Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials.

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.

Anomalous magnetoresistance by breaking ice rule in Bi2Ir2O7/Dy2Ti2O7 heterostructure.

While geometrically frustrated quantum magnets host rich exotic spin states with potentials for revolutionary quantum technologies, most of them are necessarily good insulators which are difficult to be integrated with modern electrical circuit. The grand challenge is to electrically detect the emergent fluctuations and excitations by introducing charge carriers that interact with the localized spins without destroying their collective spin states. Here, we show that, by designing a Bi2Ir2O7/Dy2Ti2O7 heterostructure, the breaking of the spin-ice rule in insulating Dy2Ti2O7 leads to a charge response in the conducting Bi2Ir2O7 measured as anomalous magnetoresistance during the field-induced Kagome ice-to-saturated ice transition. The magnetoresistive anomaly also captures the characteristic angular and temperature dependence of this ice-rule-breaking transition, which has been understood as magnetic monopole condensation. These results demonstrate a novel heteroepitaxial approach for electronically probing the transition between exotic insulating spin states, laying out a blueprint for the metallization of frustrated quantum magnets.

Comprehensive Electrical Control of Metamagnetic Transition of a Quasi-2D Antiferromagnet by In Situ Anisotropic Strain.

Effective nonmagnetic control of the spin structure is at the forefront of the study for functional quantum materials. This study demonstrates that, by applying an anisotropic strain up to only 0.05%, the metamagnetic transition field of spin-orbit-coupled Mott insulator Sr2 IrO4 can be in situ modulated by almost 300%. Simultaneous measurements of resonant X-ray scattering and transport reveal that this drastic response originates from the complete strain-tuning of the transition between the spin-flop and spin-flip limits, and is always accompanied by large elastoconductance and magnetoconductance. This enables electrically controllable and electronically detectable metamagnetic switching, despite the antiferromagnetic insulating state. The obtained strain-magnetic field phase diagram reveals that C4 -symmetry-breaking anisotropy is introduced by strain via pseudospin-lattice coupling, directly demonstrating the pseudo-Jahn-Teller effect of spin-orbit-coupled complex oxides. The extracted coupling strength is much weaker than the superexchange interactions, yet crucial for the spontaneous symmetry-breaking, affording the remarkably efficient strain-control.