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The hydrogen evolution reaction (HER) is central to sustainable hydrogen production, and nitrogen coordinated dual atom catalysts (DACs) offer a promising route to noble metal activity at low cost. Yet their vast compositional and…
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors…
Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass…
Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety.…
Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and…
Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of…
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…
Directional detection of Dark Matter (DM) particles could be accomplished by studying either ion or electron recoils in large arrays of parallel carbon nanotubes. For instance, a MeV mass DM particle could scatter off a lattice electron,…
The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics of graphene and a (8,0) carbon nanotube (CNT) decorated with…
Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this…
The use of pressure to obtain new materials that can be recovered under ambient conditions is a central problem in high-pressure physics. Despite decades of research, this goal has only been achieved in the laboratory for a few notable…
This paper presents the first comprehensive literature review of deep learning (DL) applications in additive manufacturing (AM). It addresses the need for a thorough analysis in this rapidly growing yet scattered field, aiming to bring…
The most recent concern of all people on Earth is the increase in the concentration of greenhouse gas in the atmosphere. The concentration of these gases has risen rapidly over the last century and if the trend continues it can cause many…
We present a comprehensive framework for interpreting electron recoil signals induced by fast-moving dark matter (DM), applicable across a wide range of theoretically motivated models. Amid both null results in conventional weakly…
Understanding excitonic effects in two-dimensional (2D) materials is critical for advancing their potential in next-generation electronic and photonic devices. In this study, we introduce a machine learning (ML)-based framework to predict…
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term…
Additive manufacturing has become one of the forefront technologies in fabrication, enabling new products impossible to manufacture before. Although many materials exist for additive manufacturing, they typically suffer from performance…
The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains…
The urgent need to mitigate rising atmospheric CO2 levels motivates the search for stable, efficient, and tunable adsorbent materials. In this study, we employ first-principles density functional theory to investigate the adsorption of CO2…
Direct conversion of carbon dioxide (CO2) to high-energy fuels and high-value chemicals is a fascinating sustainable strategy. For most of the current electrocatalysts for CO2 reduction, however, multi-carbon products are inhibited by large…