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Found 4 papers, 2 papers with code
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
Toward Explainable AI for Regression Models
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks.
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.
