ATLAS：一种用于非晶材料的基础神经采样器

中文摘要 AI 辅助

非晶材料具有优异的机械和功能特性，但其崎岖的能量景观极难采样。低于玻璃化转变温度时，传统分子动力学和蒙特卡罗方法效率低下，数据驱动生成模型受限于稀缺且有偏差的参考系。本文引入ATLAS，一种通过学习扩散过程直接从目标能量函数生成玻尔兹曼分布非晶结构的高效采样器。由等变图神经网络参数化，可跨系统大小、温度和成分进行泛化。利用扩散过程的时间反转，能有效估计热力学量并导向目标可观测量。在二维Kob-Andersen系统中，ATLAS再现了并行回火马尔可夫链蒙特卡罗结构分布、自由能和熵，在低温玻璃态下自由能误差低于0.2%，能量评估次数减少超500倍。在Cu-Zr和Cr-Co-Ni金属玻璃中，ATLAS恢复了实验观察到的短程有序趋势，并将结构导向规定的序参量和优化的体模量。此外，成分摊销预训练优于从头开始的特定成分训练，将逆设计成本降低数百倍，并能使用昂贵的通用机器学习原子间势进行采样。与大语言模型智能体结合，ATLAS在八元素空间中搜索高熵金属玻璃，在480次预言机评估内确定了收敛的帕累托前沿。这些结果确立了ATLAS作为非晶材料采样、导向和设计的基础模型。

英文摘要

Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an efficient sampler that learns a diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. Parameterized by an equivariant graph neural network, ATLAS generalizes across system size, temperature, and composition. By exploiting the time reversal of the diffusion process, it enables efficient estimation of thermodynamic quantities and steering toward target observables. In two-dimensional Kob-Andersen systems, ATLAS reproduces parallel tempering Markov chain Monte Carlo structural distributions, free energies and entropies, achieving below 0.2% free energy error in the low-temperature glass regime with over 500-fold fewer energy evaluations. In Cu-Zr and Cr-Co-Ni metallic glasses, ATLAS recovers experimentally observed short-range-order trends and steers structures toward prescribed order parameters and optimized bulk moduli. Moreover, composition-amortized pretraining outperforms composition-specific training from scratch, reduces inverse-design costs by several hundred-fold, and enables sampling with expensive universal machine learning interatomic potentials. Coupled to a large language model agent, ATLAS searches an eight-element space for high-entropy metallic glasses balancing stiffness and ductility, identifying a converged Pareto frontier within 480 oracle evaluations. Together, these results establish ATLAS as a foundation model for sampling, steering and designing amorphous materials.

发表机构

• Microsoft Research New England（微软研究院新英格兰分部）
• Center for Computational Science and Engineering（计算科学与工程中心）
• MIT（麻省理工学院）
• Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
• Department of Materials Science and Engineering（材料科学与工程系）
• Department of Computer Science and Engineering（计算机科学与工程系）
• OSU（俄亥俄州立大学）

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