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

2026-04-14 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Engineering & Applied Sciences, Physical Sciences & Chemistry, Research Methodology & Innovation · Depth: Expert, extended

Summary

A new generative machine learning framework, based on diffusion models with adaptive constraint guidance, has been developed to address the challenge of discovering inorganic crystal structures with targeted properties. This finetuning-free approach allows users to incorporate physical and chemical constraints during the generation process, making it practical and interpretable for human experts. The framework utilizes a multi-step validation pipeline, combining graph neural network estimators for DFT-level accuracy and convex hull analysis for thermodynamic stability. Tested on various inorganic compound families, including high-density boron, Fe-Nd-B, Li-Co-O, and Cu-Si-P systems, the method successfully generates thermodynamically plausible crystal structures that satisfy targeted geometric constraints. The approach emphasizes quality over quantity, ensuring generated structures adhere to fundamental chemical principles and domain-specific knowledge, even for complex multi-objective constraints in quaternary systems like Cu-Si-P-Ca.

Key takeaway

For materials scientists and AI researchers focused on novel inorganic crystal discovery, this finetuning-free guidance framework offers a powerful tool. You can now direct generative diffusion models to produce structures that meet specific chemical and geometric constraints, moving beyond unverified, large-scale predictions. This approach allows for expert-driven exploration and the design of functional materials by ensuring thermodynamic plausibility and targeted local environments, significantly enhancing the practical utility of generative AI in materials science.

Key insights

Adaptive constraint guidance in diffusion models enables finetuning-free generation of inorganic crystal structures with targeted properties.

Principles

• Integrate human guidance into generative AI for materials science.
• Prioritize quality and plausibility over sheer quantity in material generation.
• Ensure differentiability for effective constraint guidance.

Method

The method employs Universal Guidance on a pre-trained diffusion model (MatterGen) to enforce user-defined physical and chemical constraints during sampling, without retraining. It uses differentiable loss functions and a multi-step validation pipeline for thermodynamic stability.

In practice

• Generate high-density boron allotropes by targeting per-atom volume.
• Steer B-Fe coordination in Fe-Nd-B systems to specific values.
• Induce metastable Li-Co-O structures with reduced Co-O coordination.

Topics

• Diffusion Models
• Crystal Structure Generation
• Constraint Guidance
• Inorganic Materials
• Materials Discovery

Code references

• Inirius/mattergenbis

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.