Table of Content

Table of Contents
1.0 Executive Summary
1.1 Research Scope
1.2 Research Methodology
1.3 Research Methodology Explained
2.0 Digitization in the Materials Industry
2.1 Chemicals & Materials Industry is an Important Enabler of the Fourth Industrial Revolution and is Investing Heavily in Digitization
2.2 Digitization is Expected to have Game Changing Effect Across Functions in Material Companies
2.3 Chemical Companies are Expecting to Use AI to Design New Products and to Grow their Business into New Verticals
2.4 Lack of Relevant Skills and Resistance to Change from Leadership is Hindering the Adoption of Digitization Among Chemical & Materials Companies
3.0 Market Leaders Embracing Digitization
3.1 BASF has Emerged as One of the Pioneers of Digitization in the Chemical & Materials Industry
3.2 The Merger and Subsequent Restructuring of Dow and DuPont Have Been Made Possible by a Successful Implantation of Digitization Initiatives
3.3 Sumitomo is Betting on IIoT Implantation to Infuse Efficiency into its Production Processes
4.0 AI Impacting Materials Research & Production
4.1 AI-based Algorithms can be Used to Simulate R&D Processes That are Unfeasible to be Performed in Laboratories
4.2 Advanced Analytics Helps Chemical Plants Minimize Their Downtime by Enabling Predictive and Prescriptive Applications
4.3 Machine Learning-based Algorithms Can Augment their Accuracy Over Time by Leveraging the Results of Previous Experiments
4.4 Large Scale Commercialization of AI-based New Material Discovery Methods Will Depend on the Availability and Quality of Training Datasets
5.0 Using AI To Solve Address Fundamental Problems in OLEDs and Polymers
5.1 AI is Helping OLED Manufacturers Achieve the Goal of Mass Production of OLED with Desired Material Properties and Minimum Defects
5.2 Machine Learning Algorithms have Enabled Better Prediction of Polymer Properties and are Empowering Sustainable Recycling Techniques
6.0 Key Innovations – AI For Materials Research
6.1 Molecular Space Shuttle Deep Learning System
6.2 Novel Approaches of Using AI for Chemical Research
6.3 Autonomous Laboratory for Materials Development
6.4 Simulation and Experiments in OLED Research
6.5 Material Development AI Platform
6.6 AI-driven Non-Destructive Testing Manufacturing Inspection
6.7 Efficient and High-Performance System for Defect Classification in OLED Devices
6.8 AI-based Polymer Selection and Property Prediction
6.9 AI-based Method for Design of New Materials for OPV Solar Cells
6.10 Leveraging Machine Learning for Property Prediction
6.11 Using Machine Learning to Alter the Alter Lower Temperature of Temperature Sensitive Polymers
6.12 Using Machine Learning and HPC to Create More Capable Capacitors
6.13 Digitization Initiatives for Efficient Production Process Control
6.14 Using Computer Modeling, AI and ML to Develop Eco-Friendly Chemicals
6.15 Use Quantum Algorithms To Discover Advanced Materials For Next-generation OLED
6.16 Applying Machine Learning to Conduct Focused Simulations for New Material Discovery
7.0 Conclusion and Recommendations
7.1 Conclusions and Recommendations
8.0 Industry Contacts
8.1 Industry Contacts
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