Achievements
Communications and Electronics
- Composition search for low-dielectric materials for 5G communication systems
- Discovery of new materials for OLED light-emitting materials and encapsulants
- Spectral prediction and compound discovery for organic OLED light-emitting materials, including blue and yellow emitters
- Application of machine learning to NMR prediction of OLED materials and correlation analysis with device performance
- Discovery of antioxidants and conductivity enhancers for coating materials
- Investigation of material compositions for PVD coatings
- Prediction and analysis of particle size in pigment materials
- Screening of quantum dot materials
- Improvement of curing speed for optical fibre coating materials
- Discovery of coating materials using high-refractive-index monomers
- Development of photo-functional small-molecule compounds and high-frequency radio-wave diffusion films
- Crystallisation prediction for UV filters
Semiconductors
- Screening of semiconductor cleaning processes and materials
- Simulation and screening of materials for CMP
- Application of Materials Informatics to CMP slurry material development
- Application of machine learning to NMR prediction of OLED materials and correlation analysis with device performance
- Screening of semiconductor precursor compounds
- Optimisation of laser processing conditions
- Optimisation of film formation process conditions
Energy
- Development of lithium-ion battery-related materials, including electrolytes, new solvents, nanoparticles, and electrode materials
- Energy calculation and candidate molecule discovery for cation-encapsulated polyoxometalates
- Simulation of lithium-ion behaviour
- Development of prediction models for the redox potential of electrolyte candidates
- Definition of model applicability domains for redox potential prediction
- Improvement of storage performance in battery materials and suppression of electrolyte resistance
- Composition optimisation for catalyst materials and oxygen storage materials
Sustainability
- Optimisation of catalyst compositions and fabrication conditions for N₂O decomposition
- Performance improvement and cost optimisation of biodegradable plastics
- Investigation of new additives for biodegradable plastics using chemical descriptors
- Development of material prediction models for epoxy resins and biodegradable resins
- Parameter estimation for concrete and precipitates from release agents
- Discovery of PFAS alternative materials for seals and packing materials
- Property prediction for recyclable alloy conductors
- Development of environmental impact prediction models
- Material design for flame-retardant auxiliaries and stranded conductors
- Prediction of anion selectivity in LDHs using machine learning
Other Fields
- Optimisation of flow reactor conditions
- Optimisation of polymerisation conditions in adhesive development
- Development of physical property prediction models using computational science and machine learning
- Discovery of carrier materials for drug delivery systems using pKa prediction models
- Prediction of viscoelastic properties in urethane resins
- Investigation of reagent compositions for inspection equipment
- Discovery of alternative flavour materials using sensory evaluation models
- Automated particle size determination from TEM images
- Generation of material features from SEM images
- Extraction of molecular features from FT-IR spectra
- Feature extraction from XRD spectra and factor analysis of performance
MI-6 works with customers as a long-term R&D partner, not simply as a provider of feasibility studies. By combining advanced data science, materials informatics, and laboratory automation, we help accelerate development across a broad range of research themes, including many beyond those listed above.
Supported R&D Themes
