Xie and Hsu Reply

Xie, Rui-Hua; Hsu, Paul S.

doi:10.1103/physrevlett.100.159302

Cited by 13 publications

(26 citation statements)

References 4 publications

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“…More details about the exchange between the Wick-type stochastic equation and the usual partial differential equation can be found in Holden et al [3] and Xie [4].…”

Section: Some Basic Concepts On Wick-type and Main Steps Of Finding Smentioning

confidence: 98%

Exact solutions for a variable coefficient and Wick-type stochastic long–short wave resonance equation

Sun

Gao

2009

Communications in Nonlinear Science and Numerical Simulation

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“…More details about the exchange between the Wick-type stochastic equation and the usual partial differential equation can be found in Holden et al [3] and Xie [4].…”

Section: Some Basic Concepts On Wick-type and Main Steps Of Finding Smentioning

confidence: 98%

Exact solutions for a variable coefficient and Wick-type stochastic long–short wave resonance equation

Sun

Gao

2009

Communications in Nonlinear Science and Numerical Simulation

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“…Within the algorithms they tested, extremely randomized trees provided the best performance. Most recent research [121] focuses on graph convolutional neural networks, instead of constructing vector matrix to represent the structures, graphs of atoms connection are used to train the multilayer neural network to predict the formation energies with a reasonable mean absolute error of 130 meV/atom. Other researches took both featurization and algorithms implementation into consideration: Gossett et al [122] built a machine learning workflow/pipeline (AFLOW-ML) using raw materials data as input and electronic, thermal and mechanical properties as outputs.…”

Section: Materials Stabilitymentioning

confidence: 99%

Machine Learning for Computational Heterogeneous Catalysis

et al. 2019

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Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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“…11 As a result, F, G, a 1 2 , and a 2 surely satisfy the universal relations ͑4͒-͑7͒ with respect to ⌬. 11͒.…”

mentioning

confidence: 94%

“…1,10,11 To define this potential, a perfect scaling procedure for diatomic potentials is needed. 1,10,11 To define this potential, a perfect scaling procedure for diatomic potentials is needed.…”

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confidence: 99%

Universal scaling features of spectroscopic constants for diatomic systems

Xie

2006

The Journal of Chemical Physics

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Based on a new criterion that was proposed to search for the universality of spectroscopic constants for bound ground-state diatomics [R. H. Xie and P. S. Hsu, Phys. Rev. Lett. 96, 243201 (2006)], we have found universal scaling relations between spectroscopic constants of diatomic systems with s-, p-, and d-type valence-shell constituents. Our study suggests a useful empirical approach for the prediction of molecular spectroscopic constants.

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Xie and Hsu Reply

Cited by 13 publications

References 4 publications

Exact solutions for a variable coefficient and Wick-type stochastic long–short wave resonance equation

Exact solutions for a variable coefficient and Wick-type stochastic long–short wave resonance equation

Machine Learning for Computational Heterogeneous Catalysis

Universal scaling features of spectroscopic constants for diatomic systems

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