等变生成扩散学习并泛化非晶氧化物的结构系综
Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides
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中文总结 AI 辅助
提出SE(3)等变去噪扩散模型学习非晶氧化物构型分布，数据高效，可生成大规模结构并探索构型空间，实现能量引导生成。
中文摘要 AI 辅助
非晶材料是统计系综而非确定结构，传统的密度泛函理论（DFT）和机器学习势模拟仅能采样该系综的一小部分。我们提出了一种$SE(3)$等变去噪扩散模型，该模型学习非晶氧化物的构型分布，使得模型本身即成为结构数据库。该学习过程具有数据高效性。一个在$1{,}781$个DFT构型上训练的模型足以重现部分径向分布函数、配位统计和键角分布，并能以与最廉价的经典对势相当的成本生成超过$3\ imes10^{5}$个原子的模型。训练后的模型可以为第一性原理弛豫提出非晶原子结构，以探索构型空间。例如，它定位了一种非晶Zr-Ta-O结构，其能量比先前已知的最低能量低$36$ meV/原子。生成还可以扩展到训练条件之外，如非化学计量比组成、其他质量密度、界面和掺杂。第一性原理验证证实，生成可以被引导至所请求的能量，并表明去噪训练损失不能对生成质量进行排序，因为两者衡量的是不同的方面。
英文摘要
Amorphous materials are statistical ensembles rather than definitive structures, and conventional density-functional (DFT) and machine-learned-potential simulations sample only a small part of that ensemble. We present an $SE(3)$-equivariant denoising-diffusion model that learns the configurational distribution of amorphous oxides, so the model itself is the structure database. The learning is data efficient. A model trained on $1{,}781$ DFT configurations suffices to reproduce partial radial distribution functions, coordination statistics and bond-angle distributions, and to generate models of over $3\times10^{5}$ atoms at a cost comparable to that of the cheapest classical pair potentials. The trained model can propose amorphous atomic structures for first-principles relaxation to explore the configuration space. For example, it locates an amorphous Zr-Ta-O structure $36$ meV/atom below the previously known minimum. Generation can also extend beyond trained conditions to non-stoichiometric compositions, other mass densities, interfaces, and doping. First-principles verification confirms that generation can be steered to a requested energy, and shows that the denoising training loss does not rank generative quality, because the two measure different things.
发表机构
- Northeastern University（东北大学）
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