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Found 18 papers, 9 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.
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
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.
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.
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems.
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.
Implementing graph neural networks with TensorFlow-Keras
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently.
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.
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.
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.
MEGAN: Multi-Explanation Graph Attention Network
Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
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.
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.
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.
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
In this work, we extend artificial simulatability studies to the domain of graph neural networks.
Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI
As the importance of high-throughput screening (HTS) continues to grow due to its value in early stage drug discovery and data generation for training machine learning models, there is a growing need for robust methods for pre-screening compounds to identify and prevent false-positive hits.
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability.
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been used to learn the Boltzmann distribution directly, without samples.
