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Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Electrocatalysis in alkaline media and alkaline membrane-based energy technologies
Hydrogen energy-based electrochemical energy conversion technologies offer the promise
of enabling a transition of the global energy landscape from fossil fuels to renewable energy …
of enabling a transition of the global energy landscape from fossil fuels to renewable energy …
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
Uni-mol: A universal 3d molecular representation learning framework
Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …
role in learning from limited supervised data for applications like drug design. In most MRL …
Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
[HTML][HTML] GPAW: An open Python package for electronic structure calculations
We review the GPAW open-source Python package for electronic structure calculations.
GPAW is based on the projector-augmented wave method and can solve the self-consistent …
GPAW is based on the projector-augmented wave method and can solve the self-consistent …
Prospective de novo drug design with deep interactome learning
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …
chemical and pharmacological properties. We present a computational approach utilizing …
Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
Gaussian process regression for materials and molecules
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
Machine learning for alloys
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …
data-science-inspired work. The dawn of computational databases has made the integration …