Structure Modeling with Jupyter Notebooks

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Data-driven Manufacturing: Additive Manufacturing Focus

Covers data-driven manufacturing with a focus on additive manufacturing, including manufacturability analysis and a machine learning approach.

Computational Quantum Physics

Covers path-integral ground state methods and Monte Carlo simulations in computational quantum physics.

Molecular Simulation at CECAM: Celebrating 50 Years

Celebrates 50 years of CECAM by exploring molecular and materials simulation.

Atomic-Scale Modeling: Introduction & Hands-On

Offers a hands-on introduction to atomic-scale modeling through Jupyter notebooks, focusing on fundamental materials science concepts.

Numerical Analysis: Nonlinear Equations

Covers algorithms for solving mathematical problems approximately using a computer, including nonlinear equations and numerical approximation methods.

Path Integral Methods: Efficient Energy Estimators

Covers advanced path integral methods for deriving efficient energy estimators in molecular dynamics simulations.

Monte Carlo Methods: Thermal Radiation Applications

Discusses the application of Monte Carlo methods in thermal radiation analysis, focusing on probability functions and numerical integration techniques.

Root Finding Methods: Bisection and Secant Techniques

Covers root-finding methods, focusing on the bisection and secant techniques, their implementations, and comparisons of their convergence rates.

Efficient Stochastic Numerical Methods

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Quantum Many-Body Dynamics: Artificial Neural Network Approach

Discusses simulating quantum many-body dynamics using artificial neural networks to overcome computational challenges and stabilize solutions.