ML 分子模拟

More abstract general purpose featurizations, such as SMILES strings & Coulomb matrices (discussed below) only excel with large data and deep learning models, which can learn to extract the useful information.
With small data, great gains in accuracy can sometimes be gained by hand selecting features using chemical intuition and domain expertise. For example, the number of azide groups in a molecule is known to increase energetic performance while also making the energetic material more sensitive to shock. While the number of azide groups is implicitly contained in the SMILES string and Coulomb matrix for the molecule, ML models such as neural networks typically need a lot of data to learn how to extract that information. To ensure that azide groups are being used by the model to make predictions with small data, an explicit feature corresponding to the number of such groups can be put in the feature vector.

参考资料：

手机版“神探玺洛克”请扫码