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Found 5 papers, 1 papers with code
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
AutoChart: A Dataset for Chart-to-Text Generation Task
The analytical description of charts is an exciting and important research area with many applications in academia and industry.
Introduction to Machine Learning for the Sciences
This is an introductory machine-learning course specifically developed with STEM students in mind.
Fermionic neural-network states for ab-initio electronic structure
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems.
Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability
