Deep learning generative model for crystal structure prediction
arxiv(2024)
摘要
Recent advances in deep learning generative models (GMs) have created
unprecedented high capabilities in accessing and assessing complex
high-dimensional data, allowing superior efficiency in navigating vast material
configuration space in search of viable structures. Coupling such capabilities
with physically significant data to construct trained models for materials
discovery is crucial to moving this emerging field forward. Here, we present a
universal GM for crystal structure prediction (CSP) via a conditional crystal
diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to
allow user-defined material and physical parameters such as composition and
pressure. This model is trained on an expansive dataset containing over 670,000
local minimum structures, including a rich spectrum of high-pressure
structures, along with ambient-pressure structures in Materials Project
database. We demonstrate that the Cond-CDVAE model can generate physically
plausible structures with high fidelity under diverse pressure conditions
without necessitating local optimization, accurately predicting 59.3
3,547 unseen ambient-pressure experimental structures within 800 structure
samplings, with the accuracy rate climbing to 83.2
fewer than 20 atoms per unit cell. These results meet or exceed those achieved
via conventional CSP methods based on global optimization. The present findings
showcase substantial potential of GMs in the realm of CSP.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要