A graph-convolutional neural network model for the prediction of chemical reactivity

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Items 301–400 of 679

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44. Co‐crystal Prediction by Artificial Neural Networks**
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65. Transformer-based multitask learning for reaction prediction under low-resource circumstances
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66. Relation Extraction with Self-determined Graph Convolutional Network
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67. Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries
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68. Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
    Ryan‐Rhys Griffiths, Philippe Schwaller, Alpha A. Lee · 2018 · ChemRxiv
69. A Survey on Graph Neural Network Acceleration: A Hardware Perspective
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70. Leveraging artificial intelligence for research and action on climate change: opportunities, challenges, and future directions
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86. DRACON: disconnected graph neural network for atom mapping in chemical reactions
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87. Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties
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88. Reaction Impurity Prediction using a Data Mining Approach**
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