Physics-Transfer Learning for Material Strength Screening
Yingjie Zhao Zian Zhang Zhiping Xu
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.