arXiv subjects

Garritt J. Tucker

Publications and source records attributed to Garritt J. Tucker.

5 recordsLinked to original sources

arXiv2026

Chemical short-range order controls deformation pathways in a complex concentrated alloy

Chemical short-range order (CSRO) is an intrinsic feature of complex concentrated alloys (CCAs), yet its influence on deformation mechanisms is controversial because of the inconclusive state of concurrent CSRO quantification during deformation. Here, we provide experimental evidence that CSRO acts as an intrinsic thermodynamic state variable governing stacking-fault energetics and deformation pathways in a Co30Cr40Ni30 alloy. By comparing quenched (CSRO-lean) and aged (CSRO-enriched) conditions with equivalent grain structure and phase constitution, we isolate the influence of atomic-scale chemical ordering on mechanical behavior. Calorimetry confirms reversible CSRO formation, while synchrotron X-ray diffraction and electron microscopy reveal that CSRO suppresses deformation-induced fcc-hcp martensitic transformation at both room and cryogenic temperatures. Despite differences in transformation dynamics, the macroscopic tensile response is still broadly similar. Atomistic simulations show that CSRO increases both stable and unstable stacking-fault energies, raising the energetic barrier for partial-dislocation activity and stabilizing the fcc lattice against transformation. Together, the experimental and computational results establish CSRO as an added degree of freedom for tuning stacking-fault energetics and controlling deformation pathways in complex concentrated alloys.

cond-mat.mtrl-sci↗

arXiv2025

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti$_3$C$_2$T$_X$ MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures--from isolated vacancies to nanopores--revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

cond-mat.mtrl-sci↗

arXiv2024

Describe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters

Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine learning, but representing a grain boundary structure in a manner suitable for machine learning is not a trivial task. There are three key steps common to property prediction in grain boundaries and other variable-sized atom clustered structures. These are: (1) describe the atomic structure as a feature matrix, (2) transform the variable-sized feature matrices of different structures to a fixed length common to all structures, and (3) apply machine learning to predict properties from the transformed feature matrices. We examine these feature engineering steps to understand how they impact the accuracy of grain boundary energy predictions. A database of over 7000 grain boundaries serves to evaluate the different feature engineering combinations. We also examine how these combination of engineered features provide interpretability, or the ability to extract insightful physics from the obtained structure-property relationships.

cond-mat.mtrl-sci↗

arXiv2022

Band gap analysis and carrier localization in cation-disordered ZnGeN$_2$

Cation site disorder provides a degree of freedom in the growth of ternary nitrides for tuning the technologically relevant properties of a material system. For example, the band gap of ZnGeN$_2$ changes when the ordering of the structure deviates from that of its ground state. By combining the perspectives of carrier localization and defect states, we analyze the impact of different degrees of disordering on electronic properties in ZnGeN$_2$, addressing a gap in current studies which focus on dilute or fully disordered systems. The present study demonstrates changes in the density of states and localization of carriers in ZnGeN$_2$ calculated using band gap-corrected density functional theory and hybrid calculations on partially disordered supercells generated using the Monte Carlo method. We use localization and density of states to discuss the ill-defined nature of a band gap in a disordered material, comparing multiple definitions of the energy gap in the context of theory and experiment. Decreasing the order parameter results in a large reduction of the band gap in disordered cases. The reduction in band gap is due in part to isolated, localized states that form above the valence band continuum and are associated with nitrogen coordinated by more zinc than germanium. The prevalence of defect states in all but the perfectly ordered structure creates challenges for incorporating disordered ZnGeN$_2$ into optical devices, but the localization associated with these defects provides insight into mechanisms of electron/hole recombination in the material.

cond-mat.mtrl-sci↗

arXiv2014

A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [arXiv:0910.1019]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum coefficients.

cond-mat.mtrl-sci↗