CAREER: A Novel Computational Thermodynamics Framework with Intrinsic Chemical Short-Range Order

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NONTECHNICAL SUMMARYThis CAREER award supports computational and theoretical research, experiment, and education to advance the ability to calculate phase diagrams of materials. Phase diagrams are roadmaps of materials that indicate how atoms organize themselves at different temperatures, compositions, pressures, or other variables. Computational thermodynamic modeling of metals and alloys could reveal their stability under various temperatures and compositions, providing guidance for manipulating the properties of alloys. However, currently missing in the mathematical models commonly used by computational thermodynamic approaches is the description of atomic-scale short-range order. When atoms bond with each other to form a solid, they prefer to have specific kinds of atoms as their neighbors which leads to atomic-scale (short-range) order. This CAREER award supports the PI and his team to pursue a fundamental improvement in computational thermodynamics by developing a new cluster-based model which incorporates atomic-scale order into the mainstream thermodynamic modeling framework. This new framework is implemented in a software platform and accessible to the broad materials science community. The computational prediction of atomic-scale order is experimentally validated in several alloys with experiments that use intense high-energy X-rays to obtain different kinds of images of the locations of atoms. The developed computational framework represents a new methodology that enables atomic-scale order to be exploited for materials design, which potentially leads to novel complex concentrated alloys or improved commercial alloys that may appear in automobiles, airplanes, or other applications.This CAREER award provides support for education and outreach activities including: mentoring local K-12 students through the NanoDays events at the University of Virginia, holding annual summer programs for underrepresented minority students through the Virginia-North Carolina Alliance for Minority Participation, developing a thermodynamics-related course and learning modules, and creating a Python code library for 3D visualization of concepts in thermodynamics, making this subject fun and intuitive.TECHINICAL SUMMARYThis CAREER award supports computational and theoretical research, experiment, and education to advance the ability to calculate phase diagrams and thermodynamic properties of materials, particularly metals and alloys. The PI aims to explore a cluster-based thermodynamic approach that enables the prediction of chemical short-range order (SRO) in multicomponent materials using the CALculation of PHAse Diagram (CALPHAD) method. CALPHAD is a leading method for computational thermodynamic modeling of materials. However, the current approach used in CALPHAD, the sublattice model, is a mean-field model with the ideal entropy of mixing. This makes CALPHAD inadequate for properly describing order-disorder transitions or chemical SRO in alloys, such as the Guinier-Preston zones or nanoscale clusters, which are critical for alloy mechanical properties. First-principles alloy theories, using the cluster variation method (CVM) or the cluster expansion method, can describe SRO but are generally limited to binary or ternary systems due to the large number of configuration variables. The PI plans to develop a hybrid framework by marrying unique advantages from CVM and CALPHAD through incorporating SRO into CALPHAD with a novel cluster-based solution model. The key is to use the Fowler-Yang-Li transform to decompose the cumbersome cluster probabilities in CVM into fewer site/point probabilities of the basis cluster, thereby considerably reducing the number of minimizing variables for multicomponent systems. The configurational and non-configurational, for example vibrational and elastic, free energies are modeled separately to gain insight into their respective effects on phase stability. Modern, efficient algorithms are employed to minimize the non-linear cluster-based free energy functions. The resulting codes are implemented in an open-sourced platform, OpenCALPHAD. The predicted chemical SRO is validated in selected alloys with synchrotron X-ray experiments. This hybrid CVM-CALPHAD framework represents a new methodology for thermodynamic modeling that enables atomic-scale order to be exploited as a dimension for materials design, which potentially leads to novel complex concentrated alloys. This CAREER award provides support for education and outreach activities including: mentoring local K-12 students through the NanoDays events at the University of Virginia, holding annual summer programs for underrepresented minority students through the Virginia-North Carolina Alliance for Minority Participation, developing a thermodynamics-related course and learning modules, and creating a Python code library for 3D visualization of concepts in thermodynamics, making this subject fun and intuitive.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.