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We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine…
Strategies for tuning structural and (opto-)electronic properties are fundamental to the rational design of functional materials. Here, we present a molecular design approach for precisely modulating the optoelectronic properties of…
Disorder can have a dominating influence on correlated and quantum materials leading to novel behaviors which have no clean limit counterparts. In magnetic systems, spin and exchange disorder can provide access to quantum criticality,…
Disordered hyperuniform heterogeneous materials are new, exotic amorphous states of matter that behave like crystals in the manner in which they suppress volume-fraction fluctuations at large length scales, and yet are statistically…
An ideal magnetohydrodynamics (MHD) code with adaptive mesh refinement (AMR) was used to investigate the interactions of fast-mode shocks with self-gravitating, isothermal cores with mass-to-flux ratios that are somewhat below the minimum…
Designing efficient, metal free, and in-expensive catalyst for electrochemical hydrogen evolution reaction (HER) is crucial for large scale clean and green energy production. Recently synthesized 1D Biphenylene nanoribbons (BPRs) display…
Graphene has been studied in detail due to its mechanical, electrical, and thermal properties. It is well documented that the introduction of dopants or defects in the lattice can be used to tune material properties for a specific…
The growth dynamics of a single crack in a heterogeneous material under subcritical loading is an intermittent process; and many features of this dynamics have been shown to agree with simple models of thermally activated rupture. In order…
Friction dissipates a substantial portion of global energy, motivating the pursuit of superlubricity, a state of near-zero friction, in real-world systems. Conventional approaches rely on crystalline lattice mismatch to suppress periodic…
In this paper we present a detailed computational study of the electronic structure and optical properties of triply-bonded hydrocarbons with linear, and graphyne substructures, with the aim of identifying their potential in opto-electronic…
X-ray scattering is one of the primary tools to determine crystallographic configuration with atomic accuracy. However, the measurement of ultrafast structural dynamics in monolayer crystals remains a long-standing challenge due to a…
Molybdenum disulfide (MoS2) has emerged as a promising, cost-effective catalyst for hydrogen production via water splitting. We investigate the structural and electrocatalytic properties of MoS2 thin films deposited on different substrates…
Studies aimed at designing new allotropic forms of carbon have received much attention. Recently, a new 2D graphene-like allotrope named Pentahexoctite was theoretically proposed. Pentahexoctite has a metallic signature, and its structure…
Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains…
The low concentration of proton donors in alkaline HER, subsequently leading to the extra water adsorption and dissociation steps, identifies the value of active sites (edge and basal sites) and crystal phases in lowering the extra…
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive…
The development of safe, efficient, and reversible hydrogen storage materials is critical for advancing hydrogen-based energy technologies and achieving carbon-neutral goals. Ennea-Graphene, a new 2D carbon allotrope made of 4-, 5-, 6-, and…
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of…
Motivated by recent in situ studies of carbon nanotube growth from large transition-metal nanoparticles, we study various alpha-iron (ferrite) facets at different carbon concentrations using ab initio methods. The studied (110), (100) and…
Defects in crystalline silicon consisting of a silicon self-interstitial atom and one, two, three, or four hydrogen atoms are studied within density-functional theory (DFT). We search for low-energy defects by starting from an ensemble of…