Ligand Design in Atomically Precise Copper Nanoclusters and Their Application in Electrocatalytic Reactions

Y Liu, J Yu, Y Lun, Y Wang, Y Wang… - Advanced Functional …, 2023 - Wiley Online Library
Metal nanoclusters (MNCs) are compositionally well‐defined and also structurally precise
materials with unique molecule‐like properties and discrete electronic energy levels …

Machine learning interatomic potentials for heterogeneous catalysis

D Tang, R Ketkaew, S Luber - Chemistry–A European Journal, 2024 - Wiley Online Library
Atomistic modeling can provide valuable insights into the design of novel heterogeneous
catalysts as needed nowadays in the areas of, eg, chemistry, materials science, and biology …

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys

YX Guo, YB Zhuang, J Shi, J Cheng - The Journal of Chemical Physics, 2023 - pubs.aip.org
Semiconductor alloy materials are highly versatile due to their adjustable properties;
however, exploring their structural space is a challenging task that affects the control of their …

Advancements and challenges in the integration of indium arsenide and van der Waals heterostructures

T Cheng, Y Meng, M Luo, J Xian, W Luo, W Wang… - Small, 2024 - Wiley Online Library
The strategic integration of low‐dimensional InAs‐based materials and emerging van der
Waals systems is advancing in various scientific fields, including electronics, optics, and …

Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics

L Guo, T Jin, S Shan, Q Tang, Z Li, C Wang… - The Journal of …, 2023 - pubs.aip.org
AgPd nanoalloys often undergo structural evolution during catalytic reactions; the
mechanism underlying such restructuring remains largely unknown due to the use of …

Hierarchical structures and magnetism of Co clusters: a perspective from integration of deep learning and a hybrid differential evolution algorithm

WH Yang, FQ Yu, ZW Guo, R Huang, JR Chen, FQ Gao… - Nanoscale, 2024 - pubs.rsc.org
Theoretically determining the lowest-energy structure of a cluster has been a persistent
challenge due to the inherent difficulty in accurate description of its potential energy surface …

DFT-Based Study of the Structure, Stability, and Spectral and Optical Properties of Gas-Phase NbMgn (n = 2–12) Clusters

XF Gao, GH Liu, XK Hu, LL Chen, BC Zhu… - ACS …, 2023 - ACS Publications
Gas-phase NbMg n (n= 2–12) clusters were fully searched by CALYPSO software, and then
the low-energy isomers were further optimized and calculated under DFT. It is shown that …

Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning

FQ Gong, K Xiong, J Cheng - National Science Open, 2024 - nso-journal.org
Nanoparticles, distinguished by their unique chemical and physical properties, have
emerged as focal points within the realm of materials science. Traditional theoretical …

Machine learning force field study of carboxylate ligands on the surface of zinc-blende CdSe quantum dots

H Zhang, B Cao, L Huang, X Peng, L Wang - Nano Research, 2024 - Springer
In colloidal quantum dots (QDs), the geometries of surface ligands may play significant roles
in tuning the electronic structure, optical spectra and exciton dynamics. We here propose an …

Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential

L Guo, S Shan, X Liu, W Zhang, P Xu, F Ma… - The Journal of …, 2024 - pubs.aip.org
The design of heterogeneous catalysts generally involves optimizing the reactivity descriptor
of adsorption energy, which is inevitably governed by the structure of surface-active sites. A …