CAREER: Emergence of in-liquid structures in metallic alloys by nucleation and growth

NONTECHNICAL SUMMARY

Crystallization is a well-known phenomenon where solid regions start forming within a liquid on very small length scales. This phenomenon is foundational to natural phenomena and engineering applications, including ice formation, hydrocarbon clathrates in natural gas pipelines, bio-mineralization of calcium phosphate during bone formation, the synthesis of molecular crystals for drug design and production, and solidification of metallic alloys to achieve desirable mechanical properties.

The formation of solid regions is highly dependent on the underlying liquid phase structure. In the liquid, a few atoms and/or molecules can self-organize themselves into geometric or non-geometric structures that exist for a very short time. These structures, which are called emergent structures, can sometimes convert themselves into new nanoscale structures that trigger the crystallization process.

Depending on the material, the conversion may involve multiple steps or can even be a cascading process, where one structure leads to another structure, all happening within the liquid phase. An understanding of the in-liquid emergent structures and the multistep crystallization process is critical because the structure at each step may hold useful information and reveal properties that can be leveraged for desired applications.

Unfortunately, physical interrogation of the liquid structure is often based on expensive custom-built instrumentations, i.e., in situ X-ray synchrotron diffraction, that tend to be limited by temporal and spatial resolution. On the other hand, atomistic simulations are, in principle, able to provide a detailed, spatially and temporally resolved, characterization of the in-liquid emergent structure-to-crystal conversion mechanisms.

Yet, these simulations can be prohibitively time-consuming because they require computing quantum-mechanical interactions between a large number of atoms over multiple iterations. Modern machine-learning and artificial intelligence approaches promise to circumvent limitations currently faced by atomistic simulations. The team of the PI will combine atomistic simulations with machine-learning and artificial intelligence approaches to gain unprecedented insights into in-liquid emergent structures and their influence on crystallization.

This project will integrate research and education to establish a pathway of undergraduate and graduate students from underrepresented communities to join New Mexico Tech. This effort recognizes that New Mexico is a state with a Hispanic majority population and a significant Native American community that currently face limitations in gaining access to STEM programs.

To this end, the PI will pursue two projects: (1) the development of two month-long summer camps, named Camp PyMatter, for 10-11th grade students and teachers from local high schools, which will introduce the basics of Materials Science using the Python programming language, and (2) outreach to Navajo Technological University’s School of Engineering, Math & Technology by developing inter-university faculty collaborations and engaging their students with alloy fabrication techniques and computational Materials Science-based topics.

TECHNICAL SUMMARY

Recent studies have revealed that intricate structures that emerge within the liquid state catalyze crystallization via multi-step nucleation processes. These structures bear surprisingly little resemblance to the final equilibrium solid. However, extant classical nucleation and growth theories assume a direct transformation from the liquid phase to equilibrium solid via a single-step process and do not account for such structures.

The project seeks to overcome this fundamental limitation by developing a thermodynamically integrated, mechanism-based modeling framework to predict muti-step nucleation and growth pathways and quantify their energetics and kinetics for a wide-range of crystallization problems. The PI’s research team will pursue the following three research objectives by studying model metallic alloys: (1) develop a robust methodology to detect emergent structures within a liquid phase and correlate them with energy-structure landscape, (2) utilize that landscape to quantify activation energy and nucleation rates associated with multi-step nucleation processes, and (3) leverage energy-structure landscape to quantify growth kinetics of a solids.

Atomistic simulations will be employed to detect structures and determine their energies, and establish structure-thermodynamics relationship by using unsupervised, supervised, and generative machine learning methods. This project will directly impact the discovery of novel metallic alloys used in load-bearing applications. It will guide the selection of appropriate alloying elements that effectively influence the nucleation energetics and rates during solidification from a liquid melt.

By exerting control over the nucleation process, one can precisely manipulate the grainsize distribution and mechanical properties of the solidified microstructure. The framework used in this study is robust, adaptable, and scalable, as it allows for the examination of the effects of novel elemental additions on solidification of current alloys and, crucially, pave way for discovering next-generation materials with transformative mechanical properties.

One such example is the development of high entropy alloys, which emphasize the role of complex crystallization mechanisms due to their multi-element environment.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.