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Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Designing microbial cell factories for the production of chemicals
The sustainable production of chemicals from renewable, nonedible biomass has emerged
as an essential alternative to address pressing environmental issues arising from our heavy …
as an essential alternative to address pressing environmental issues arising from our heavy …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Artificial intelligence for retrosynthetic planning needs both data and expert knowledge
Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many
scientific disciplines. In organic chemistry, the challenge of planning complex multistep …
scientific disciplines. In organic chemistry, the challenge of planning complex multistep …
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
We investigated the effect of different training scenarios on predicting the (retro) synthesis of
chemical compounds using text-like representation of chemical reactions (SMILES) and …
chemical compounds using text-like representation of chemical reactions (SMILES) and …
Machine intelligence for chemical reaction space
Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …
accessible chemical space are critical drivers for major technological advances and more …
Self-supervised graph-level representation learning with local and global structure
This paper studies unsupervised/self-supervised whole-graph representation learning,
which is critical in many tasks such as molecule properties prediction in drug and material …
which is critical in many tasks such as molecule properties prediction in drug and material …
Deep retrosynthetic reaction prediction using local reactivity and global attention
As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways
and intermediates for a target compound. The goal of artificial intelligence (AI)-aided …
and intermediates for a target compound. The goal of artificial intelligence (AI)-aided …
Local augmentation for graph neural networks
Abstract Graph Neural Networks (GNNs) have achieved remarkable performance on graph-
based tasks. The key idea for GNNs is to obtain informative representation through …
based tasks. The key idea for GNNs is to obtain informative representation through …
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning
We present the open-source AiZynthFinder software that can be readily used in
retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively …
retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively …