Catalytic reactions of oxalic acid degradation with Pt/SiO<sub>2</sub> as a catalyst in nitric acid solutions

Hao, Shuai; Li, B.; Liu, Zhanyuan; Huang, Wenlong; Jiang, Dongmei; Xia, Liangshu

doi:10.1039/d3ra01244a

RSC Adv.

2023

DOI: 10.1039/d3ra01244a

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Catalytic reactions of oxalic acid degradation with Pt/SiO₂ as a catalyst in nitric acid solutions

Shuai Hao

B. Li

Zhanyuan Liu

et al.

Abstract: Large quantities of solutions containing oxalic acid and nitric acid are produced from nuclear fuel reprocessing, but oxalic acid must be removed before nitric acid and plutonium ions can be recovered in these solutions.

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An overview about neural networks potentials in molecular dynamics simulation

Martin‐Barrios,

Navas‐Conyedo,

Zhang

et al. 2024

Int J of Quantum Chemistry

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Ab‐initio molecular dynamics (AIMD) is a key method for realistic simulation of complex atomistic systems and processes in nanoscale. In AIMD, finite‐temperature dynamical trajectories are generated by using forces computed from electronic structure calculations. In systems with high numbers of components a typical AIMD run is computationally demanding. On the other hand, machine learning (ML) is a subfield of the artificial intelligence that consist in a set of algorithms that show learning by experience with the use of input and output data where algorithms are capable of analysing and predicting the future. At present, the main application of ML techniques in atomic simulations is the development of new interatomic potentials to correctly describe the potential energy surfaces (PES). This technique is in constant progress since its inception around 30 years ago. The ML potentials combine the advantages of classical and Ab‐initio methods, that is, the efficiency of a simple functional form and the accuracy of first principles calculations. In this article we review the evolution of four generations of machine learning potentials and some of their most notable applications. This review focuses on MLPs based on neural networks. Also, we present a state of art of this topic and future trends. Finally, we report the results of a scientometric study (covering the period 1995–2023) about the impact of ML techniques applied to atomistic simulations, distribution of publications by geographical regions and hot topics investigated in the literature.

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An overview about neural networks potentials in molecular dynamics simulation

Martin‐Barrios,

Navas‐Conyedo,

Zhang

et al. 2024

Int J of Quantum Chemistry

View full text Add to dashboard Cite

show abstract

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Catalytic reactions of oxalic acid degradation with Pt/SiO₂ as a catalyst in nitric acid solutions

Abstract: Large quantities of solutions containing oxalic acid and nitric acid are produced from nuclear fuel reprocessing, but oxalic acid must be removed before nitric acid and plutonium ions can be recovered in these solutions.

Cited by 1 publication

References 41 publications

An overview about neural networks potentials in molecular dynamics simulation

An overview about neural networks potentials in molecular dynamics simulation

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Catalytic reactions of oxalic acid degradation with Pt/SiO2 as a catalyst in nitric acid solutions

Abstract: Large quantities of solutions containing oxalic acid and nitric acid are produced from nuclear fuel reprocessing, but oxalic acid must be removed before nitric acid and plutonium ions can be recovered in these solutions.

Cited by 1 publication

References 41 publications

An overview about neural networks potentials in molecular dynamics simulation

An overview about neural networks potentials in molecular dynamics simulation

Contact Info

Product

Resources

About

Catalytic reactions of oxalic acid degradation with Pt/SiO₂ as a catalyst in nitric acid solutions