Predicting Phase Transitions in Correlated Topological Quantum Materials

One of the most promising route for manipulating the properties of correlated solids for technological applications is through controlled perturbations via atomic-defects, doping, stoichiometry, strain as well as heterostructuring. However, the mechanisms that drive the electronic, magnetic and/or topological transitions in these materials and the specific role of these perturbations is not fully understood. For example, the perovskite SrCoO₃ is a ferromagnetic metal, while the oxygen-deficient (n-doped) brownmillerite SrCoO_2.5 is an antiferromagnetic insulator. Similarly, 2D topological materials such as MnBi₂Te₄ and WTe₂show different topological phases depending on how they are stacked or heterostructured. Inducing local strain via ion-implantation or dimensional confinement can modify magnetic and related properties in correlated metals such as PdCoO₂ and TbMn₆Sn₆. The challenge in predicting and understanding these behaviors from the intricate couplings of charge, spin, orbital, and lattice degrees of freedom. These at times challenge standard modeling approaches, requiring either significant empiricism or adoption of new methodologies to make progress. As such, in addition to using density functional theory, we outline our use of the highly accurate ab initio quantum Monte Carlo (QMC) approach to address these challenges. To control computational costs, we have developed a protocol of using QMC results to validate more scalable approaches via magnetic moments, charge densities, and thermodynamic properties. Building on our earlier work predicting metal-insulator transitions in bulk and heterostructures of correlated oxides such as VO₂[1,2] and correlated-perovskites[3], we will present the role of stacking-faults in inducing topological transitions in MnBi₂Te₄ [4], how ion-implantation can lead to novel magnetic transitions in PdCoO₂ [5] delafossite systems, and effects of dimensional confinement in realizing the Chern phase in TbMn₆Sn₆ [6], following this protocol.<br/><br/>This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.<br/><br/>References:<br/>1. P. Ganesh et al. . Physical Review B 101 155129 (2020).<br/>2. Q. Lu et al. Scientific Reports 10 1 (2020).<br/>3. M. Bennett et al. Physical Review Research Letters 4 L022005 (2022).<br/>4. Jeonghwan Ahn et al., <i>J. Phys. Chem. Lett.</i> 14, 40, 9052–9059 (2023)<br/>5. M. Brahlak et al., <i>Nano Lett.</i> 23, 16, 7279–7287 (2023)<br/>6. A. Annaberdiyev et al., npj Quantum Materials volume 8, Article number: 50 (2023)