Stress induced phase transformations and allotropy in ultra-strength materials

Allotropic phase transformations in materials can be driven by stresses, including large shear stresses made possible by recent advances in material strengthening. This expands the stress space to six dimensions and makes phase transformations dependent on crystal and grain orientation. Understanding the role of the entire stress tensor on phase transformations through multiscale methods—based on atomistic simulation methods (density functional theory, molecular dynamics), nonlinear elasticity, and phase field methods—sheds light on stress-induced phase transformations and can open pathways to novel deformation mechanisms. These mechanisms can help explain the effects of stresses on phase transformations and facilitate the design of materials with transformation-induced plasticity, achieving a balance of high strength and ductility.

References:
[1] A. S. L. Subrahmanyam Pattamatta, David J. Srolovitz, Allotropy in ultra high strength materials, Nature communications 13, 3326 (2022).

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Machine learning potentials

Machine Learning (ML) is well known for its successes in many fields, including in materials science. Among these applications, machine learning-based interatomic potentials have emerged as a new approach to achieve a balance between accuracy, generality, and efficiency in describing the energy and forces associated with different atomic structures in atomistic simulations. The machine-learning, Deep Potential (DP) method, is a relatively new and rapidly developing approach that has recently been applied to a wide range of materials systems. A particularly important application of atomistic simulation of materials is for defect structure and properties, since many important mechanical, electric, optical, etc. properties are determined by defects and their arrangements. We are collaborating with DP developers in Beijing and applying ML DPs for point defects, dislocation, and twinning properties of metals and alloys. Future opportunities lie in DPs for multi-component alloys and the expansion of DPs to a much broader range of materials.

References:
[1] Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, David J. Srolovitz, Deep potentials for materials science, Materials Futures 1, 022601 (2022).

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Interface diffusion control through doping

Coming soon.

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Grain boundary models

Coming soon.

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AI driven discovery of biocompatible superelastic refractory HEA

Coming soon.

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