**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 GraphSearch.

Lecture# Computer Simulation: Early Days and Monte Carlo Method

Description

This lecture delves into the early days of computer simulation, starting from the establishment of the Centre Européen de Calcul Atomique et Moléculaire in 1969. It covers the transition from Monte Carlo to Metropolis methods, the development of Molecular Dynamics, and the significance of the Metropolis method in high-energy physics. The presentation includes insights from Marshall Rosenbluth on the historical context and the evolution of Monte Carlo simulations. The lecture also explores the application of Monte Carlo schemes in computational molecular design and the analysis of electron dynamics. It concludes with a discussion on the impact of molecular simulations on scientific research over the decades.

Official source

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 (239)

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.

Molecular modelling

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system.

Ensemble (mathematical physics)

In physics, specifically statistical mechanics, an ensemble (also statistical ensemble) is an idealization consisting of a large number of virtual copies (sometimes infinitely many) of a system, considered all at once, each of which represents a possible state that the real system might be in. In other words, a statistical ensemble is a set of systems of particles used in statistical mechanics to describe a single system. The concept of an ensemble was introduced by J. Willard Gibbs in 1902.

Markov chain Monte Carlo

In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution. Various algorithms exist for constructing chains, including the Metropolis–Hastings algorithm.

Molecular design software

Molecular design software is notable software for molecular modeling, that provides special support for developing molecular models de novo. In contrast to the usual molecular modeling programs, such as for molecular dynamics and quantum chemistry, such software directly supports the aspects related to constructing molecular models, including: Molecular graphics interactive molecular drawing and conformational editing building polymeric molecules, crystals, and solvated systems partial charges development g

Related lectures (385)

Quantum Source CodingPHYS-758: Advanced Course on Quantum Communication

Covers entropic notions in quantum sources, Shannon entropy, Von Neumann entropy, and source coding.

Statistical Physics: Systems IsolationPHYS-105: Advanced physics II (thermodynamics)

Explores statistical physics concepts in isolated systems, focusing on entropy and disorder.

Molecular dynamics under constraints

Explores molecular dynamics simulations under holonomic constraints, focusing on numerical integration and algorithm formulation.

Interval Estimation: Method of MomentsMATH-232: Probability and statistics

Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.

Quantum Random Number GenerationPHYS-758: Advanced Course on Quantum Communication

Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.