Quantum-chemical insights from interpretable atomistic neural networks

27 June 2018

Papers citing "Quantum-chemical insights from interpretable atomistic neural networks"

8 / 8 papers shown

Title
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 48 17 0 30 Dec 2022
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 19 257 0 10 Jul 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 34 215 0 05 Jun 2020
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Marcel F. Langer Alex Goessmann M. Rupp AI4CE 23 92 0 26 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller R. Maurer AI4CE 29 382 0 24 Jun 2019
Learning representations of molecules and materials with atomistic neural networks Kristof T. Schütt A. Tkatchenko K. Müller NAI 24 13 0 11 Dec 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017