**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

MOOC# Path Integral Methods in Atomistic Modelling

Description

The course provides an introduction to the use of path integral methods in atomistic simulations.

The path integral formalism allows to introduce quantum mechanical effects on the equilibrium and (approximately) time-dependent behavior of atomic nuclei, which is relevant from cryogenic temperatures to room temperature and above, particularly for systems that contain light elements.

The course is conceived as a series of lectures on topics of increasing difficulty and specialization. For each topic, the complete course will provide a set of lecture notes, complete with pen-and-paper exercises, recorded lectures, and practical exercises based on jupyter notebooks and an advanced molecular dynamics code. The various chapters and content will appear in the coming months, as they become ready.

Molecular Dynamics and Sampling - Michele Ceriotti, EPFL The basics of path integrals - Mariana Rossi, MPG Hamburg Advanced path integral methods - Thomas Markland, Stanford Ring Polymer molecular

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (47)

Related courses (9)

Related publications (180)

Molecular dynamics

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields.

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Quantum mechanics

Quantum mechanics is a fundamental theory in physics that provides a description of the physical properties of nature at the scale of atoms and subatomic particles. It is the foundation of all quantum physics including quantum chemistry, quantum field theory, quantum technology, and quantum information science. Classical physics, the collection of theories that existed before the advent of quantum mechanics, describes many aspects of nature at an ordinary (macroscopic) scale, but is not sufficient for describing them at small (atomic and subatomic) scales.

Lectures in this MOOC (20)

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi

CH-351: Molecular dynamics and Monte-Carlo simulation

Introduction to molecular dynamics and Monte-Carlo simulation methods.

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple

Efficiency of Sampling: Ergodicity and Autocorrelation FunctionsMOOC: Path Integral Methods in Atomistic Modelling

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

Langevin dynamics: Path Integral MethodsMOOC: Path Integral Methods in Atomistic Modelling

Covers Langevin dynamics, Fokker-Planck equation, solving the Langevin equation, and efficiency of Langevin sampling in molecular dynamics.

Ring Polymer Molecular DynamicsMOOC: Path Integral Methods in Atomistic Modelling

Covers Ring Polymer Molecular Dynamics, quantum statistics, chemical reaction rates, and condensed phase problems.

Quantum Correlation FunctionsMOOC: Path Integral Methods in Atomistic Modelling

Explores quantum correlation functions and their role in molecular dynamics simulations, including the reconstruction of standard correlation functions from Kubo-transformed ones.

Ring Polymer Molecular DynamicsMOOC: Path Integral Methods in Atomistic Modelling

Explores ring polymer molecular dynamics, its accuracy in different limits, and its application in multidimensional systems and liquid environments.

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

2024François Maréchal, Jonas Schnidrig, Cédric Terrier

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...