Search Results for author:
Found 19 papers, 11 papers with code
Learning how to explain neural networks: PatternNet and PatternAttribution
We show that these methods do not produce the theoretically correct explanation for a linear model.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Ranked #1 on
Time Series
on QM9
The (Un)reliability of saliency methods
Saliency methods aim to explain the predictions of deep neural networks.
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #6 on
Formation Energy
on Materials Project
Formation Energy
Chemical Physics
Materials Science
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.
iNNvestigate neural networks!
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
Analysis of Atomistic Representations Using Weighted Skip-Connections
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation.
Generating equilibrium molecules with deep neural networks
Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science.
Learning representations of molecules and materials with atomistic neural networks
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
Autonomous robotic nanofabrication with reinforcement learning
Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules.
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.
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods.
Machine learning of solvent effects on molecular spectra and reactions
We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies.
Ranked #6 on
Drug Discovery
on QM9
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in chemistry.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
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.
