Add citation for our paper about best practices in ML

paper.bib

@@ -59,6 +59,17 @@ @article{wang2020machine
  publisher={ACS Publications}
 }
 
+@article{spiekermann2023comment,
+ title={Comment on `Physics-based representations for machine learning properties of chemical reactions'},
+ author={Spiekermann, Kevin A. and Stuyver, Thijs and Pattanaik, Lagnajit and Green, William H.},
+ journal={Machine Learning: Science & Technology},
+ volume={4},
+ number={4},
+ pages={048001},
+ year={2023},
+ publisher={IOP Publishing}
+}
+
 %%%%%%%%%% Datasets %%%%%%%%%%
 % Original QM9
 @article{ramakrishnan2014quantum,

paper.md

@@ -55,7 +55,7 @@ However, `astartes` operates on arbitrary vector inputs, so its principals and w
 # Statement of Need
 
 Machine learning has sparked an explosion of progress in chemical kinetics [@komp2022progress; @spiekermann2022fast], drug discovery [@yang2019concepts; @bannigan2021machine], materials science [@wei2019machine], and energy storage [@jha2023learning] as researchers use data-driven methods to accelerate steps in traditional workflows within some acceptable error tolerance.
-To facilitate adoption of these models, researchers must critically think about several topics, such as comparing model performance to relevant baselines, operating on user-friendly inputs, and reporting performance on both interpolative and extrapolative tasks<!-- cite Kevin's comment article-->. 
+To facilitate adoption of these models, researchers must critically think about several topics, such as comparing model performance to relevant baselines, operating on user-friendly inputs, and reporting performance on both interpolative and extrapolative tasks @spiekermann2023comment. 
 `astartes` aims to make it straightforward for machine learning scientists and researchers to focus on two important points: rigorous hyperparameter optimization and accurate performance evaluation.
 
 First, `astartes`' key function `train_val_test_split` returns splits for training, validation, and testing sets using an `sklearn`-like interface.