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The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process…
In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation…
Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, we develop…
Single-atom catalysts have attracted significant attention due to their exceptional atomic utilization and high efficiency in a range of catalytic reactions. However, these systems often face thermodynamic instability, leading to…
In this study, we address the significant challenge of overcoming limitations in catalytic efficiency for the oxygen evolution reaction (OER). The current linear scaling relationships hinder the optimization of electrocatalytic performance.…
Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerated parts of this process, unifying scientific knowledge,…
Structural prediction for the discovery of novel materials is a long sought after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require…
Catalysis has entered everyday life through a number of technological processes relying on different catalytic systems. The increasing demand for such systems requires rationalization of the use of their expensive components, like noble…
The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for this include time-consuming and expensive experimental trial-and-error approaches in labs based…
Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of…
Single-atom catalysts (SACs) have attracted ever-growing interest due to their high atom-utilization efficiency and potential for cost-effective of hydrogen production. However, enhancing the hydrogen evolution reaction (HER) performance…
Single-atom catalysts (SACs) with metal-nitrogen-carbon (M-N-C) structures are widely recognized as promising candidates in oxygen reduction reactions (ORR). According to the classical Sabatier principle, optimal 3d metal catalysts, such as…
Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles…
Surface modification of TiO2 with single-atom catalysts (SACs) is an effective strategy for enhancing photocatalytic efficiency. However, thorough characterization of SACs at the atomic scale remains challenging. X-ray absorption…
Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors…
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…
Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated…
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants,…
Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys…
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces…