Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach
Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach
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DOI：
10.1039/d3dd00085k
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发表时间：
2023-08-08
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影响因子：
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通讯作者：
Grayson, Matthew N.
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文献类型：
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作者：
Espley, Samuel G.;Farrar, Elliot H. E.;Grayson, Matthew N.
机器学习（ML）模型一旦经过训练，就可以在几秒钟内做出反应势垒预测，这比密度泛函理论（DFT）等量子力学（QM）方法快了几个数量级。然而，这些机器学习模型需要在通常具有数千个昂贵的高精度障碍的大型数据集上进行训练，并且不能很好地概括它们所训练的特定反应。在这项工作中，我们证明了迁移学习（TL）可以用于调整预训练的Diels-Alder屏障预测神经网络（NN），以使用水平TL（hTL）预测其他周环反应，此外，在更高的理论水平上使用对角TL（dTL）。TL衍生的预测可能具有低于1 kcal mol（-1）的可接受的化学准确度阈值的平均绝对误差（MAE），对TL前预测MAE>5 kcal mol（-1）的显著改善，并且在极低的数据范围内，hTL和dTL分别需要少至33和39个新数据点。因此，hTL和dTL是深入了解反应可行性的强大选择，而不需要广泛的高通量实验或计算筛选或大型数据集生成来训练定制的ML模型。
Machine learning (ML) models can, once trained, make reaction barrier predictions in seconds, which is orders of magnitude faster than quantum mechanical (QM) methods such as density functional theory (DFT). However, these ML models need to be trained on large datasets of typically thousands of expensive, high accuracy barriers and do not generalise well beyond the specific reaction for which they are trained. In this work, we demonstrate that transfer learning (TL) can be used to adapt pre-trained Diels-Alder barrier prediction neural networks (NNs) to make predictions for other pericyclic reactions using horizontal TL (hTL) and additionally, at higher levels of theory with diagonal TL (dTL). TL-derived predictions are possible with mean absolute errors (MAEs) below the accepted chemical accuracy threshold of 1 kcal mol(-1), a significant improvement on pre-TL prediction MAEs of >5 kcal mol(-1), and in extremely low data regimes, with as few as 33 and 39 new datapoints needed for hTL and dTL, respectively. Thus, hTL and dTL are powerful options for providing insight into reaction feasibility without the need for extensive high-throughput experimental or computational screening or large dataset generation for training bespoke ML models.