add link

AI4Spec_Tutorial1.ipynb

@@ -14,7 +14,7 @@
     "\n",
     "<img src=\"https://raw.githubusercontent.com/milicasan/TutorialImages/89238bfff57a38ec78ecbd08765b63bd15ff9e2c/AI4spec.png\" width=\"600px\"/>\n",
     "\n",
-    "In the exercise below, our AI objective is to train a model to predict a single electronic energy level, HOMO for example. This is a classic example of supervised machine learning regression, where the objective (label) is a single floating-point number. Many AI models can address this task, from decision trees, via kernel-based learning to neural networks. Here, we demonstrate the use of Kernel Ridge Regression, a relatively simple regression approach that has performed well on numerous prediction tasks in physics and chemistry **{Cite Mathias Rupp}**\n",
+    "In the exercise below, our AI objective is to train a model to predict a single electronic energy level, HOMO for example. This is a classic example of supervised machine learning regression, where the objective (label) is a single floating-point number. Many AI models can address this task, from decision trees, via kernel-based learning to neural networks. Here, we demonstrate the use of Kernel Ridge Regression, a relatively simple regression approach that has performed well on numerous prediction tasks in physics and chemistry, [as shown in this tutorial](https://onlinelibrary.wiley.com/doi/full/10.1002/qua.24954).\n",
     "\n",
     "We use a large dataset of molecular structures and their computed HOMO energies to train the AI. Once the model is trained, it can be used as a tool: one can input a new molecule (previously unseen by the model) and instantly receive a value for its HOMO, within the expected error of the trained model.\n",
     "\n",