Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Data-driven approaches for structure-property relationships in polymer science for prediction and understanding
Y Amamoto - Polymer Journal, 2022 - nature.com
In this review, recent developments in data-driven approaches for structure-property
relationships in polymer science are introduced. Understanding the structure-property …
relationships in polymer science are introduced. Understanding the structure-property …
Polymers Simulation using Machine Learning Interatomic Potentials
T Long, J Li, CL Wang, H Wang, X Cheng, H Lu… - Polymer, 2024 - Elsevier
Polymers are essential in a wide range of industrial and everyday applications due to their
unique properties. However, traditional simulation methods such as molecular dynamics …
unique properties. However, traditional simulation methods such as molecular dynamics …
Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials
J Zheng, X Sun, J Hu, SB Wang, Z Yao… - … Applied Materials & …, 2021 - ACS Publications
Two-dimensional (2D) materials have been developed into various catalysts with high
performance, but employing them for develo** highly stable and active nonprecious …
performance, but employing them for develo** highly stable and active nonprecious …
Molecular insights into the adsorption and penetration of oil droplets on hydrophobic membrane in membrane distillation
S Yuan, X Yang, N Zhang, J Zhang, S Yuan, Z Wang - Water Research, 2024 - Elsevier
Membrane fouling induced by oily substances significantly constrains membrane distillation
performance in treating hypersaline oily wastewater. Overcoming this challenge …
performance in treating hypersaline oily wastewater. Overcoming this challenge …
Proton transport in perfluorinated ionomer simulated by machine-learned interatomic potential
R **nouchi, S Minami, F Karsai, C Verdi… - The Journal of …, 2023 - ACS Publications
Polymers are a class of materials that are highly challenging to deal with using first-
principles methods. Here, we present an application of machine-learned interatomic …
principles methods. Here, we present an application of machine-learned interatomic …
Advances in develo** thermally conductive polymers
Polymers, with various advantages including lightweight, low cost, flexibility and ease of
processing, are popular choices for thermal management in flexible electronics …
processing, are popular choices for thermal management in flexible electronics …
Enhancing the quality and reliability of machine learning interatomic potentials through better reporting practices
Recent developments in machine learning interatomic potentials (MLIPs) have empowered
even nonexperts in machine learning to train MLIPs for accelerating materials simulations …
even nonexperts in machine learning to train MLIPs for accelerating materials simulations …
Crystallization of h-BN by molecular dynamics simulation using a machine learning interatomic potential
YQ Liu, HK Dong, Y Ren, WG Zhang, W Chen - Computational Materials …, 2025 - Elsevier
This study employs machine learning-driven molecular dynamics simulations to investigate
the structure and physical properties of hexagonal boron nitride (h-BN) across a wide …
the structure and physical properties of hexagonal boron nitride (h-BN) across a wide …
Machine Learning in Computer Aided Engineering
FJ Montáns, E Cueto, KJ Bathe - Machine Learning in Modeling and …, 2023 - Springer
The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …
promoted its introduction in more analytical engineering fields, improving or substituting …
From organic fragments to photoswitchable catalysts: The OFF–ON structural repository for transferable kernel-based potentials
F Célerse, MD Wodrich, S Vela, S Gallarati… - Journal of Chemical …, 2024 - ACS Publications
Structurally and conformationally diverse databases are needed to train accurate neural
networks or kernel-based potentials capable of exploring the complex free energy …
networks or kernel-based potentials capable of exploring the complex free energy …