most citedBenchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cond-mat.mtrl-sci2025

Equivariant Diffusion for Crystal Structure Prediction

Peijia Lin, Pin Chen, Rui Jiao +6

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CS…

cs.LG2025★ 1 cited

Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification

Liqin Tan, Pin Chen, Menghan Liu +3

We present MatUQ, a benchmark framework for evaluating graph neural networks (GNNs) on out-of-distribution (OOD) materials property prediction with uncertainty quantification (UQ).…

math.NA2023

Adaptive trajectories sampling for solving PDEs with deep learning methods

Xingyu Chen, Jianhuan Cen, Qingsong Zou

In this paper, we propose a new adaptive technique, named adaptive trajectories sampling (ATS), which is used to select training points for the numerical solution of partial differ…