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Innovations in Digital Technologies Transforming Material R&D

出版商 Frost & Sullivan 商品編碼 988592
出版日期 內容資訊 英文 65 Pages
商品交期: 最快1-2個工作天內
創新數位技術,改變物質研究與開發 Innovations in Digital Technologies Transforming Material R&D
出版日期: 2020年12月28日內容資訊: 英文 65 Pages






  • 調查範圍
  • 數位化有望改變材料行業的整體功能流程
  • 基於機器學習的算法,可以利用先前實驗的結果隨著時間的推移提高準確性


  • 使用數據科學和算法模擬研發過程
  • 主要數字技術:人工智能和機器學習
  • 主要數字技術:Digital Twin
  • 主要數字技術:物聯網(IoT)
  • 主要數字技術:大數據
  • 研發過程中數位技術實施的關鍵領域:建模和優化
  • 實施用於材料研發的數位工具的優勢
  • 實施用於材料研發的數位工具的挑戰


  • 基於人工智能的平台可促進化學工業的研究
  • 使用專有技術提取和收集材料的微觀結構信息
  • 先進的開源軟件和新材料的數據分析
  • 獨特的AI平台,可促進材料開發的決策過程
  • 人工智能結合數據推動化學和材料行業的發展
  • 獨特的數據採集和分析工具,可加快材料開發等方面的研發工作。


  • 增長機會1:開發具有易於使用的界面的集成平台
  • 增長機會2:開源平台



Product Code: D9A8

Increasing Technology Adoption Efforts Facilitates Value Creation During R&D Process

A common challenge faced by researchers during material R&D is from a data perspective, such as availability of highly fragmented information due to data systems that vary from application to application, fluctuating data quality and inability to access curated external research activities effectively. Integration of digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Big Data Analytics etc. can successfully addresses challenges faced by R&D teams and reducing time to market.

The research service titled "Innovations in Digital Technologies Transforming Material R&D" provides an overview of how digital technologies can overcome the challenges currently faced during R&D of materials and chemicals. The technological advancements in digital platforms have been captured emphasizing on how each digital technology can contribute and influence material R&D ( Research and Development) process.

This research service also provides a comprehensive overview of key stakeholders who are developing platforms that can be used by material developers for various R&D and new product developmental efforts

Various strategies opted by material stakeholders in the industry such as technology licensing, joint developmental efforts to implement digitization tools in collaboration with the solution providers are also highlighted.

Table of Contents

1.0 Strategic Imperatives

  • 1.1 The Strategic Imperative 8™
  • 1.1 The Strategic Imperative 8™
  • 1.2 The Impact of The Top Three Strategic Imperatives on Digital Technologies for Material R&D
  • 1.3 About The Growth Pipeline Engine™
  • 1.4 Growth Opportunities Fuel The Growth Pipeline Engine™
  • 1.5 Research Methodology

2.0 Executive Summary

  • 2.1 Research Scope
  • 2.2 Digitization Expected to be a Game Changer Across Functionalities in Material Industry
  • 2.3 Machine Learning Based Algorithms Can Augment their Accuracy Over Time by Leveraging the Results of Previous Experiments

3.0 Need for Digitization in Material R&D

  • 3.1 Data Science and Algorithms can be Used to Simulate R&D Processes
  • 3.2 Key Digital Technologies: Artificial Intelligence and Machine Learning
  • 3.3 Key Digital Technologies: Digital Twin
  • 3.4 Key Digital Technologies: Internet of Things (IoT)
  • 3.5 Key Digital Technologies: Big Data
  • 3.6 Key Areas of Implementation of Digital Technologies in R&D Processes: Modeling and Optimization
  • 3.6 Key Areas of Implementation of Digital Technologies in R&D Processes: Control and Diagnosis (continued)
  • 3.7 Advantages of Implementing Digital Tools for Material R&D
  • 3.8 Challenges of Implementing Digital Tools for Material R&D

4.0 Companies to Action

  • 4.1 Artificial Intelligence based Platforms to Expedite Research in Chemicals Industry
  • 4.2 Use of Proprietary Technologies to Extract and Harvest Materials Microstructural Information
  • 4.3 Advanced Open Source Software & Data Analytics for New Materials
  • 4.4 Unique AI Platform Expediting Decision Making Process for Materials Development
  • 4.5 Artificial Intelligence Combined With Data for Advancements in Chemicals & Materials Industry
  • 4.6 Unique Data Ingestion and Analaysis tool Accelerating R&D Efforts in Material Development
  • 4.7 AI Powered Platform For Identifying Materials With Desired Properties
  • 4.8 Deep Learning Technology Powered AI Tool Capable of Efficiently Analyzing Sparse and Noisy Data Sets
  • 4.9 SaaS Platform for Effective Steel Development
  • 4.10 Machine Learning and Artificial Intelligence for Coatings and Battery Optimization
  • 4.11 Cloud-based Software for Materials Development
  • 4.12 Hierarchical Machine Learning Platform for Materials Development
  • 4.13 Using AI to Optimize Chemical Extraction Process
  • 4.14 Cloud-based IoT Platform for Chemical Industry
  • 4.15 Advanced Analytics Platform for Chemical Manufacturing

5.0 Growth Opportunities

  • 5.1 Growth Opportunity 1: Development of Integrated Platforms With Easy-to-Use Interfaces
  • 5.1 Growth Opportunity 1: Development of Integrated Simulation and Modeling Platforms With Easy-to-Use Interfaces (continued)
  • 5.2 Growth Opportunity 2: Open Source Platforms
  • 5.2 Growth Opportunity 2: Open Source Platforms (Continued)

6.0 Key Contacts

  • 6.1 Key Contacts

7.0 Next Steps

  • 7.1 Your Next Steps
  • 7.2 Why Frost, Why Now?
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