基于可微图神经网络分子动力学的弹性网络分布外逆向设计
Out-of-Distribution Inverse Design of Elastic Networks with Differentiable Graph Neural Network Molecular Dynamics
- The Wolfson Department of Chemical Engineering, Technion – Israel Institute of Technology（以色列理工学院沃尔森化学工程系）
- Faculty of Mathematics, Technion – Israel Institute of Technology（以色列理工学院数学学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出基于可微图神经网络分子动力学的逆向设计框架，通过短时动力学初始化与物理细化，实现超出训练分布的弹性网络优化，可设计强拉胀网络并泛化至大规模系统。
中文摘要 AI 辅助
基于机器学习的逆向设计可以加速具有目标性能的材料的发现，但传统的结构-性能模型通常需要大型训练数据集，并且在其训练分布之外泛化能力较差。在此，我们提出了一种基于图神经网络分子动力学模拟器的可微逆向设计框架。通过在模拟过程中将短时动力学初始化与基于物理的细化相结合，该框架能够在远超训练数据所代表条件的范围内进行优化。利用无序弹性网络，我们表明，仅使用泊松比在0.1到0.4之间的非拉胀系统训练的模拟器，能够设计出泊松比低至-0.3的强拉胀网络。该框架还产生了训练期间未观察到的应力-应变响应，创建了训练数据中不存在的局部力学缺陷，并且在不同系统尺寸间具有泛化能力，使得尽管仅在少于200个节点的网络上训练，也能优化测试规模达5000个节点的网络。
英文摘要
Machine-learning-based inverse design can accelerate the discovery of materials with targeted properties, but conventional structure--property models often require large training datasets and generalize poorly beyond their training distribution. Here, we present a differentiable inverse design framework based on a graph neural network molecular dynamics simulator. By combining a short dynamical initialization with physics-based refinement during simulation, the framework enables optimization well beyond the conditions represented in the training data. Using disordered elastic networks, we show that a simulator trained only on non-auxetic systems with Poisson's ratios between 0.1 and 0.4 can design strongly auxetic networks with values as low as -0.3. The framework also produces stress--strain responses outside the range observed during training, creates localized mechanical defects absent from the training data, and generalizes across system size, enabling optimization of networks tested up to 5000 nodes despite being trained only on networks with fewer than 200 nodes.
