Publications

The metastable layered compound MnBi2Te4 is the first experimentally realized intrinsic antiferromagnetic topological insulator, predicted to host the quantum anomalous Hall effect at high temperatures upon exfoliation to atomically thin layers. While its magnetic ordering and topological properties have generated intensive interest, the mechanism behind its metastability and the ideal crystal synthesis conditions have remained elusive. Here, using a combined first-principles-based approach that considers lattice, charge, and spin degrees of freedom, we investigate the metastability of MnBi2Te4 by calculating the Helmholtz free energy for the reaction Bi2Te3 + MnTe -> MnBi2Te4. We identify a narrow temperature range (767 K to 873 K) in which the compound is stable with respect to the competing binary phases and successfully synthesize high-quality MnBi2Te4 single crystals using the Bi-Te flux method within this range. We also predict the various contributions to the total specific heat, which is consistent with our experimental measurements. Our findings indicate that the degrees of freedom responsible for the van der Waals interaction, magnetic coupling, and nontrivial band topology in layered materials not only enable emergent phenomena but also determine thermodynamic stability. This conclusion lays the foundation for future computational material synthesis of novel layered systems.

Herein we report on using Egyptian blue as an anode material for Li-ion batteries. A 1^st cycle lithiation capacity of 594 mA h g^-1 and reversible capacity of 210 mA h g^-1 at 20 mA g^-1, and at 500 mA g^-1 a reversible capacity of 120 mA h g^-1 (stable over 1000 cycles) were achieved with coulombic efficiency more than 99.5%. Using X-ray diffraction, and FTIR and X-ray absorption spectroscopies we found that the material goes through a conversion reaction during the 1^st cycle that results in the formation of amorphous mixed oxides with copper nanoclusters.

VO2 is renowned for its electric transition from an insulating monoclinic (M1) phase, characterized by V-V dimerized structures, to a metallic rutile (R) phase above 340 K. This transition is accompanied by a magnetic change: the M1 phase exhibits a non-magnetic spin-singlet state, while the R phase exhibits a state with local magnetic moments. Simultaneous simulation of the structural, electric, and magnetic properties of this compound is of fundamental importance, but the M1 phase alone has posed a significant challenge to the density functional theory (DFT). In this study, we show none of the commonly used DFT functionals, including those combined with on-site Hubbard U to treat 3d electrons better, can accurately predict the V-V dimer length. The spin-restricted method tends to overestimate the strength of the V-V bonds, resulting in a small V-V bond length. Conversely, the spin-symmetry-breaking method exhibits the opposite trends. Each of these two bond-calculation methods underscores one of the two contentious mechanisms, i.e., Peierls lattice distortion or Mott localization due to electron-electron repulsion, involved in the metal-insulator transition in VO2. To elucidate the challenges encountered in DFT, we also employ an effective Hamiltonian that integrates one-dimensional magnetic sites, thereby revealing the inherent difficulties linked with the DFT computations.

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In this work, we demonstrate that contrastive learning is a computationally efficient and flexible method to incorporate a physical constraint in ML-based density functional design. We propose a schematic approach to incorporate the uniform density scaling property of electron density for exchange energies by adopting contrastive representation learning during the pretraining task. The pretrained hidden representation is transferred to the downstream task to predict the exchange energies calculated by DFT. The electron density encoder transferred from the pretraining task based on contrastive learning predicts exchange energies that satisfy the scaling property, while the model trained without using contrastive learning gives poor predictions for the scaling-transformed electron density systems. Furthermore, the model with pretrained encoder gives a satisfactory performance with only small fractions of the whole augmented dataset labeled, comparable to the model trained from scratch using the whole dataset. The results demonstrate that incorporating exact constraints through contrastive learning can enhance the understanding of density-energy mapping using neural network (NN) models with less data labeling, which will be beneficial to generalizing the application of NN-based XC functionals in a wide range of scenarios that are not always available experimentally but theoretically justified. This work represents a viable pathway toward the machine learning design of a universal density functional via representation learning.

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice I{\it h} at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.