Author

Cameron Gruich

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Preprint Sep 2026

Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

Equivariant-Free Transformer-Autoencoded Latent Flow Matching is introduced, a two-stage framework that samples a fixed-dimensional latent vector using flow matching and uses an autoregressive Transformer to determine molecular size, atom types, coordinates, and chemical attributes.

Yao Weichi, Cameron J. Gruich, B. R. Goldsmith et al. · 1 citation

Jul 2026

Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction

PG-EVIKAL learns a property-distance metric to re-rank structurally similar neighbors by their property relevance before fusion, building on EVIKAL (scalar Kalman filter) and GP-EVIKAL (Gaussian process variant handling correlated neighbors).

Cameron J. Gruich, Wei-Chi Yao, Yi-Xin Wang et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage generative framework that relies entirely...

Yao Weichi, Cameron J. Gruich, B. R. Goldsmith et al. · 0 citations