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Gaussian stochastic process (GaSP) has been widely used as a prior over functions due to its flexibility and tractability in modeling. However, the computational cost in evaluating the likelihood is $O(n^3)$, where $n$ is the number of…

Machine-learned potential-driven molecular dynamics (MLMD) simulations are of great value in guiding the design and optimization of memory devices. Amorphous indium-tin-oxide (ITO) is widely used as transparent conducting oxide for…

Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction of the computational cost and enabling the simulation of…

Novel wide-band-gap semiconductors are needed for next-generation power electronic but there is a gap between a promising material and a functional device. Finding stable contacts is one of the major challenges, which is currently dealt…

Faithful quantum state transfer between telecom photons and microwave frequency mechanical oscillations necessitate a fast conversion rate and low thermal noise. Two-dimensional (2D) optomechanical crystals (OMCs) are favorable candidates…

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is their…

Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic…

Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is…

Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces (PESs) very accurately at a fraction of computational cost.…

Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-the-art models enforce roto-translational symmetries through…

Oxidation of two-dimensional (2D) layered materials has proven advantageous in creating oxide/2D material heterostructures, opening the door for a new paradigm of low-power electronic devices. Gallium (II) sulfide ($\beta$-GaS), a hexagonal…

Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here,…

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with…

Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods largely rely on contrastive learning to maximize cross-modal…

We demonstrate a method of making a very shallow, gateable, undoped 2-dimensional electron gas. We have developed a method of making very low resistivity contacts to these structures and systematically studied the evolution of the mobility…

Using machine learning (ML) approach, we unearthed a new III-V semiconducting material having an optimal bandgap for high efficient photovoltaics with the chemical composition of Gallium-Boron-Phosphide(GaBP$_2$, space group: Pna2$_1$). ML…

V$_2$O$_5$ is a promising battery electrode material that can intercalate not only Li, but also more abundant alkaline metals such as Na and K, and even multivalent ions such as Al, Ca, Cu, Mg, and Zn. V$_2$O$_5$ exhibits several different…

We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential approach, we present LC-GAP, a new system for generating…

Beta phase gallium oxides, an ultrawide-bandgap semiconductor, has great potential for future power and RF electronics applications but faces challenges in bipolar device applications due to the lack of p-type dopants. In this work, we…

Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor…