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DIMECHAIN

Industry

Materials & Manufacturing

Materials discovery and production optimisation, from molecular simulation through to the factory floor.

Problems we address

  • Candidate materials spaces too large to screen experimentally at any realistic budget
  • Simulation runs that answer the question after the design decision was taken
  • Production scheduling under constraints that change shift to shift
  • Quality data collected diligently and used for nothing

How we approach it

Related research

Maturity labels are not decoration. Most of this is at concept stage.

Materials & aerospace
Maturity: concept

High-Entropy Alloys for Aerospace

Simulation of alloy composition, phase stability, defects and aerospace material performance.

Materials & aerospace
Maturity: concept

Programmable Matter & Smart Polymers

Molecular self-assembly, phase transitions and stimuli-responsive polymer design.

Materials & aerospace
Maturity: concept

Quantum-Encrypted Materials Databases

Consortium members want to pool materials data without exposing their own pipeline, and trust-plus-a-contract does not scale across parties who are collaborators and competitors at once. We are looking at quantum-resistant schemes for confidentiality that has to outlast current cryptography. Stated plainly: most of this is a governance problem wearing a cryptography costume, and we would want to argue the answer is boring standard cryptography before agreeing that it is not.

Materials & aerospace
Maturity: concept

Quantum Simulations for Molecular Design

Electronic-structure and molecular-interaction simulation for candidate design and screening.

Materials & aerospace
Maturity: concept

Hypersonic Aerodynamics

Shock, thermal and high-speed flow simulation and geometry optimisation. The live route is misspelled “ypersonic”.

Materials & aerospace
Maturity: in development

Quantum-Enhanced Turbulence Modeling

Turbulence is the canonical unsolved problem of classical physics, and every practical method models the small scales rather than resolving them — the modelled part is where the error lives. We are testing whether quantum representations can carry sub-grid correlation directly rather than approximating its effect. The bar is a canonical benchmark flow first, then a Reynolds number where classical models are known to degrade. We are at the first, not the second.

Working on something in materials & manufacturing?