CCP5 Summer School 2020

Molecular simulation methods


Durham University, 19-30 July 2020

Register now!

About


The 2020 Summer School will take place at Durham University from 19 to 30 July. Organised by CCP5 and sponsored by CECAM, the School is intended for newcomers to the science of molecular simulation and will provide a comprehensive introduction to the theoretical background as well as practical sessions on computational methods and research seminars to illustrate the versatility of simulation in modern research. There will also be opportunities for participants to present their own research.

The Summer School starts with a two-day programming course, where students can opt to take either Python or modern Fortran. After this preparation, the first five days of the main School will cover the basics of molecular simulation, and the remaining three days will be devoted to more advanced courses with options in mesoscale, ab initio, and biomolecular simulation. Course notes will be provided. In addition to the lectures, there will be extensive practical sessions in which students will undertake computational exercises to reinforce and further explore the material.

The fee for the Summer School is £450, which includes accommodation for 11 nights (19-30 July) and all meals. If you are selected to attend the Summer School you will be sent a separate link to make your payment.

Please note The school can be recognized towards your doctoral training in UK, also upon request we can provide a letter for ECTS credits for your school.

There are no funds available for travel support or waving the fee.

Key dates

  • 1st of April, deadline for registration
  • 15th April, selection announcement
  • 1st of May, payment deadline
  • 19th of July, arrival
  • 30th of July, departure

Organising Committee

  • Dr Colin Freeman, University of Sheffield
  • Prof Neil Allan, University of Bristol
  • Dr Mark Miller, University of Durham
  • Dr Alin Elena, STFC Daresbury Laboratory

Sponsors

Registration


Please note the registration is handled externally by STFC. For registration please click here

Lectures


Programming Courses

  • Introduction to Modern Fortran (6 lectures, 4 practical sessions)
  • Introduction to Python

Basic Courses

  • An Overview of Molecular Simulation
  • Statistical Mechanics (2 lectures)
  • Molecular Dynamics (3 lectures)
  • Monte Carlo Methods (3 lectures)
  • Free Energy Methods (3 lectures)
  • Optimisation Methods
  • Introduction to Force Fields
  • Long timescale methods
  • Advanced Free Energy methods
  • Practicals (10 sessions over 5 afternoons)

Advanced Courses

Lecturers

First principles simulations

Mesoscale methods

Simulation of organic and biomolecules

Programming

Timetable


July 19 Activity Location
14:00 - 17:00 Arrival
July 20
9:00 - 10:00 Fortran I/Python I
10:00 - 11:00 Fortran II/Python II
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Fortran III/Python III
12:30 - 14:00 Lunch
14:00 - 15:30 Practicals
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:30 Practicals
July 21
9:00 - 10:00 Fortran IV/Python iV
10:00 - 11:00 Fortran V/Python V
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Fortran VI/Python VI
12:30 - 14:00 Lunch
14:00 - 15:30 Practicals
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:30 Practicals
July 22
9:00 - 10:00 Overview of molecular simulations - PC CG85
10:00 - 11:00 Statistical Mechanics 1 - MB CG85
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Statistical Mechanics 2 - MB CG85
12:30 - 14:00 Lunch
14:00 - 15:30 Practical - Stat Mech Problems
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals - Basics
17:10 - 18:10 Invited Speaker 1
July 23
9:00 - 10:00 Introduction to force fields - PC CG85
10:00 - 11:00 Monte Carlo 1 - NA CG85
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Monte Carlo 2 - NA CG85
12:30 - 14:00 Lunch
14:00 - 15:30 Practical - MC integration
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals - Intro to MC
17:10 - 18:10 Invited Speaker 2
July 24
9:00 - 10:00 Molecular Dynamics 1 - CF CG85
10:00 - 11:00 Molecular Dynamics 2 - CF CG85
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Monte Carlo 3 - MA CG85
12:30 - 14:00 Lunch
14:00 - 15:30 Practical - Intro to MD
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals - Phase Equilibria
17:10 - 18:10 Invited Speaker 3
July 25
9:00 - 10:00 Molecular Dynamics 3 - MA CG85
10:00 - 11:00 Free energy methods 1 - JA CG85
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Free energy methods 2 - JA CG85
12:30 - 14:00 Lunch
14:00 - 15:30 Practical - Thermostats+ shake
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals - Stability + accur MD
17:10 - 18:10 Invited Speaker 4
** July 26 Free day **
July 27
9:00 - 10:00 Optimisation methods - JH CG85
10:00 - 11:00 Long timescale methods - JH CG85
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Advanced Free Energy CG85
12:30 - 14:00 Lunch
14:00 - 15:30 Practical - Chemical potential
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals - Forcefields optimisation
17:10 - 18:10 Research Seminar
19:00 - School Dinner Durham Castle
July 28
9:00 - 10:00 Advanced Seminar 1 CG85/CG91/CG83
10:00 - 11:00 Advanced Seminar 2 CG85/CG91/CG83
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Advanced Seminar 3 CG85/CG91/CG83
12:30 - 14:00 Lunch
14:00 - 15:30 Practicals
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals
17:10 - 18:10 Invited Speaker 5
July 29
9:00 - 10:00 Advanced Seminar 4 CG85/CG91/CG83
10:00 - 11:00 Advanced Seminar 5 CG85/CG91/CG83
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Advanced Seminar 6 CG85/CG91/CG83
12:30 - 14:00 Lunch
14:00 - 15:30 Practicals
15:30 - 16:00 Refreshments Chemistry Café
16:00 - 17:00 Practicals
**July 30 **
9:00 - 10:00 Advanced Seminar 7 CG85/CG91/CG83
10:00 - 11:00 Practicals
11:00 - 11:30 Refreshments Chemistry Café
11:30 - 12:30 Practicals
12:30 - 14:00 Lunch
14:00 - 15:30 Practicals
15:30 - Departure

Advanced Seminars may be structured different depending on the lecturers.

Research Seminar Speakers


Campus information


How to make it to Durham University

University instructions

Arrival is on 19th July unless you have already made other arrangements with us. You can collect your room keys from the Reception of Collingwood College (post code DH1 3LT) any time from 2pm. Accommodation will be at Collingwood College in single-occupancy en-suite rooms. A map showing the location of the college is here. For more information about getting to and around Durham, please see. Information about local buses is here

Student Events


Student Seminar (13+2 minutes)

If you opted to give an oral presentation, we will make a selection of 4-5 talks.

CCP5 Student Poster Prize

Keep in mind for poster size is A0 maximum.

CCP5 Student Talk Prize

Contact


For more information do not hesitate to contact Alin M Elena alin-marin.elena@stfc.ac.uk

Overview basic lectures


An Overview of Molecular Simulation

An overview of the current state of molecular simulation with examples of special interest taken from the literature.

Introduction to force fields

Statistical Mechanics 1

In this lecture we will begin with an important question: why bother with statistical thermodynamics? We will progress to basic statistical quantities and concepts such as averages, fluctuations and correlations and how to use them in practice to calculate the physical properties of systems. This will lead us to the determination of the true statistical error for system properties obtained by simulation. We will apply these ideas to commonly calculated properties such as diffusion, radial distribution functions and velocity autocorrelation, while also examining the physical meaning of these properties. We will conclude with a look at distribution functions: how they arise and what they mean.

Statistical Mechanics 2

In the second lecture we shall look at more theoretical aspects of statistical mechanics. Beginning with the Lagrange and Hamiltonian description of classical mechanics we shall progress to the idea of phase space and the concept of a probability distribution function. This will be followed by basic applications (and associated mathematical manipulations) of the distribution function to obtain various physical properties of a system. We will examine the common ensembles (NVE, NVT and NPT) and discuss their application and interrelation. Finally we shall look at time dependence, beginning with the Liouville Equation and its connection with other time dependent equations. We shall conclude with the fluctuation-dissipation theorem.

Monte Carlo 1

Basics: The system. Random sampling. Importance sampling. Detailed balance. Metropolis algorithm in the canonical ensemble. Isothermal-isobaric ensemble. Grand-canonical ensemble. Which ensemble?

Monte Carlo 2

Practicalities: Finite-size effects. Random number generators. Tuning the acceptance rate. Equilibration. Configurational temperature. Ergodicity and free-energy barriers. Measuring ensemble averages. Examples (showing ensemble independence for the Lennard-Jones fluid)

Monte Carlo 3

(Free) Energy Barriers: Quasi non-ergodicity. Vapour-liquid phase transition as an example. Removing the interface by Gibbs ensemble MC. Free-energy barrier in the grand-canonical ensemble. Multicanonical preweighting. Histogram reweighting. Parallel tempering

Molecular Dynamics 1

Molecular dynamics: the basic methodology. Integration algorithms and their derivation. Static properties: thermodynamics and structure. Dynamic properties: correlation functions and collective properties

Molecular dynamics 2

Practical aspects of molecular dynamics - Verlet neighbour list, link cell algorithm. Calculating pressure: the virial theorem and the thermodynamic method. Estimating statistical errors: the blocking method. Symplectic algorithms and the Tuckerman-Berne-Martyna approach. Extended systems: canonical (NVT) and isothermal-isobaric (NPT) ensembles.

Molecular dynamics 3

Rigid Bodies, SHAKE, RATTLE.

Free energy methods 1

Free energy, chemical potential & thermodynamics. Applications. Essential statistical mechanics. Ensemble averages, probability distributions & simulations. Free energy, the challenge. Particle insertion & removal. Energy density distributions. The perturbation method.

Free energy methods 2

Review essential statistical mechanics. Thermodynamic integration. Potential of mean force calculations. Umbrella sampling. Absolute free energies. Free energy of liquids.Free energy of solids.

Optimization Methods

The energy landscape, geometrical optimisation and saddle points. Minimisation methods (steepest descent, conjugate gradient, genetic algorithm). Saddle-points (transition state theory, harmonic theory, nudged elastic band, dimer method).

Long timescale methods

Long timescales simulations - the problems. Transition state theory and kinetic Monte Carlo. Temperature accelerated hyperdynamics. Metadynamics.

Advanced Free Energy Methods

TBD

First-principles simulation


First-principles simulation has grown to become one of the most influential and important techniques for modelling at the atomic level. With nuclei and electrons as the basic ingredients the system is modelled at a deeper level of physics than with atoms and interatomic potentials. By explicitly including the electrons in the model and treating their interactions using quantum-mechanical laws, chemical bonding arises as an emergent phenomenon of the model. All kinds of bonding - ionic, covalent, metallic, hydrogen can be treated using the same method. The price of this accurate Hamiltonian is a computational cost orders of magnitude higher than atomic potential models. Nevertheless it is possible and convenient with modern parallel computers to simulate systems of hundreds of atoms, and perform optimization and molecular dynamics in a variety of ensembles.

In this advanced course I will provide a rapid introduction to the “nuts and bolts” of first-principles simulation. In accordance with the philosophy of the CCP5 Summer School, the aim is to attempt to open up the “black box” and explain the concepts and algorithms used. The presentation will assume a familiarity with wave mechanics at the undergraduate level and Dirac notation.

In the practicals you will be able to try for yourself using an advanced density functional code. You should be capable of running realistic calculations by the end of the course, and aware of the major aspects of setup and testing that are vital ingredients for success. The practicals will consist of a series of guided exercises with the CASTEP and CRYSTAL codes.

SYNPOSIS

An Introduction to First-Principles Simulation

  • Motivation
  • Quantum-Mechanical approaches
  • Density-Functional Theory
  • Excited states: TD-DFT
  • Electronic Structure of Condensed Phases
  • Total-energy calculations
  • Basis sets
  • Plane waves and pseudopotentials
  • How to solve the equations
  • Ab-initio simulations

Practical calculations using first-principles QM: Convergence, convergence, convergence

  • Convergence
  • Structural Calculations
  • Lattice Dynamics
  • Exchange and Correlation Functionals
  • Summary

FURTHER STUDY

The lecture notes from the CASTEP workshop held in 2007 are available from http://www.castep.org. Links to a number of ab-initio methods and resources are available at http://electronicstructure.org/.

Mesoscale Mehtods


Mesoscale methods of modelling are capable of tackling larger length and time scales than those available using atomistic methods. By using particles considerably larger than atoms and appropriate choices of interactions between them, these techniques can readily model bulk materials and large structures at the cost of omitting some fine atomic detail. Hydrodynamics start to become more important at these scales: these modelling techniques are thus designed to ensure correct (emergent) fluid behaviour. A mesoscale model can be set up either using a ‘bottom-up’ approach from atomistic models, a ‘top-down’ approach from continuum fluid models, or both.

In this advanced course we will provide an introduction to two mesoscale methods: Dissipative Particle Dynamics (DPD) and the Lattice Boltzmann Equation (LBE) method. We will explain the origins, concepts and algorithms of both methods, as well as their applications, continuing developments and how they can be related to material models at smaller and larger scales (including those covered by the basic lectures).

In the practicals, you will be able to try out DPD and LBE using both simple ‘hackable’ codes and the general-purpose mesoscale modelling package DL_MESO. By the end of the course, you will gain insight into the capabilities of both mesoscale modelling methods. The practicals will consist of a series of guided exercises using the provided codes.

SYNPOSIS

Introduction to the Mesoscale

  • Techniques
  • Physical scales
  • Mesoscale simulation strategies

Dissipative Particle Dynamics (DPD)

  • DPD algorithm
  • Fokker-Planck formulation
  • Application to simple/complex fluids
  • Boundary conditions
  • Thermodynamics and DPD
  • Molecular dynamics and DPD

Lattice Boltzmann Equation (LBE)

  • Classical Boltzmann/Boltzmann Bhatnagar-Gross-Krook (BGK) Equations
  • Lattice Gas Cellular Automata (LGCA)
  • Multiple component or “diphasic” LGCA
  • Lattice Boltzmann Equation method
  • Lattice Boltzmann BGK Equation and kinetic theory
  • LBE for multi-component flow

Campus Maps


to be added soon