Description
SchNet is an end-to-end deep neural network architecture based on continuous-filter convolutions. It follows the deep tensor neural network framework, i.e. atom-wise representations are constructed by starting from embedding vectors that characterize the atom type before introducing the configuration of the system by a series of interaction blocks.
Papers Using This Method
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles2025-04-17Machine learning surrogate models of many-body dispersion interactions in polymer melts2025-03-19Machine Learned Force Fields: Fundamentals, its reach, and challenges2025-03-07OpenQDC: Open Quantum Data Commons2024-11-29SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction2024-11-18Distribution Learning for Molecular Regression2024-07-30Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery2024-04-05On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions2023-10-10End-to-end AI framework for interpretable prediction of molecular and crystal properties2022-12-21Machine Learning for Screening Large Organic Molecules2022-11-23Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls2022-09-26Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations2021-05-26The Open Catalyst 2020 (OC20) Dataset and Community Challenges2020-10-20Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties2020-09-26A deep neural network for molecular wave functions in quasi-atomic minimal basis representation2020-05-11Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics2020-02-17Learning representations of molecules and materials with atomistic neural networks2018-12-11Analysis of Atomistic Representations Using Weighted Skip-Connections2018-10-23Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials2018-06-08SchNet: A continuous-filter convolutional neural network for modeling quantum interactions2017-06-26