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Quantum Computing: Machine Learning with Emphasis on Quantum Boltzmann Machines
Studies of Quantum Dots using Neural Networks and Coupled Cluster
Electrostatics in Mesoscale Simulations of Biological Membranes using the Hybrid Particle-Field Approach
Evaluating the Phase Behaviour of Silica Modeled by the Vashishta Potential Using Free Energy Methods
Studying Place Cell Formation and Remapping in an Artificial Neural Network Model
Predicting static friction in a molecular dynamic system using machine learning
Bayesian neural network estimation of next-to-leading order cross sections
A machine learning approach to understanding depression and anxiety in new mothers
Towards predicting Harmful Conspiracies through Phase Transitions in Complex Interaction Networks
Applications of the Yang-Mills gradient flow to topological observables in QCD
Solving the Many-Electron Problem Using Neural Networks and Variational Monte Carlo
Characterization of Cardiac Cellular Dynamics Using Physics-informed Neural Networks