Designing composite materials relies on conjoining different base materials and manipulating complex microstructures. In model-driven design of such materials, calculation of physical properties often relies on multiscale modeling approaches such as finite element analysis (FEM) or molecular dynamics simulations. However, a large gap exists between the design space and physical performance of these materials due to the intractable number of computational measurements.
Deep learning applications incorporating field-based, particle-based, and continuum-based modeling have revolutionized predictions in quantum interactions, and molecular force fields. The machine learning approach of this invention aims to simplify the data needed for accurate prediction of material behavior by translating material composition directly to strain and stress fields.