# Machine learning of molecular properties: locality and active learning Source: [Machine learning of molecular properties: locality and active learning]() Collection index: [llms.txt]() Data updated: 2025-10-24T22:31:08.632Z Authors: Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev Published: 2017-09-18T22:52:52.000Z ## Abstract In recent years the machine learning techniques have shown a great potential in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of DFT on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers - the out-of-sample molecules, not well-represented in the training set. In the present paper we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Molecular Energy Prediction on QM9 50000 samples](): Molecular energy prediction; QM9 50000 samples; MAE: 0.41. ## Original sources and implementation [Original paper]() [PDF]()