Related papers: Automatic diffusion path exploration for multivale…
In this paper, we introduce an approach for the prediction of capacity for over 100,000 spinel compounds relevant for battery materials, from which we propose the 20 most promising candidate materials. In the design of batteries, selecting…
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations…
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to…
Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-defined relationship between central frequency and propagation…
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…
We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal…
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…
Energy carrier transport and recombination in emerging semiconductors can be directly monitored with optical microscopy, leading to the measurement of the diffusion coefficient (D), a critical property for design of efficient optoelectronic…
Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is…
Quantum--Mechanical methods that are both computationally fast and accurate are not yet available for electronic excitations having charge transfer character. In this work, we present a significant step forward towards this goal for those…
The longitudinal nonreciprocal charge transport (NCT) in crystalline materials is a highly non-trivial phenomenon, motivating the design of next generation two-terminal rectification devices (e.g., semiconductor diodes beyond PN junctions).…
Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on…
Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess intrinsic van der Waals gap at the layer interface allowing for remarkable tunability of the optoelectronic features via external intercalation of…
The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs…
We calculate the phonon-dispersion relations of several two-dimensional materials and diamond using the density-functional based tight-binding approach (DFTB). Our goal is to verify if this numerically efficient method provides sufficiently…
Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identification and forecasting approaches become markedly less…
Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Novel pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh…
In this paper, we have built a numerical p-n Si/GaAs heterojunction model using a quantum-mechanical tunneling theory with various quantum tunneling interfacial materials including two-dimensional semiconductors such as hexagonal boron…
The storage mechanism and diffusion kinetics of Na$_{3}$V$_{2-x}$Ni$_{x}$(PO$_{4}$)$_{2}$F$_{3}$/C ($x=$ 0--0.07) cathodes are investigated through electrochemical impedance spectroscopy (EIS), galvanostatic intermittent titration technique…
Rechargeable aqueous zinc-ion batteries (RAZIBs) attract considerable scientific and commercial interest for deployment in grid-scale energy storage due to higher safety and lower manufacturing cost when compared to lithium-ion batteries.…