02295nas a2200169   4500008004100000022001400041245010700055210006900162260000900231300001400240490000700254520167800261100002001939700001801959700001801977856013001995       2020                        eng d  a1144-054600aUnderstanding the ML black box with simple descriptors to predict cluster-adsorbate interaction energy0 aUnderstanding the ML black box with simple descriptors to predic  cMAY   a8545-85530 v443 a<p>Density functional theory (DFT) is currently one of the most accurate and yet practical theories used to gain insight into the properties of materials. Although successful, the computational cost required is still the main hurdle even today. In recent years, there has been a trend of combining DFT with Machine Learning (ML) to reduce the computational cost without compromising accuracy. Finding the right set of descriptors that are simple to understand in terms of giving insights about the problem at hand, lies at the heart of any ML problem. In this work, we demonstrate the use of nearest neighbor (NN) distances as descriptors to predict the interaction energy between the cluster and an adsorbate. The model is trained over a size range of 5 to 75 atom clusters. When the training and testing is carried out on mutually exclusive cluster sizes, the mean absolute error (MAE) in predicting the interaction energy is similar to 0.24 eV. MAE reduces to 0.1 eV when testing and training sets include information from the complete range. Furthermore, when the same set of descriptors are tested over individual sizes, the MAE further reduces to similar to 0.05 eV. We bring out the correlation between dispersion in the nearest neighbor distances and variation in MAE for individual sizes. Our detailed and extensive DFT calculations provide a rationale as to why nearest neighbor distances work so well. Finally, we also demonstrate the transferability of the ML model by applying the same recipe of descriptors to systems of different elements like (Na-10), bimetallic systems (Al6Ga6, Li4Sn6, and Au40Cu40) and also different adsorbates (N-2, O-2, and CO).</p>
1 aAgarwal, Sheena1 aMehta, Shweta1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/understanding-ml-black-box-simple-descriptors-predict-cluster-adsorbate-interaction-energy