Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

arXiv:2604.13354 · cond-mat.mtrl-sci, cs.AI · Submitted 2026-04-14 · Read on arXiv

cond-mat.mtrl-sci, cs.AI

Submitted: 2026-04-14

Updated: 2026-09-30

Code: https://github.com/link-lab3629/scout-matter

Terminology

• Diffusion Model — 3× in this paper · explained in 9 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• UMA: A Family of Universal Models for Atoms
• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
• Graph atomic cluster expansion for foundational machine learning interatomic potentials

Related papers

• Mode-selective electron-phonon coupling drives charge density waves in the kagome metals YRu 3 Si 2 and LaRu 3 Si 2
• Restoring the Surface Magnetic Gap in MnBi 2 Te 4
• ATLAS: A Foundation Neural Sampler for Amorphous Materials
• A Generalizable Machine Learning Framework for Half-Heusler Thermoelectrics
• Nonlinear dynamics in magnonic Fabry-P' e rot resonators: Low-power neuron-like activation and transmission suppression
• AES-Debye: an Accurate, Efficient, and Scalable Engine for Debye Scattering Calculations