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Extensive first-principle calculations on embedded clusters containing few O, Y, Ti, and Cr atoms as well as vacancies are performed to obtain interaction parameters to be applied in Metropolis Monte Carlo simulations, within the framework…

Bimetallic nanoparticles are often superior candidates for a wide range of technological and biomedical applications, thanks to their enhanced catalytic, optical, and magnetic properties, which are often better than their monometallic…

Amorphous interfacial complexions have been shown to restrict grain growth and improve damage tolerance in nanocrystalline alloys, with increased chemical complexity stabilizing the complexions themselves. Here, we investigate local…

Structural characterization of nanoscale-two-metal-phase systems, which exhibit partial, complete, or no mixing when co-sputtered with a few percent of a minority element, is extremely challenging. Co-sputtering two metals at room…

Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…

Nanoporous Cu (NPC), synthesized by chemical dealloying of brass, holds significant potential for catalysis of electrochemical CO2 and CO reduction, owing to the optimal binding energy of Cu with *CO and *H intermediates, and the abundance…

Here we explore the use of scanning electron diffraction coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order as a function of position in a complex multicomponent system, a hot rolled,…

In a hierarchical nanopatterning routine relying exclusively on self-assembly processes we combine crystal surface reconstruction, microphase separation of copolymers, and selective metal diffusion to produce monodisperse metal…

Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial…

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…

There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML)…

Ni/Al reactive multilayers are promising materials for applications requiring controlled local energy release and superior mechanical performance. This study systematically investigates the impact of compositional variations, ranging from…

This work demonstrates the development of a strong and ductile medium entropy alloy by employing conventional alloying and thermomechanical processing to induce partial recrystallization (PR) and precipitation strengthening in the…

Surface wettability, governed by both topography and chemistry, plays a critical role in applications such as heat transfer, lubrication, microfluidics, and surface coatings. In this study, we present a machine learning (ML) framework…

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes…

The thermal decomposition behavior, the microstructural evolution and its influence on the mechanical properties of a supersaturated Cu Co solid solution with ~100 nm average grain size prepared by severe plastic deformation is investigated…

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…

Neuromorphic computing systems may be the future of computing and cluster-based networks are a promising architecture for the realization of these systems. The creation and dissolution of synapses between the clusters are of great…

We discuss how simulations of mechanical properties of materials require descriptions at many different length scales --- from the nanoscale where an atomic description is appropriate, through a mesoscale where dislocation based…

This paper proposes a machine learning (ML) method to predict stable molecular geometries from their chemical composition. The method is useful for generating molecular conformations which may serve as initial geometries for saving time…