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Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, and this set is screened for candidate materials for specific applications. In contrast, many…
The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not…
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical…
Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity…
We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential…
Boosting the sensitivity of solid-state gas sensors by incorporating nanostructured materials as the active sensing element can be complicated by interfacial effects. Interfaces at nanoparticles, grains, or contacts may result in non-linear…
Context: Concurrent objects with asynchronous messaging are an increasingly popular way to structure highly available, high performance, large-scale software systems. To ensure data-consistency and support synchronization between objects…
Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery…
Single atom catalysts (SACs) based on 2D materials are identified to be efficient in many catalytic reaction. In this work, the catalytic performance of Pd/Pt embedded planar carbon nitride (CN) for CO oxidation, has been investigated via…
Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening…
Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally…
The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potential of repurposing these ATCs in advanced reactor designs,…
A relativistic version of the coupled-cluster single-double (CCSD) method is developed for atoms with a single valence electron. In earlier work, a linearized version of the CCSD method (with extensions to include a dominant class of triple…
Discrete choice experiments (DCEs) investigate the attributes that influence individuals' choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that…
Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and…
Alloy nanocatalysts have found broad applications ranging from fuel cells to catalytic converters and hydrogenation reactions. Despite extensive studies, identifying the active sites of nanocatalysts remains a major challenge due to the…
Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt power, and nanosecond-scale speed while providing clear…
Density functional theory based global geometry optimization has been used to demonstrate the crucial influence of the geometry of the catalytic cluster on the energy barriers for the CO oxidation reaction over Pd-based bimetallic…
We demonstrate an ab-initio predictive approach to computing the thermal conductivity ($\kappa$) of InAs/GaAs superlattices (SLs) of varying period, thickness, and composition. Our new experimental results illustrate how this method can…
Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current AI workflows optimize performance first, deferring sustainability…