Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
<|reference_start|>Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates: Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates.<|reference_end|>
@article{okabe2024structural,
    title={Structural Constraint Integration in Generative Model for Discovery of
  Quantum Material Candidates},
    author={Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen
  Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi
  S. Jaakkola, Yongqiang Cheng and Mingda Li},
    journal={arXiv preprint arXiv:2407.04557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04557},
    primaryClass={cond-mat.mtrl-sci cs.LG}
}