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Computational Techniques

Integrated Materials Processing and Computational Techniques (IMPACTTM)

Clients come to Lucideon for solutions to some of the riskiest and time-consuming parts of the R&D lifecycle. IMPACTTM uses First Principle Guidance, Finite Element Analysis, and Machine Learning to optimise the design, synthesis, and characterisation of novel and existing materials and processes.


Lucideon's IMPACTTM team can develop novel materials and processes through:

  • Reducing the number of Design of Experiments for more effective results for client’s materials and processes
  • Finite Element Analysis for simulating materials in its natural environment.
  • Data science & machine learning for supporting the innovative research completed and driving novel materials and processes to commercialisation.

Furthermore, the optimisation of current materials and processes is a strong area of expertise at Lucideon, a key pathway to Industry 4.0.


Our primary goals are:

  • To promote a culture that harnesses advanced technology to drive step-change solutions to materials/process challenges
  • To combine computation, experimental, and synthesis methods to expand the solution toolbox and support overall material/process development efficiency
  • To leverage latest development in characterisation techniques and to develop high-throughput benchmarking tools
  • To bring industry together under common challenges and to promote industry best practice in order to enable clients to compete at the top without the steep learning curves



Our capabilities

  • First Principles Guidance – Working with our skilled network of modelling partners, we can explore novel chemical space, design new materials, or optimise existing materials based on their fundamental chemical and structural characteristics. We use targeted experimental methods to directly validate and guide the model output, linking the theoretical to the physical and accelerating the iterative R&D cycle.
  • Finite Element Analysis - Combining this with our testing characterisation facilities at Lucideon, we can create accurate and robust simulations of materials or systems during production or end-component operation. For example, we can simulate the deformation and failure of materials under different loading conditions and at different temperatures, validate the behaviour with real data from our testing team, and use the predictions to optimise a component’s design or performance.
  • Machine Learning - We can use ML techniques to carry out Design of Experiments, improve the quality of the data collected, and identify the optimal conditions for achieving desired material properties. The ML approach can consider a greater number of process parameters than traditional statistics, while utilising fewer and more efficient experiments. We harness clients' historical data to predict the behaviour and performance of materials under various operating conditions and scenarios, or to create real-time dashboards that monitor and visualise the status and progress of anything from experiments and projects to online process monitoring.


The Lucideon toolbox

Technology is constantly changing. IMPACTTM will connect teams to the right tools, and bridge the gaps in materials/process knowledge, to help provide solutions and reach Lucideon’s vision: to make the world a materially better place. Lucideon has built a toolbox of partnerships to support projects across the materials development lifecycle, from early-stage R&D through to product validation.


Applications of IMPACTTM

  • Reformulation of Intelligent Controlled Release Technology (iCRTTM)
  • Gel rheology
  • Additive Manufacturing development
  • Novel alloy development for high temperature applications


Further resources


Combining our expertise and computational methods to solve material & process development challenges

Case Study

Improving Process Design Through Computational Modelling