In the CM³ Lab, we advance physics-based and artificial-intelligence-based approaches from subatomic to macro scale and apply them to understanding processing-structure-property relations in chemically and structurally complex materials.

For the processing-structure relation, we use the discrete element method for powder dynamics (using FLOW-3D AM), thermal-fluid model for melt pool (using FLOW-3D AM), and CALPHAD-informed phase field model for solidification (using Thermo-Calc and MOOSE).

For the structure-property relation, we mainly work with two multiscale modeling frameworks. The first framework involves concurrent multiscale modeling based on a coarse-grained atomistic method (using PyCAC). The second framework concerns sequential multiscale modeling, which links density functional theory (using VASP), atomistic simulation method (using LAMMPS), phase-field model (using PFDD), and crystal plasticity model (using ρ-CP).

Unlike single elements and dilute solid solutions, a CCM contains multiple elements in equal or nearly equal atomic fractions. When CCMs are metallic alloys, in either crystalline or amorphous form, they usually serve as structural materials. Some of those CCMs possess superior strength at high temperatures, excellent fracture toughness at low temperatures, or elevated damage resistance. As a result, they are considered next generation structural materials in jet engines, high-latitude vessels, and nuclear reactors.

Because of the inherent chemical complexity, characteristics of CCMs cannot be obtained by simple extrapolation from dilute solid solutions or rule of mixtures prediction based on constituent elements. My research objective is to investigate unique deformation and failure mechanisms in CCMs that give rise to their outstanding mechanical properties.

To date, I have studied plastic deformation of metallic CCMs:

• Su et al. 2019 Modelling Simul. Mater. Sci. Eng.
• Xu et al. 2020 Intermetallics; Wang et al. 2020 Science; Jian et al. 2020 Acta Mater.; Smith et al. 2020 Int. J. Plast.
• Xu et al. 2021 Acta Mater.; Jian et al. 2021 Appl. Phys. Lett.; Xie et al. 2021 Acta Mater.
• Xu et al. 2022 Comput. Mater. Sci.; Jian et al. 2022 Scr. Mater.; Romero et al. 2022 Int. J. Plast.; Fey et al. 2022 Phys. Rev. Mater.; Xu et al. 2022 Appl. Phys. Lett.; Xie et al. 2022 Int. J. Plast.; Gupta et al. 2022 Int. J. Plast.; Xu et al. 2022 Eur. Phys. J. B; Xu et al. 2022 APL Mater.
• Xu et al. 2023 J. Alloys Compd.; Zheng et al. 2023 npj Comput. Mater.; Mamun et al. 2023 Phys. Scr.; Guo et al. 2023 Extreme Mech. Lett.
• Chen et al. 2024 J. Appl. Phys.; Rhodes et al. 2024 Acta Mater.; Mubassira et al. 2024 Modelling; Ruiz et al. 2024 APL Mach. Learn.; Jian et al. 2024 Appl. Phys. Lett.; Wang et al. 2024 npj Comput. Mater.

SCMs are composed of multiple structures/phases that vastly differ in their size, from nano to microscale, and in properties, such as elasticity and plasticity. Compared with homogeneous alloys, metallic SCMs usually have increased specific strength, stiffness, and wear/creep resistance. One example is the metal matrix composite, which consists of a continuous, relatively soft metallic matrix (e.g., Al, Mg) and dispersed, strong reinforcement phases that are ceramics, heavy metals, or carbon-based materials.

One main challenge in producing SCMs is to strike a balance between phases with high thermal conductivity, toughness, and ductility, and those with low thermal expansion, high strength, and high modulus. Designing SCMs with optimal overall mechanical properties requires knowledge of their strengthening mechanism. My research objective is to gain a fundamental understanding of the strengthening of SCMs.

To date, I have studied plastic deformation of SCMs in metals, semiconductors, and polymers:

• Xu et al. 2019 Acta Mater.
• Xu et al. 2020 Phys. Status Solidi (b)
• Selimov et al. 2021 J. Mater. Res.; Jian et al. 2021 J. Mater. Res.
• Xu et al. 2022 Comput. Methods Appl. Mech. Eng.; Cheng et al. 2022 Nano Lett.; Jian et al. 2022 Acta. Mater.; Xu et al. 2022 J. Mech. Phys. Solids
• Zhou et al. 2023 Scr. Mater.; Raj et al. 2023 Polymers
• Fani et al. 2024 Materials; Cheng et al. 2024 Acta Mater.

Many CCMs and SCMs can be produced by AM, which make objects from 3D model data, usually layer upon layer, as opposed to subtractive methodologies, where objects are formed by removing materials through cutting, drilling, milling, or grinding. AM is advantageous over traditional manufacturing in that high-value components with topologically optimized complex geometries and functionalities become achievable.

The AM processes and the AMed materials face many challenges. For example, most metallic alloys in use today would contain undesirable columnar grains and periodic cracks if they were produced by AM. Thus, many current AMed metals have highly anisotropic microstructures and poor mechanical properties, e.g., low strength, fracture toughness, and fatigue resistance. My research objective is to leverage multi-scale, multi-physics simulations and ML to quantify the processing-to-property linkage in the context of the AM processes of CCMs and SCMs.