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Explore our
digital assets
Introduction
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The Innovation Return on Research (IROR) programme is a 5 year collaboration between the Hartree Centre and IBM Research, which aims to solve industry challenges and create economic and societal impact. We are developing a suite of digital assets which are industry relevant in the areas of Chemistry & Materials, Life Sciences and Engineering & Manufacturing. Businesses from these sectors have engaged with the programme as partners through projects which validate and shape the capabilities of our digital assets. Underpinning IROR is a cross-cutting Enabling Technologies programme, applicable to a wide range of industries.
IROR digital assets consist of reusable software components or workflows that have been designed and validated through challenge-led projects with partner companies in the IROR programme. They have been designed so that businesses can use them for similar applications but they are also flexible enough so they can be adapted or recombined to address new challenges in the future.
Our digital assets are now available for companies to access through various adoption routes including Platform as a Service, software licensing or collaborative R&D projects. Please do explore their capabilities, applications and potential benefits for your business.
Alison Kennedy Director | STFC Hartree Centre
Adoption routes
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Platform as a Service (PaaS) Software licensing Collaborative R&D projects
We offer a range of high performance
computing (HPC), artificial intelligence
(AI), data analytics and visual computing
resources to customers on a pre-paid, or
pay as you go basis.
Customers may supply their own simulation
software to use on our platforms, or may
opt to use Hartree Centre digital assets for a
premium on top of standard compute time
and storage costs.
Platform as a Service (PaaS) projects can
also include support from Hartree Centre
staff, at an additional cost.
Many of our digital assets are available for
organisations to access and use under commercial
license terms.
Upon payment of a license fee, access will be
given to the source code of the digital asset,
typically for a period of one year.
Additional license terms can be negotiated on
a case-by-case basis. Organisations can deploy
digital assets on their own on premise or cloud
computing resources.
3-month, no-cost evaluation licenses are also
available.
Examples of collaborative R&D projects
could involve the set up or running of
simulations to answer specific industrial
challenges defined by the customer.
An R&D project could also be used
to complete the necessary software
development as a step towards eventual
software licensing or PaaS.
Projects are costed at Full Economic
Costs, and may be paid for by the client
or (part) grant-funded through a range of
programmes.
Best for: Organisations who want to access
our state of the art HPC platforms quickly,
reducing costs and accelerating product
development.
Best for: This adoption route is well suited to
organisations with in-house software development
and compute resources or data that cannot leave
their premises.
Best for: Organisations who want to
use our digital assets but do not have
the capability or capacity available in
house. We can also modify a digital
asset to make it suitable for your specific
requirements.
Chemistry & Materials
For businesses to remain competitive they must move away from ad-hoc, labour intensive and expensive approaches towards a more robust and adaptive computer-aided paradigm.
Our Chemistry and Materials programme brings together STFC’s expertise in atomistic and mesoscale modelling with IBM’s artificial intelligence (AI) technologies to transform chemical and materials product design. This will boost efficiency in development of new products, especially for high value manufacturing supply chains.
Our digital assets will:
• Reduce time to market
• Provide an adaptive response to supply chain variability
• Enable development for sustainability
• Accelerate R&D processes
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• Cost reduction and reduced time to market by replacing physical experiments with simulation
• New insights into product performance
Particle Dynamics ModellerSimulation tools for Dissipative Particle Dynamics
Benefits
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Dissipative Particle Dynamics (DPD) is a meso-scale simulation method, suitable for modelling a wide range of industry products. We have developed a suite of interoperable tools for DPD simulations spanning the whole modelling workflow from system setup, to running simulations on GPU-accelerated high performance computing platforms alongside statistical and graphical analysis of simulation results.
Our tools are available for organisations to access under a commercial license for use on your own platforms, or through our Platform as a Service (PaaS) offering. We can also support bespoke simulation projects through collaborative R&D.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
Yes Yes Yes
Applications
Suitable for modelling a wide range of industry products including:
• Personal and home care products; creams and gels
• Lubricants
• Fuels incl. additives
• Polymer melts and waxes
• Cost reduction through improved simulation accuracy
Parameterisation EngineAutomated model creation
Benefits
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Applications
Suitable for parameterising a wide range of industry products including:
• Personal and home care products; creams and gels
• Lubricants
• Fuels incl. additives
• Polymer melts and waxes
For robust modelling and accurate results – we need high quality models, however parameterising even simple models like Dissipative Particle Dynamics (DPD) is a difficult and time consuming task that must be done at the outset of any simulation project.
We have a developed a range of tools and approaches for generating industry-quality bespoke parameter sets for DPD simulations. These can be based on experimental data or on first principle calculations using Density Functional Theory. Parameter sets can be optimised using either a Force-balance method, or Bayesian optimisation.
Our MEAMfit tool is available for organisations under a commercial license. Use of the entire tool set is available through our Platform as a Service offering. We can also generate bespoke parameter sets for your organisation as part of a collaborative R&D project.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
Yes (MEAMfit) Yes Yes
• Accessible through an easy to use iPad interface
• Experiments can be run remotely
• User friendly
• Saves staff time learning to use HPC and simulation tools
Consumable Computing for ChemistryMaking simulation accessible to all
Benefits
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Building on the Particle Dynamics Modeller, we have developed a series of virtual experiments that are digital analogues of lab-based formulation chemistry. R&D scientists in your organisation can both configure experiments and execute them remotely on our high performance computing (HPC) platforms at the Hartree Centre.
Examples of virtual experiments
• Salt curve plotting
• Meso-structure mapping
• Micelle formation
• Adsorption isotherms of surfactants on
to surfaces
• Phase boundary detection (ternary phase
diagrams)
• Isothermal compressibility experiments
• Rheology of surfactants in microfluidic
channels
This provides a Simulation as a Service (SaaS) interface to our systems. Customisation for your organisation or implementation of new virtual experiments can be delivered as collaborative R&D projects.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No Yes Yes
Applications
• Replacing laboratory formulation chemistry experiments with computer simulation
• Improved accuracy
• Fewer simulations required
• Cost-saving
• Faster
AI Accelerator for Chemistry Compute smarter
Benefits
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Parameter sweep-type operations such as phase diagram exploration can be accelerated by adopting an artificial intelligence (AI) approach, learning the shape of the parameter space in real-time within the program.
Bayesian Optimisation automatically balances refining the detail of interesting features (exploitation) with ensuring good coverage of the whole space (exploration), using far fewer simulations than a grid-based approach. In the end, a more accurate result is achieved more quickly, and for lower cost.
The Artificial Intelligence (AI) accelerator forms part of some of the virtual experiments available through the Consumable Computing for Chemistry interface. This approach could be adapted to your organisation as part of a collaborative R&D project.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No Yes Yes
Applications
• Accelerating phase-diagram exploration experiments
Engineering & Manufacturing
We are developing software tools and methods for high fidelity modelling, uncertainty quantification, code coupling, simulation and optimisation to create virtual products.
Our digital assets will:
• Reduce development costs
• Improve product performance
• Reduce time to market
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• Flexible and scalable approach to code-coupling
• Cost reduction and reduced time to market, by replacing physical experiments with simulation
• New insights into product performance
Multiphysics and Multiphase Code Coupler Scalable and flexible code coupling
Benefits
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Industry-relevant systems often require a modelling approach which spans multiple scales (e.g. atomistic and macroscopic) or physical models (e.g. CFD and solid mechanics) to capture all the relevant behaviour of a virtual product. This requires coupling together two or more models, often implemented in separate simulation codes. We have developed the open-source Multiscale Universal Interface (MUI) library with inter-process communication, spatial and temporal data samplers, and a Python API to approach this. Alongside the MUI library, we also have a set of adaptors that couple specific simulation codes:
CFD Thermal transfer Reactor dynamics Particle transport
Code_Saturne
OpenFOAM
SYRTHES DYN3D OpenMC
The core MUI library is available under Apache or GPL open-source licenses. If your organisation is interested in coupling any of the codes listed, a commercial license from STFC is available. MUI can also be accessed as part of our Platform as a Service (PaaS) offering. Support on bespoke code coupling applications is available through a collaborative R&D project.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
Yes Yes Yes
Applications
Modelling industrial processes and products including:
• Aerospace & automotive
• Compressors & turbines
• Marine/submarine
• Nuclear
• Industrial plants
• Better understanding of performance variation
• Discovery of rare product failure modes
• Cost reduction by reducing the number of simulations required
Quantification of Uncertainty Toolkit
Benefits
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For many engineering applications, simple, fast and cheap simulation methods can reproduce the results of a more accurate and more computationally demanding model to within a defined tolerance level. To make virtual product design a reality, we must be able to quantify and control the size of the errors made by the model, understanding the sensitivity outputs to the values of input parameters.
Our QUTE toolkit can build ‘surrogate models’ – simpler, cheaper and faster methods capable of reproducing results of more accurate and computationally demanding models to within a defined tolerance level. It automates the process of parametric uncertainty studies, including selection of parameters to sample (using methods such as Polynomial Chaos and multi-level Monte Carlo), submission of simulation tasks to computing platforms, retrieval and analysis of results. QUTE is available under a commercial software license, or can be deployed on our high performance computing (HPC) resources via our Platform as a Service offering. Customisation of QUTE to support specific HPC systems can be carried out through collaborative R&D projects.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
Yes Yes Yes
Applications
• Virtual product design and optimisation
• Modelling the operation of industrial processes
for Engineering (QUTE)Computing the unknown
Using Hartree Centre skills and software can:
• Reduce costs
• Reduce time to market
Simulation Methods for
Benefits
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Building on our code coupling and artificial intelligence (AI) tools and expertise, we have developed simulation models for a wide range of applications.
Applications of our simulation models
• Acoustic performance prediction and
optimisation of axial compressors
• Multiphase flow jet break-up for
industrial mixing
• Fluid-structure interactions
• Carbon deposition modelling on nuclear
fuel pins
• Transient thermal flows in complex pipe
geometries
• Neutronics and convection for reactor
modelling
• Ice accretion on aircraft wings
We provide a collaborative R&D service to build bespoke simulation models, either for deployment on our high performance computing (HPC) platforms or for HPC resources within your organisation.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No No Yes
Applications
Modelling industrial processes and products including:
• Aerospace & automotive
• Compressors & turbines
• Marine/submarine
• Nuclear
• Industrial plants
Engineering ApplicationsBuilding the virtual product
• Knowledge capture by training AI using expert input
• Improved efficiency of existing capital infrastructure
AI-driven Process Simulation Using AI to control systems
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Industrial processes from waste water treatment to factory packing lines, are controlled by manual or semi-automated rule-based approaches. Machine learning offers the opportunity to build control systems which can adapt to changing conditions and optimise themselves to meet business requirements – for example reducing energy use, cost, or improving output quality.
By building simulation models, or by learning how a process behaves from operational data, we can provide recommendations as to how process operations can be improved. Contact us to discuss collaborative R&D projects to model and improve your business processes.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No No Yes
Applications
Optimisation and control of industrial processes including:
• Production and packing lines
• Water treatment and distribution
Life sciences
Advances in molecular simulation and high-throughput sequencing have created a deluge of biological data. We are using this to develop new data-driven approaches to molecular design and metagenomics analysis.
Our digital assets will:
Combine method development with practical implementation of existing technology to build cutting-edge applications in:• Data driven
antimicrobial peptide design
• Soil and crop metagenomics
• Precision agriculture
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• Improved product efficacy by rapid, large-scale screening
• Cost reduction by reducing number of lab experiments required
Virtual Membrane Binding and
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Rapid screening of candidate drug compounds remains a major challenge for the pharmaceutical industry. Accurate approaches that accelerate the discovery process and provide new insights at a molecular scale can increase the number of lead compounds that can be screened, reducing costs by focusing lab testing on peptides with a high chance of success.
Our virtual assay rapidly screens potential anti-microbial peptides by computing free energies of binding and insertion into biological membranes in parallel using a HPC workflow. We then adopt a data-centric approach to identify novel peptide sequences which have anti-microbial properties but are harmless to human cells. This method has allowed us to discover new compounds and verify effectiveness in the lab.
You can access the virtual assay on our high performance computing systems via Platform as a Service (PaaS). Customising the workflow for new classes of applications or for activity against a different biological target could be approached through a collaborative R&D project.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No Yes Yes
Applications
Screening and designing anti-microbial and anti-fungal agents including:
• Small molecule drugs
• Linear and cyclic peptides
Permeation AssayVirtual screening for anti-microbial peptides
• Reduced costs by using more efficient algorithms
• Data compression
• New applications enabled through real-time analysis
Omics Analytics Toolkit Extracting knowledge from omics data
Benefits
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Applications
Analysis of metagenomic data including:
• Skin and mouth microbiome
• Gut / faecal microbiome
• Soil microbiome
• Gene profiling
We are developing tools which can be used to build powerful and efficient analytics pipelines for metagenomic data. The Microbiome Characterisation Utilities (MCU) are based on our novel methods for metagenome analysis.
Microbiome Characterisation Utilities (MCU)
graphMap histoSketch sketchPredict geneFlow
Combining variation
graph respresentation of
gene sets with a locality-
sensitive hashing indexing
scheme, graphMap can
perform accurate gene
profiling of microbiome
samples in minutes using
just a laptop.
Using similarity-preserving
sketches to represent
streaming k-mer spectra,
histoSketch can compress
large microbiome samples
to small sketches. These
can be indexed, searched
or classified in real-time.
Applying machine
learning classifiers
to sketches,
sketchPredict is able
to classify microbiome
samples according to
phenotype or other
metadata.
Combining MCU
components with the
powerful Nextflow
framework, geneFlow is
able to identify microbiome
samples of interest, perform
gene and taxonomic
profiling and predict gene
context.
These tools are available to companies under a commercial license. We can develop bespoke analytics pipelines for specific applications via collaborative R&D projects.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
Yes No Yes
Enabling technologies
Underpinning Innovation Return on Research (IROR) is a technology development programme, delivering cutting edge machine learning at scale, exploring new data technologies and a platform to allow industry collaborators to exploit our digital assets in their organisation.
Our digital assets will:
• Reduce costs by accelerating the time taken to reach a solution
• Highlight new applications through large-scale data analytics
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• Portability of code on current and future architectures
• Reduced costs
• Faster time to simulation
Code analysis and generation for HPC Automating performance optimisation
Benefits
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As next generation architectures continue to evolve, increasing performance, it is important to adapt software to realise gains in speed. We have developed a suite of tools that can analyse source code and generate auto-tuned kernels for emerging architectures. These have been used as part of the PSyclone project to generate code for the Met Office’s next generation weather forecast model.
Code analysis and code generation tools for HPC
• PSyclone: code generation for weather
modelling
• Fparser: a Fortran code parser
• Fgenerator: a Fortran code generator
• Melody: automated performance
tuning
• Habakkuk: performance prediction
from Fortran source code
The tools above are available under open source licenses, free of charge. Hartree Centre also provide bespoke software profiling, analysis and optimisation services.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No No Yes
Applications
High performance computing (HPC) optimisation of simulation codes:
• Weather & climate modelling
• Computational Fluid Dynamics (CFD)
• Materials modelling
• Increased efficiency of product operation and production
• Reduced training time for image classification tasks
Machine Learning at Scale More than Deep Neural Networks
Benefits
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Applications
General purpose optimisation problems including:
• Product design
• Process optimisation
• Drug design
• Deep neural network training
We have developed a range of advanced machine learning capabilities, covering two classes of application:
Machine learning at scale applications
Bayesian Optimisation Image detection and classification
A flexible and efficient method for global optimisation
that is able to balance between exploration and
exploitation: when to prioritise between learning
about the unknown and when to improve on the best
known solution. We have applied this approach in
a range of areas from optimisation of aerodynamic
wings to designing small molecule drugs for biomedical
applications.
Building and tuning deep neural networks for specific
applications such as object detection and labelling.
Using our IBM Power systems with NVIDIA GPUs, we
can train complex neural networks much more rapidly
than conventional compute resources.
Our Bayesian Optimisation tools are accessible through
an API - Bayesian Optimisation as a Service (BOaaS),
allowing end users to build their own applications
without having to deploy the tools on premises.
Our team are able to develop image processing
models for specific applications through collaborative
R&D projects.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No Yes Yes
• Cost saving by automation of data ingestion, alignment and analysis
• New applications enabled by very large scale data analytics
Data Technology Platform Enabling big data applications
Benefits
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Based on IBM PAIRS technology, we have the ability to handle very large geospatial data sets and use them to build bespoke applications, for example crop pest prediction for precision agriculture.
To explore how this technology could be used for your application, contact us for access to the platform or to discuss potential collaborative R&D projects.
Adoption routes
Software Licensing PaaS/SaaS Collaborative R&D
No No Yes
Applications
Geospatial data applications including:
• Agritech
• Logistics
• Weather
• Routing
www.hartree.stfc.ac.uk
@hartreecentre /company/stfc-hartree-centre [email protected]®
The Hartree Centre was created to transform UK industry by accelerating the adoption of high performance computing (HPC), big data analytics and artificial intelligence (AI) technologies. We play a key role in realising the UK Government Industrial Strategy by stimulating applied digital research and innovation, creating value for the organisations we work with and generating economic and societal impact for the UK.
The Science and Technology Facilities Council (STFC) Hartree Centre is part of UK Research and Innovation.