Search Results for author:
Found 21 papers, 14 papers with code
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.
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.
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.
Quantum Computer-Aided design of Quantum Optics Hardware
It is not clear how the full potential of large quantum systems can be exploited.
Quantum Physics Computational Physics Optics
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
Artificial Intelligence and Machine Learning for Quantum Technologies
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly.
Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics
We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data.
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.
Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics.
Design of quantum optical experiments with logic artificial intelligence
In this work, we propose the use of logic AI for the design of optical quantum experiments.
Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement.
Active learning machine learns to create new quantum experiments
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.
Quantum Optical Experiments Modeled by Long Short-Term Memory
In this work, we show that machine learning models can provide significant improvement over random search.
Computer-inspired Quantum Experiments
We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future.
Conceptual understanding through efficient inverse-design of quantum optical experiments
Here we present Theseus, an efficient algorithm for the design of quantum experiments, which we use to solve several open questions in experimental quantum optics.
Quantum Physics Optics
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.
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents.
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.
