The strength of materials, like many problems in the natural sciences, spans multiple length and time scales, and the solution has to balance accuracy and performance. Peierls stress is one of the central concepts in crystal plasticity that measures the strength through the resistance of a dislocation to plastic flow. The determination of Peierls stress involves a multiscale nature depending on both elastic lattice responses and the energy landscape of crystal slips. Material screening by strength via the Peierls stress from first-principles calculations is computationally intractable for the nonlocal characteristics of dislocations, and not included in the state-of-the-art computational material databases. In this work, we propose a physics-transfer framework to learn the physics of crystal plasticity from empirical atomistic simulations and then predict the Peierls stress from chemically accurate density functional theory-based calculations of material parameters. Notably, the strengths of single-crystalline metals can be predicted from a few single-point calculations for the deformed lattice and on the {\gamma} surface, allowing efficient, high-throughput screening for material discovery. Uncertainty quantification is carried out to assess the accuracy of models and sources of errors, showing reduced physical and system uncertainties in the predictions by elevating the fidelity of training models. This physics-transfer framework can be generalized to other problems facing the accuracy-performance dilemma, by harnessing the hierarchy of physics in the multiscale models of materials science.

翻译：材料的强度，如同自然科学中的许多问题一样，跨越多个长度和时间尺度，其求解需要在精度与性能之间取得平衡。派尔斯应力是晶体塑性力学的核心概念之一，它通过位错对塑性流变的阻力来衡量材料强度。派尔斯应力的确定涉及多尺度特性，既依赖于弹性晶格响应，也依赖于晶体滑移的能量图景。通过第一性原理计算得到的派尔斯应力来筛选材料强度，由于位错的非局域特性而在计算上难以实现，并且未被纳入当前最先进的计算材料数据库中。在这项工作中，我们提出了一种物理迁移学习框架，从经验原子模拟中学习晶体塑性力学，然后基于化学精度密度泛函理论计算的材料参数预测派尔斯应力。值得注意的是，只需对变形晶格和γ面进行几次单点计算，即可预测单晶金属的强度，从而为材料发现实现高效、高通量的筛选。通过不确定性量化来评估模型精度和误差来源，结果表明通过提高训练模型的保真度，能够降低预测中的物理和系统不确定性。这种物理迁移学习框架可推广至其他面临精度-性能困境的问题，其关键在于利用材料科学多尺度模型中物理学的层次结构。