Chemicals and Materials

Innovations in Materials for Digital Technologies for Material Modelling, and High-Strength Steels across Applications

High-Tech Materials Technology Opportunity Engine (TOE)


RELEASE DATE
12-Jul-2024
REGION
Global
DELIVERABLE TYPE
Technology Research

RESEARCH CODE
D737-00-E7-00-00
SKU
CM_2025_33541
Yes
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Innovations in Materials for Digital Technologies for Material Modelling, and High-Strength Steels across Applications
Published on: 12-Jul-2024 | SKU: CM_2025_33541

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This issue of High-Tech Materials Technology Opportunity Engine (TOE) highlights technology developments related to innovations in digital technologies for material modelling and high-strength steels across applications. Innovations related to biomimetics, computational design methods, automotive steels, metallic glasses, and artificial neural networks (ANNs) are featured in the issue.



The High-Tech Materials TOE provides intelligence on technologies, products, processes, applications, and strategic insights on various materials across industries. Some material technologies include lightweight materials, bio–based materials, ceramics, smart materials, fibers, nanomaterials, responsive materials, polymers, woven and non-woven materials, polymers and plastics and packaging materials.



The Chemicals and Advanced Materials cluster tracks research and innovation trends and developments across specialty chemicals, plastics, polymers, chemicals, bio-chemicals, metals, coatings, thinfilms, surface treatments, composites, alloys, oil and gas, fuel additives, fibers, and several other related technologies and its impact and application across industries.



Keywords: artificial neural networks, metal organic frameworks, advanced high strength steels, electronic structure simulation, and structural supercapacitor electrode

Piezoelectric Biomimetic Scaffold For Cracked Bone Regeneration

Designing Stable Non-Metallic Porous Frameworks Using Computational Methods

Twisted Epitaxy Configuration Paving the Way For Novel Quantum Electronic Applications

Advanced High-Strength Steel AHSS in Automotive Innovation

Exploring Amorphous Metals for Heavy-Duty Applications

Machine Learning Model for Predicting Electronic Properties of Material Surfaces

Deep Learning-based Hybrid Approach for Advanced Electronic Structure Simulation

New Material Modeling Methods Promise Advances in Energy Storage

Modeling Tomorrow’s Battery Materials that are Long Lasting

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