Search Results for author:
Found 14 papers, 9 papers with code
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.
Connectivity Optimized Nested Graph Networks for Crystal Structures
Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.
Actively Learning Costly Reward Functions for Reinforcement Learning
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability.
MEGAN: Multi-Explanation Graph Attention Network
However, existing explanation methods have limited expressiveness and interoperability due to the fact that only single explanations in form of node and edge importance are generated.
Graph neural networks for materials science and chemistry
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e. g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials.
On scientific understanding with artificial intelligence
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Implementing graph neural networks with TensorFlow-Keras
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently.
Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning
Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications.
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulations methods.
Scientific intuition inspired by machine learning generated hypotheses
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas.
Neural Message Passing on High Order Paths
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry.
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
