Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Mingjian Wen, Matthew K. Horton, Jason M. Munro, Patrick Huck, Kristin A. Persson
| Summary:
The elasticity tensor that describes the elastic response of a material to
external forces is among the most fundamental properties of materials. The
availability of full elasticity tensors for inorganic crystalline compounds,
however, is limited due to experimental and computational challenges. Here, we
report the materials tensor (MatTen) model for rapid and accurate estimation of
the full fourth-rank elasticity tensors of crystals. Based on equivariant graph
neural networks, MatTen satisfies the two essential requirements for elasticity
tensors: independence of the frame of reference and preservation of material
symmetry. Consequently, it provides a universal treatment of elasticity tensors
for all crystal systems across diverse chemical spaces. MatTen was trained on a
dataset of first-principles elasticity tensors garnered by the Materials
Project over the past several years (we are releasing the data herein) and has
broad applications in predicting the isotropic elastic properties of
polycrystalline materials, examining the anisotropic behavior of single
crystals, and discovering new materials with exceptional mechanical properties.
Using MatTen, we have discovered a hundred new crystals with extremely large
maximum directional Young’s modulus and eleven polymorphs of elemental cubic
metals with unconventional spatial orientation of Young’s modulus.
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