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification
Cameron Gruich, Varun Madhavan, Yixin Wang +1
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimati…