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@@ -23,6 +23,7 @@ SDEGen: Learning to Evolve Molecular Conformations from Thermodynamic Noise for
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### 自动化计算核磁共振化学位移
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### 自动化计算核磁共振化学位移
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- An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions 小分子的化学位移预测
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- An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions 小分子的化学位移预测
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- Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts 这一篇也不错，https://github.com/fenglb/SVM-M
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- Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts 这一篇也不错，https://github.com/fenglb/SVM-M
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+- AI预测化学位移的方法：Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions, CASCADE,Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network
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### 量化自动化计算核磁共振全谱
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### 量化自动化计算核磁共振全谱
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