In the CM3 Lab, we advance multi-physics and data-driven materials modeling methods 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).

Chemically complex materials (CCMs)

Chemically complex materials
(left) pure element; (center) dilute solid solution; (right) complex concentrated solid solution. Adapted from Prog. Mater. Sci.

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:

Structurally complex materials (SCMs)

Structurally complex materials
(left) ordinary crystal; (center) quasicrystal; (right) glass. Adapted from Matmatch.

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:

Additive manufacturing (AM)

Powder bed fusion

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.