首页|Studies from Jiangxi Normal University Reveal New Findings on Machine Learning [Molecular Dynamics Simulations of Liquid Gallium Alloy Ga-x (X = Pt, Pd, Rh) via Machine Learning Potentials]
Studies from Jiangxi Normal University Reveal New Findings on Machine Learning [Molecular Dynamics Simulations of Liquid Gallium Alloy Ga-x (X = Pt, Pd, Rh) via Machine Learning Potentials]
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NETL
NSTL
Royal Soc Chemistry
Current study results on Machine Learning have been published. According to news reporting from Nanchang, People's Republic of China, by NewsRx journalists, research stated, "Liquid gallium (Ga) has achieved significant attention across numerous fields in recent decades due to its distinctive physicochemical properties. In particular, the exceptional fluidic nature of liquid Ga makes it an excellent solvent to dissolve transition metals to prepare liquid Ga alloy (LGA) catalytic systems (M. A. Rahim, J. Tang, A. J. Christofferson, P. V. Kumar, N. Meftahi, F. Centurion, Z. Cao, J. Tang, M. Baharfar, M. Mayyas, F.-M. Allioux, P. Koshy, T. Daeneke, C. F. McConville, R. B. Kaner, S. P. Russo and K. Kalantar-Zadeh, Low-Temperature Liquid Platinum Catalyst, Nat." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Jiangxi Normal University, "Chem., 2022, 14, 935-941). Thus, it is an important scientific quest to understand the microscopic structures and properties of transition metal atoms in LGA. Here, we employed a newly developed machine learningbased moment tensor potential (MTP), combined with molecular dynamics simulations, to explore the coordination and diffusion behaviors of transition metal atoms in three LGA systems of Ga-Pt, Ga-Pd, and Ga-Rh. It is observed that the trained MTP can provide accurate descriptions of energies and forces, as well as local structures, for each LGA system. Besides, our simulation results reveal that the average coordination number of the transition metal atom with surrounding Ga atoms follows an order of Ga-Rh ＞Ga-Pt ＞Ga-Pd, while the diffusion coefficient of the transition metal atom in liquid Ga has an inverse order of Ga-Rh ＜Ga-Pt ＜Ga-Pd. This is primarily because the diffusion barrier of Rh in liquid Ga is maximum, yet that of Pd in liquid Ga is minimum. Furthermore, the results of mean square displacement and the van Hove function suggest a normal diffusion mechanism for all three studied transition metal atoms in liquid Ga."
NanchangPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesGalliumHeavy MetalsMachine LearningMolecular DynamicsPhysicsJiangxi Normal University
NETL
NSTL
Royal Soc Chemistry
