arXiv Machine Learning

Computing stable configurations of confined smectic liquid crystals with a deep variational framework

The paper introduces a deep variational framework (DVF) for computing stable configurations of confined smectic liquid crystals using a modified Landau–de Gennes model. By representing orientational and positional order parameters on a regular reference domain and incorporating physical confinement through coordinate mappings, the DVF overcomes spectral bias with a warmup penalty, enabling robust recovery of oscillatory smectic states. The method reproduces known smectic‑A defect structures, predicts new layer morphologies in various confinement geometries, and even forecasts a chevron‑like smectic‑C state on a tangent‑anchored sphere.

arXiv Machine Learning
2d ago

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models...

By Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan, Philipp H\"ollmer, Cheng Zeng, Adrian Roitberg, Mingjie Liu, Richard Hennig, Sapna Sarupria, Ellad B. Tadmor, Stefano Martiniani
arXiv Machine Learning
1d ago

EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations

EP-Flow introduces a new framework for predicting disordered crystal structures without requiring site-level disorder annotations. It uses an Occupancy Distribution Matrix (ODM) to represent continuous site-by-species occupancies and enforces constraints through a transportation polytope. The method jointly generates occupancies, fractional coordinates, and lattice parameters, achieving state‑of‑the‑art performance on formula‑conditioned disordered CSP benchmarks and recovering chemically meaningful local disorder patterns.

By Qiuliang Liu, Liming Wu, Qi Li, Zhonglong Peng, Chang Chen, Xiaolong Chen, Wenbing Huang, Shifeng Jin
arXiv Machine Learning
Jun 4

SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling

arXiv:2606. 04000v1 Announce Type: cross Abstract: We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic materials.

By Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer, Janith Wanni, George T. Gray III, Dan J. Thoma, Curt A. Bronkhorst, Nan Chen, Wenxiao Pan