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High-Entropy/multicomponent rare-earth oxides (HECs and MCCs) show promise as alternative materials for thermal barrier coatings (TBC) with the ability to tailor properties based on the combination of rare-earth elements present. By…
Ring attractors, mathematical models inspired by neural circuit dynamics, provide a biologically plausible mechanism to improve learning speed and accuracy in Reinforcement Learning (RL). Serving as specialized brain-inspired structures…
Coupled spin-lattice dynamics (SLD) underlie a wide range of magnetic phenomena, yet a unified first-principles framework that propagates both degrees of freedom without empirical parameterization has remained elusive. We present a fully ab…
A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline…
Modern drug discovery is often time-consuming, complex and cost-ineffective due to the large volume of molecular data and complicated molecular properties. Recently, machine learning algorithms have shown promising results in virtual…
Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important…
We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $\kappa_L$. Several cadmium (Cd) compounds containing elements from the…
A fully relativistic description of the spin-orbit induced spin Hall effect is presented that is based on Kubo's linear response formalism. Using an appropriate operator for the spin current density a Kubo-St\v{r}eda-like equation for the…
Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning…
Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety.…
The more-than-6000 2D materials predicted thus far provide a huge combinatorial space for forming functional heterostructures with bulk materials, with potential applications in nanoelectronics, sensing, and energy conversion. In this work,…
The coexistence and competition of superconductivity and magnetism can lead to a variety of rich physics and technological applications. Recent discovery of atomic-layer superconductors and self-assembly of magnetic molecules on solid…
Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, particularly in data-scarce regimes where conventional data-driven approaches are least…
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning…
The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic…
Additive manufacturing has become one of the forefront technologies in fabrication, enabling new products impossible to manufacture before. Although many materials exist for additive manufacturing, they typically suffer from performance…
Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic arrangements, typically decomposed into local atomic environments. This approach, while well-suited for…
The active-space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial, but a…
Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally…
Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of…