Industry Training at the Mesoscale

Innovative and effective interactions with industrialists are one of the pillars of the E-CAM project. In the original E-CAM proposal, two main vehicles to promote these interactions were indicated: collaborative pilot projects matching E-CAM funded human resources with investigative research and software developments directly connected with an industrial partner’s need, and scoping workshops. The latter were to combine presentation of simulation and modelling in areas directly connected to E-CAM’s broad expertise, with open discussion sessions and workgroups involving E-CAM’s participants and industrial partners to identify new collaborative activities and directions for software development of industrial interest. In order to increase industrial involvement in these workshops, industrial researchers have also been involved as co-organisers in meetings with a particular focus of industrial interest.

To further expand the portfolio of activities targeted at industrialists, E-CAM has established a series of new events targeted at training interested industrial researchers on the simulation and modelling techniques implemented in specific codes and in the direct use of this software for their industrial applications. Preliminary discussions within the consortium focussed interest on codes that have played a flagship role in the project and that already have notable industrial interest or are perceived to have significant potential in this domain. Codes exploited by small software or service vendor companies in simulation and modelling are also of particular interest, in view of the additional bonus to foster collaborations with these SMEs, another major target of E-CAM’s industrial strategy.

This application details the first proposal for new E-CAM industrial training events, focussing on the area of meso- and multiscale simulations (Workpackage 4) and on the flagship code DL_MESO.

2. Event Description

In this workshop we will introduce DL_MESO[1]: a software package for mesoscale simulations based on the Dissipative Particle Dynamics (DPD) and Lattice Boltzmann Equation methodologies. The intention is to gradually present the usage of the software, starting with tutorials based on theoretical background and following up with hands-on sessions. We will focus on the DPD methodology, exploring the different capabilities of the DPD code in DL_MESO (DL_MESO_DPD) in order of growing complexity via practical examples that reflect daily industrial challenges: moving from simple soft repulsive (Groot-Warren) interactions to systems with electrostatic potentials. Particular attention will be paid to the problem of parametrization and how to obtain the best results, as well as interpreting simulation outputs.

Following the current growing usage of General-Purpose Graphic Processing Units (hereafter GPUs) as computing accelerators, we will introduce the GPU version of DL_MESO to speed up your applications. This is a rewritten version of the DPD code in the CUDA language to enable the best possible performance on NVidia GPU cards. However, users will not need to code or modify any sections of DL_MESO_DPD as this GPU version is fully transparent and compatible with the master version, which is designed for use with standard computing hardware.

The participants will be able to run their simulations on the Hartree Centre supercomputer GPU nodes and considerably reduce the computing time as well as increasing the problem system size. This will allow participants to move towards real industrial applications, where the number of particles and computational costs are usually prohibitive on a common laptop.

The one-day GPU section will introduce the NVidia GPU hardware and the different market options with pros and cons for the different products, which will enable users to get the best choice for their industrial scenario and find the ideal trade-off between cost and productivity. Moreover, it will focus on the setup of the GPU software environment to allow the DL_MESO_DPD solver to run on accelerators as well as the current limitations of the GPU version.

3. Industrial use cases for DL_MESO DPD

The Dissipative Particle Dynamics (DPD) code in DL_MESO (DL_MESO_DPD) has been used for a wide range of problems of both scientific and industrial interest: to date, more than 120 journal articles have cited the article describing DL_MESO [1]. Within UKRI STFC itself, DL_MESO_DPD was the “simulation engine” for the Computer Aided Formulation (CAF) project [2]. This was a £1 million Technology Strategy Board project involving three industrial partners – Unilever, Syngenta and Infineum – to develop DPD parameterisation strategies and simulation protocols to predict important properties of newly-devised surfactant-based formulations, e.g. alkyl sulphates used in detergents [3]. The direct outputs from this project included additional functionalities being implemented in DL_MESO_DPD, a DPD parameterisation scheme and a corresponding set of interaction parameters based on matching water/octanol partition coefficients [4], a method to calculate critical micelle concentrations from DPD simulations [5] and a new particle simulation analysis toolkit, UMMAP [6].

Further projects based on the work completed for the CAF project have subsequently been carried out by and/or with the STFC Hartree Centre and IBM Research Europe, all using DL_MESO_DPD, UMMAP and other in-house tools. These projects include devising more efficient parameterisation techniques using machine learning [7], studying other types of surfactants (e.g. alkyl ethoxylates [8], poly(ethylene oxide) alkyl ethers [9]) and their adsorption onto chemically heterogeneous surfaces [10], characterising worm-like and branched micelles [11] and devising a DPD model for alkanes that can incorporate solidification effects (i.e. wax formation) [12]. An STFC spinout venture company, Formeric [13], has also been formed to help industrial users to study their own formulated projects, primarily by developing a software platform to make it easier for them to access DPD simulations and modelling tools.

4. Programme

Day 1	Day 2 part (1)	Day 2 part (2)	Day 3	Day 4 (optional)
Introduction DPD and DL_MESO	DPD Parametrisation strategies	Electrostatics and surfaces	Accelerating your simulation with DL_MESO on GPU	Set up your own simulations

Day 1, Monday 1st March

Introduction to DPD and DL_MESO

09:00 – 11:00 Background and theory

11:00 – 11:30 Break

11:30 – 12:30 Applications

12:30 – 13:30 Break

13:30 – 15:30 Introduction to DL_MESO and DL_MESO_DPD

15:30 – 16:00 Break

16:00 – 17:00 Hands-on session: access/compile DL_MESO_DPD and try running a few test cases

Day 2, Tuesday 2nd March

Part (1): DPD parametrisation strategies

09:00 – 09:30 Background and theory

09:30 – 10:30 Interaction parameters

10:30 – 11:00 Break

11:00 – 12:00 Matching to experimentally-determined properties

12:00 – 12:45 Hands-on session

Part (2): Electrostatics and surfaces

14:00 – 14:45 Strategies to include charges with DPD particles

14:45 – 15:45 Incorporating charge polarisation effects

15:45 – 16:15 Break

16:15 – 17:15 Surfaces, frozen particle walls and moving boundaries

17:15 – 18:00 Hands-on session

Day 3, Wednesday 3rd March

Accelerating your simulation with DL_MESO on GPU

09:00 – 10:00 Introduction to the GPU version of DL_MESO_DPD

10:00 – 10:30 Break

10:30 – 12:30 Hands-on session: Compile DL_MESO_DPD with CUDA language

12:30 – 13:30 Break

13:30 – 15:30 Hands-on session: try out larger-scale simulations (e.g. parameterisation using partition coefficients)

Day 4, Thursday 4th March

Setting up your own simulations

09:00 – 12:30 Hands-on: getting started on parametrising and running DPD simulations of participants’ own systems

All listed times are in GMT

5. Organizers biography

Dr Jony Castagna studied at the University of Calabria “Unical” (Italy) and obtained his PhD on “Direct Numerical Simulation of Turbulent Flows around Complex Geometries” in 2010 (London). After a post-doctoral experience at the University of Southampton, he worked for a CFD company porting to GPU architectures the main solver PROMPT. In 2016 he joined the STFC-Hartree Centre at Daresbury Laboratory and is now part of the High Performance Software Engineering group. He ported the DL_MESO on multi-GPUs under the E-CAM project and several other scientific applications in collaboration with main industrial partners. Jony is an NVidia Ambassador for the Deep Learning Institute since 2018 and actively give courses on CUDA, OpenACC and Introduction to Deep Learning. His main research activity is in Turbulent flow simulations, HPC for hybrid CPU-GPU programming and Neural Network for CFD.

Dr Michael Seaton studied Chemical Engineering at the University of Manchester (previously UMIST), obtaining his EngD in 2008 on modelling acoustic fields through heterogeneous media using the mesoscopic lattice Boltzmann equation (LBE) technique. He joined the Scientific Computing Department at UKRI STFC in 2009 and has since led the DL_MESO project as the principal author and maintainer of its general-purpose mesoscale modelling codes, providing code and simulation support for the UK Collaborative Computing Project CCP5 and the EPSRC High-End Computing consortium UKCOMES. Michael has contributed to projects of industrial and technical interest, including the Innovate UK project on Computer Aided Formulation based on property prediction using Dissipative Particle Dynamics (DPD), the Horizon 2020 E-CAM WP4 pilot project on polarizable mesoscopic water models, and code porting efforts to Intel Xeon Phi co-processors for the Intel Parallel Computing Centre at STFC Hartree Centre. He currently leads metadata and ontology development efforts for the Horizon 2020 Virtual Materials Marketplace (VIMMP) project. Michael has extensive experience in development and optimization of software for high-performance computing (HPC), with interests and expertise in mathematical algorithms and applications of mesoscale modelling techniques.

6. References

MA Seaton, RL Anderson, S Metz and W Smith, DL_MESO: highly scalable mesoscale simulations, Mol Simul 39 (10), 796–821 (2013).
R Anderson, “Accelerating Formulated Product Design by Computer Aided Approaches”, STFC SCD website (2017): https://www.scd.stfc.ac.uk/Pages/Accelerating-Formulated-Product-Design-by-Computer-Aided-Approaches.aspx
RL Anderson, DJ Bray, A Del Regno, MA Seaton, AS Ferrante and PB Warren, Micelle formation in alkyl sulfate surfactants using dissipative particle dynamics, J Chem Theory Comput 14 (5), 2633–2643 (2018).
RL Anderson, DJ Bray, AS Ferrante, MG Noro, IP Stott and PB Warren, Dissipative particle dynamics: systematic parametrization using water-octanol partition coefficients, J Chem Phys 147, 094503 (2017).
MA Johnston, WC Swope, KE Jordan, PB Warren, MG Noro, DJ Bray and RL Anderson, Toward a standard protocol for micelle simulation, J Phys Chem B 120 (26), 6337–6351 (2016).
DJ Bray, A Del Regno and RL Anderson, UMMAP: a statistical analysis software package for molecular modelling, Mol Simul 46 (4), 308–322 (2020).
JL McDonagh, A Shkurti, DJ Bray, RL Anderson and EO Pyzer-Knapp, Utilizing machine learning for efficient parameterization of coarse grained molecular force fields, J Chem Inf Model 59 (10), 4278–4288 (2019)
E Lavagnini, JL Cook, PB Warren, MJ Williamson and CA Hunter, A surface site interaction point method for dissipative particle dynamics parametrization: application to alkyl ethoxylate surfactant self-assembly, J Phys Chem B 124 (24), 5047–5055 (2020).
MA Johnston, AI Duff, RL Anderson and WC Swope, Model for the simulation of the CnEm nonionic surfactant family derived from recent experimental results, J Phys Chem B 124 (43), 9701–9721 (2020).
J Klebes, S Finnigan, DJ Bray, RL Anderson, WC Swope, MA Johnston and B O Conchuir, The roles of chemical heterogeneity in surfactant adsorption at solid-liquid interfaces, J Chem Theory Comput 16 (11), 7135 – 7147 (2020).
B O Conchuir, K Gardner, KE Jordan, DJ Bray, RL Anderson, MA Johnston, WC Swope, A Harrison, DR Sheehy and TJ Peters, Efficient algorithm for the topological characterization of worm-like and branched micelle structures from simulations, J Chem Theory Comput 16 (7), 4588–4598 (2020).
DJ Bray, RL Anderson, PB Warren and K Lewtas, Wax formation in linear and branched alkanes with dissipative particle dynamics, J Chem Theory Comput 16 (11), 7109–7122 (2020).
“Formeric: Accessible Computer Aided Formulation”, website: https://formeric.co.uk