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Density functional theory (DFT) underpins modern atomistic simulations of transition-metal surfaces. It can predict key properties linked to catalytic performance, such as adsorption energies and barrier heights, enabling new paradigms in…

Materials Science · Physics 2026-03-23 Benjamin X. Shi , Timothy C. Berkelbach

We developed a facile and scalable 3-step hydrothermal, electrodeposition, and annealing technique to synthesize a variety of nanowire heterostructures. The heterojunction catalysts of CP-ZnO-MoS2 and CP-ZnO-ZnS-MoS2 both saw an increase in…

Materials Science · Physics 2025-03-10 Lee Kendall , Dawn Ford , Giovanni Zangari , Stephen McDonnell

Electrochemical CO2 reduction reaction (CO2RR) using 2D nanomaterials has emerged as a sophisticated approach to mitigate industrial CO2 emissions. In this work, the potential application of pristine as well as strategically Fe, Co,…

Materials Science · Physics 2025-09-23 Md. Mostaqul Islam , Ahmed Zubair

Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge,…

The design of inorganic catalysts and the prediction of their catalytic efficiency are fundamental challenges in chemistry and materials science. Traditional catalyst evaluation methods primarily rely on machine learning techniques;…

Machine Learning · Computer Science 2025-03-11 Zhangdi Liu , Ling An , Mengke Song , Zhuohang Yu , Shan Wang , Kezhen Qi , Zhenyu Zhang , Chichun Zhou

Metal-organic frameworks (MOFs) provide a versatile and tailorable material platform that embody many desirable attributes for photocatalytic water-splitting. The approach taken in this study was to use Density Functional Theory (DFT) to…

Chemical Physics · Physics 2015-10-19 Terence Musho , Jiangtan Li , Nianqiang Wu

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods $\Delta\Delta G$ are suitable for large-scale mutant screening,…

Biomolecules · Quantitative Biology 2024-09-19 Kairi Furui , Masahito Ohue

The CO_{2} electro-reduction reaction (CORR) is a promising avenue to convert greenhouse gases into high-value fuels and chemicals, in addition to being an attractive method for storing intermittent renewable energy. Although…

The concept of chemical bonding is a crucial aspect of chemistry that aids in understanding the complexity and reactivity of molecules and materials. However, the interpretation of chemical bonds can be hindered by the choice of the…

Computational Physics · Physics 2024-01-09 Toni Oestereich , Ralf Tonner-Zech , Julia Westermayr

Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as…

Materials Science · Physics 2022-09-16 Chen Liang , Bowen Wang , Shaogang Hao , Guangyong Chen , Pheng-Ann Heng , Xiaolong Zou

Identifying low-energy adsorption geometries on catalytic surfaces is a practical bottleneck for computational heterogeneous catalysis: the difficulty lies not only in the cost of density functional theory (DFT) but in proposing initial…

Machine Learning · Computer Science 2026-02-24 Jiangjie Qiu , Wentao Li , Honghao Chen , Leyi Zhao , Xiaonan Wang

CoPt nanoparticle catalysts are integral to commercial fuel cells. Such systems are prohibitive to fully characterize with electronic structure calculations. Machine-learned potentials offer a scalable solution; however, such potentials are…

Materials Science · Physics 2024-09-12 Cheng Zeng , Sushree Jagriti Sahoo , Andrew J. Medford , Andrew A. Peterson

Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study, a high corrosion resistance. In the…

The ability to efficiently evolve hydrogen via electrocatalysis at low overpotentials holds tremendous promise for clean energy. Hydrogen evolution reaction (HER) can be easily achieved from water if a voltage above the thermodynamic…

The rational design of transition metal dichalcogenide electrocatalysts for efficiently catalyzing hydrogen evolution reaction (HER) is believed to lead to the generation of a renewable energy carrier. To this end our work has made three…

This article reports a concept of heat-induced topological modifications of non-layered WO 3 followed by successful synthesis of oxygen-vacant more-porous nanosheets with exposed active (002) facet. Experimental measurements and Density…

The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated three components of elastic modulus with Molecular Dynamics…

Discovering heterogeneous catalysts tailored for specific reaction intermediates remains a fundamental bottleneck in materials science. While traditional trial-and-error methods and recent generative models have shown promise, they struggle…

Materials Science · Physics 2026-05-19 Minkyu Kim , Nayoung Kim , Honghui Kim , Sungsoo Ahn

The "CO adsorption puzzle", a persistent failure of utilizing generalized gradient approximations (GGA) in density functional theory to replicate CO's experimental preference for top-site adsorption on transition-metal surfaces, remains a…

Materials Science · Physics 2025-07-30 Xinyuan Liang , Renxi Liu , Mohan Chen

Adsorption and catalytic properties of the polar (111) surface of transition-metal carbides (TMC's) are investigated by density-functional theory. Atomic and molecular adsorption are rationalized with the concerted-coupling model, in which…

Computational Physics · Physics 2010-05-05 Aleksandra Vojvodic , Anders Hellman , Carlo Ruberto , Bengt I. Lundqvist