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We have investigated the lowest-energy structures and electronic properties of the Au$_n$(n=2-20) clusters based on density functional theory (DFT) with local density approximation. The small Au$_n$ clusters adopt planar structures up to…
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which…
Higher superconducting critical temperature and large-area epsilon-near-zero interfaces are two long-standing goals of Condensed Matter Physics and Optics. Motivated by the recent advancements of experimental interests on metallic…
A computational approach to determine the equilibrium structures of nanoclusters in the whole temperature range from 0 K to melting is developed. Our approach relies on Parallel Tempering Molecular Dynamics (PTMD) simulations complemented…
Two-dimensional (2D) materials and Transition Metal Dichalcogenides (TMD) in particular are at the forefront of nanotechnology. To tailor properties for engineering applications, alloying strategies used for bulk metals in the last century…
We present a general approach to the computational design of nanostructured chemical sensors. The scheme is based on identification and calculation of microscopic descriptors (design parameters) which are used as input to a thermodynamic…
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a…
The genomics approach to materials, heralded by increasingly accurate density functional theory (DFT) calculations conducted on thousands of crystalline compounds, has led to accelerated material discovery and property predictions. However,…
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are…
Recently, sophisticated deep learning-based approaches have been developed for generating efficient initial guesses to accelerate the convergence of density functional theory (DFT) calculations. While the actual initial guesses are often…
Solving the electronic Schr\"odinger equation for strongly correlated systems remains one of the grand challenges in quantum chemistry. Here we demonstrate that Transformer architectures can be adapted to capture the complex grammar of…
The understanding of inorganic reactions, especially those far from the equilibrium state, is relatively limited due to their inherent complexity. Poor understandings on the underlying synthetic chemistry have constrained the design of…
Atomically precise thiolate-stabilized gold nanoclusters are currently of interest for many cross-disciplinary applications in chemistry, physics and molecular biology. Very recently, synthesis and electronic properties of "nanoalloy"…
Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific…
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between…
Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster…
We investigate the properties of clusters formed from two connected $\mathrm{Ge_{m}}$ cage-like clusters, such as experimentally synthesized $\mathrm{Au_3Ge_{18}^{5-}}$, using first-principles DFT methods. We focus particularly on…
CoPt nanoparticle catalysts are integral to commercial fuel cells. Such systems are prohibitive to fully characterize with electronic structure calculations. Machine-learned potentials offer a scalable solution; however, such potentials are…
Packing transitions in the lowest energy structures of Li clusters as a function of size have been identified via simulated annealing. For N>21, the large $p$ character of Li leads to unexpected ionic structures. At N~25, a packing pattern…
Density functional theory (DFT) provides convenient electronic structure methods for the study of molecular systems and materials. Regular Kohn-Sham DFT calculations rely on unitary transformations to determine the ground-state electronic…