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市場調査レポート

マテリアルズインフォマティクス (MI) :2020年~2030年

Materials Informatics 2020-2030

発行 IDTechEx Ltd. 商品コード 939931
出版日 ページ情報 英文 147 Slides
納期: 即日から翌営業日
価格
マテリアルズインフォマティクス (MI) :2020年~2030年 Materials Informatics 2020-2030
出版日: 2020年05月31日 ページ情報: 英文 147 Slides
担当者のコメント
IDTechEx Ltd. より マテリアルズインフォマティクス (MI)  の資料が発行されました。MI は 統計分析などを活用したインフォマティクス(情報学)の手法により、大量のデータから新素材を探索する取り組みです。当資料はMI について 技術の評価、企業の分析、応用とケーススタディ、主要企業などについて、体系的な情報を提供します。
概要

当レポートでは、マテリアルズインフォマティクス (MI) 市場について調査分析し、概要、技術の評価、企業の分析、応用とケーススタディ、主要企業などについて、体系的な情報を提供します。

目次

第1章 エグゼクティブサマリーと結論

第2章 イントロダクション

第3章 技術の評価

  • マテリアルズインフォマティクス (MI) アルゴリズムの入出力
  • マテリアルズインフォマティクス (MI) に必要なもの
  • MIアルゴリズムの概要
  • ディスクリプターとモデルのトレーニング
  • 自動機能選択
  • MIアルゴリズムの種類:監視付き/監視なし
  • MIアルゴリズムの種類:典型的な監視付きモデル
  • MIアルゴリズムの種類:監視なしケーススタディ
  • MIアルゴリズムの種類:生成型 vs. 識別型
  • MIアルゴリズムの種類:深層学習
  • MIアルゴリズムの種類:深層学習(2)
  • MIアルゴリズムの種類:深層学習(3)
  • 小型材料データセットを使用する方法
  • 小型材料データセットを使用した深層学習
  • アルゴリズムの進歩の主要分野
  • 技術アプローチの概要

第4章 企業の分析

  • 重要な業界活動の概要
  • マテリアルズインフォマティクス (MI) 企業:カテゴリー
  • 戦略的アプローチの結論と見通し
  • マテリアルズインフォマティクス (MI) 企業:概要
  • 外部プロバイダーの主要パートナーと顧客
  • 民間企業が資金調達
  • 企業リスト:民間企業
  • 主要企業
  • 企業リスト:公的機関
  • 社内運用
  • 逆合成予測因子
  • データリポジトリ:組織
  • データリポジトリ:動向
  • データリポジトリの活用
  • 注目すべきMIコンソーシアム(1)
  • 注目すべきMIコンソーシアム(2)
  • 注目すべきMIコンソーシアム(3)
  • 材料ゲノムイニシアチブ(MGI)
  • 材料ゲノム工学(MGE)
  • 将来:全自動ラボ
  • 将来:「Chemputer」

第5章 応用とケーススタディ

  • ケーススタディ:概要
  • 市場予測
  • マテリアルズインフォマティクス (MI) ロードマップ
  • プロジェクトのカテゴリー
  • マテリアルズインフォマティクス (MI) :市場浸透、成熟度別
  • アルミニウム・チタン合金
  • 金属ガラス合金
  • ニッケル基超合金
  • 高エントロピー合金
  • 金属間化合物
  • 有機エレクトロニクス(1):OLED
  • 有機エレクトロニクス(2):OLED
  • 有機エレクトロニクス:RFID
  • 有機エレクトロニクス:OPV
  • 有機エレクトロニクス:それ以上
  • 触媒
  • 触媒(2)
  • イオン液体
  • 超伝導体
  • リチウムイオン電池(1)
  • リチウムイオン電池(2)
  • リチウムイオン電池(3)
  • ポリマー・複合材料(1)
  • ポリマー・複合材料(2)
  • ポリマー・複合材料(3)
  • 潤滑油
  • 熱電
  • 有機金属
  • 2Dマテリアル
  • 2Dマテリアル(2)
  • その他のナノ材料
  • 光吸収剤と太陽電池

第6章 企業プロファイル

目次

Title:
Materials Informatics 2020-2030
Data-centric approaches for design and discovery within materials science R&D. Notable advancements in data infrastructures and machine learning. Player profiles, technology progression, market outlook, business models, and case studies.

Materials Informatics is an R&D paradigm shift; enabling discoveries and cutting the time to market.

Materials informatics (MI) involves using data-centric approaches for materials science R&D. There are multiple strategic approaches and already some notable success stories; the adoption is happening now and missing this transition will be very costly.

This report provides key insights and commercial outlooks for this emerging field. Built upon technical primary interviews, readers will get a detailed understanding of the players, business models, technology, and the application areas.

What is materials informatics?

Materials informatics is the use of data-centric approaches for the advancement of materials science. This can take numerous forms and influence all parts of R&D (hypothesis - data handling & acquisition - data analysis - knowledge extraction).

Primarily, MI is based on using data infrastructures and leveraging machine learning solutions for the design of new materials, discovery of materials for a given application, and optimisation of how they are processed.

MI can accelerate the "forward" direction of innovation (properties are realised for an input material) but the idealised solution is to enable the "inverse" direction (materials are designed given desired properties).

This is not straight-forward and is still at a nascent stage. In many cases, the data infrastructure is not comprehensive and MI algorithms are often too immature for the given experimental data. The challenge is not the same as in other AI-led areas (such as autonomous cars or social media), the players are often dealing with sparse, high-dimensional, biased, and noisy data; leveraging domain knowledge is an essential part of most approaches.

Contrary to what some may believe, this is not something that will displace research scientists; if integrated correctly, MI will become a set of enabling technologies accelerating their R&D process. For many, the dream end-goal is for humans to oversee an autonomous self-driving laboratory; although still at an early-stage there have been key improvements, spin-out companies formed, and success stories all facilitated by MI developments.

Why now?

This is not a new approach, many sectors have adopted similar design approaches for decades. But there are three main reasons why this transformative technology is impacting the materials science space right now:

  • Improvements in AI-driven solutions leveraged from other sectors.
  • Improvements in data infrastructures, from open-access data repositories to cloud-based research platforms.
  • Awareness, education, and a need to keep up with the underlying pace of innovation.

IDTechEx have classified the projects undertaken into six main categories outlined in detail within the report. Within that, there are three repeated advantages to employing advanced machine learning techniques into your R&D process: enhanced screening of candidates & scoping research areas, reducing the number of experiments to develop a new material (and therefore time to market), and finding new materials or relationships. The training data can be based on internal experimental, computational simulation and/or from external data repositories; enhanced laboratory informatics and high throughput experimentation or computation can be integral to many projects.

This report looks at the key progressions in machine learning for MI, the success stories, and how end-users are actively engaging with this.

What are the strategic approaches?

Ignoring this R&D transition is a major oversight for any company that designs materials or designs with materials. The impact will not be seen immediately, but in the mid- to long-term the missed opportunity will be significant. This could be when bringing competitive products to market, developing versatility in the supply chain, finding next-generation opportunities, or generating the ability to diversify a business unit or material portfolio.

Numerous players have already begun this adoption with three core approaches: operate fully in-house, work with an external company, or join forces as part of a consortium.

Each of these approaches is appraised in detail in the report; choosing to start the adoption of MI is important, choosing the right path is essential.

The external MI players can come from numerous starting points, as outlined in the figure below. There is also the option for MI players to become a licencing company with a strong advanced material portfolio and also for end-users to offer MI as a service. Geographically, many of the end-users embracing this technology are Japanese companies, many of the emerging external companies are from USA, and the most notable consortia and academic labs are split across Japan and the USA.

Interview based profiles of all the key companies are included within this IDTechEx report.

What application areas are successfully using this?

Organic electronics, battery compositions, additive manufacturing alloys, polyurethane formulations, and nanomaterial development are all examples of areas that MI is having an immediate impact on. The broad range of material use-cases means industrial adoption is being seen from electronics manufacturers to chemical companies.

There are universal challenges, but each application area will have certain considerations, be it in the availability of existing data, the domain knowledge, the complexity of the structure-property relationships, and more.

The final part of this report goes into detail on each applications area in turn, highlighting key developments, commercial use-cases, and notable publications. This provides end-users the opportunity to focus on case studies in their specific areas of interest and MI players to what areas to explore.

What about COVID-19?

The impact from the global pandemic cannot be overlooked. Alongside this report, IDTechEx has provided a detailed additional document outlining the impact of COVID-19 on the development of materials informatics.

A large amount of the negative impact on MI players will depend on the fate of their customer-base which supply into multiple sectors; the majority of which will be negatively affected in the short-to-mid term. However, there are some positives for this field, most notably being the role MI can play in helping to develop a responsive and versatile global supply chain and how it can support computational simulation

What will I learn from the report?

This market report is released at a point in time where the 10-year outlook is prime for rapid adoption. This report goes far beyond what is available on the internet, providing key commercial outlooks based on primary-interviews coupled with expertise on both this topic and numerous of the relevant application areas.

Market forecasts, player profiles, investments, roadmaps, and comprehensive company lists are all provided. Making this essential reading for anyone wanting to get ahead in this field.

Analyst access from IDTechEx

All report purchases include up to 30 minutes telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY AND CONCLUSIONS

  • 1.1. What is materials informatics?
  • 1.2. Overview of significant industry activity
  • 1.3. AI opportunities at every stage of materials design and development
  • 1.4. Problems with material science data
  • 1.5. Key areas of algorithm advancements
  • 1.6. Materials informatics players - categories
  • 1.7. Conclusions and outlook for strategic approaches
  • 1.8. Key partners and customers of external providers
  • 1.9. Notable MI consortia
  • 1.10. Project categories

2. INTRODUCTION

  • 2.1. Common abbreviations
  • 2.2. What is materials informatics?
  • 2.3. Materials informatics - why now?
  • 2.4. What can ML/AI do in materials science?
  • 2.5. Materials Informatics - category definitions
  • 2.6. The broader informatics space in science and engineering
  • 2.7. The broader informatics space in science and engineering
  • 2.8. Key challenges for MI across the full material spectrum
  • 2.9. Closing-the-loop on traditional synthetic approaches
  • 2.10. High Throughput Virtual Screening (HTVS)
  • 2.11. Advantages of ML for chemistry and materials science - Acceleration
  • 2.12. Advantages of ML for chemistry and materials science - Scoping and screening
  • 2.13. Advantages of ML for chemistry and materials science - New species and relationships
  • 2.14. Data infrastructures for chemistry and materials science

3. TECHNOLOGY ASSESSMENT

  • 3.1. Inputs and outputs of materials informatics algorithms
  • 3.2. What is needed for materials informatics?
  • 3.3. Overview of MI algorithms
  • 3.4. Descriptors and training a model
  • 3.5. Automated feature selection
  • 3.6. Types of MI algorithms - supervised vs unsupervised
  • 3.7. Types of MI algorithms - typical supervised models
  • 3.8. Types of MI algorithms - unsupervised case study
  • 3.9. Types of MI algorithms - generative vs discriminative
  • 3.10. Types of MI algorithms - deep learning
  • 3.11. Types of MI algorithms - deep learning (2)
  • 3.12. Types of MI algorithms - deep learning (3)
  • 3.13. How to work with small material datasets
  • 3.14. Deep learning with small material datasets
  • 3.15. Key areas of algorithm advancements
  • 3.16. Summary of technology approaches

4. PLAYER ANALYSIS

  • 4.1. Overview of significant industry activity
  • 4.2. Materials informatics players - categories
  • 4.3. Conclusions and outlook for strategic approaches
  • 4.4. Materials Informatics players - Overview
  • 4.5. Key partners and customers of external providers
  • 4.6. Funding raised by private companies
  • 4.7. Full player list - private companies
  • 4.8. Full player list - private companies
  • 4.9. Main players
  • 4.10. Full player list - public organisations
  • 4.11. Taking the operation in-house
  • 4.12. Retrosynthesis predictors
  • 4.13. Data repositories - organisations
  • 4.14. Data repositories - trends
  • 4.15. Leveraging data repositories
  • 4.16. Notable MI consortia (1)
  • 4.17. Notable MI consortia (2)
  • 4.18. Notable MI consortia (3)
  • 4.19. Materials Genome Initiative (MGI)
  • 4.20. Materials Genome Engineering (MGE)
  • 4.21. The future - fully autonomous labs
  • 4.22. The future - "Chemputer"

5. APPLICATIONS AND CASE STUDIES

  • 5.1. Case studies - overview
  • 5.2. Market forecast
  • 5.3. Materials informatics roadmap
  • 5.4. Project categories
  • 5.5. Materials informatics - market penetration by maturity
  • 5.6. Aluminium and titanium alloys
  • 5.7. Aluminium and titanium alloys
  • 5.8. Metallic glass alloys
  • 5.9. Nickel-base superalloys
  • 5.10. High-entropy alloys
  • 5.11. Intermetallics
  • 5.12. Organic electronics (1) - OLED
  • 5.13. Organic electronics (2) - OLED
  • 5.14. Organic electronics - RFID
  • 5.15. Organic electronics - OPV
  • 5.16. Organic electronics - beyond
  • 5.17. Catalysts
  • 5.18. Catalysts (2)
  • 5.19. Ionic Liquids
  • 5.20. Superconductors
  • 5.21. Lithium-ion batteries (1)
  • 5.22. Lithium-ion batteries (2)
  • 5.23. Lithium-ion batteries (3)
  • 5.24. Polymers and composites (1)
  • 5.25. Polymers and composites (2)
  • 5.26. Polymers and composites (3)
  • 5.27. Lubricants
  • 5.28. Thermoelectrics
  • 5.29. Organometallics
  • 5.30. 2D materials
  • 5.31. 2D materials (2)
  • 5.32. Other nanomaterials
  • 5.33. Light absorbers and solar cells

6. COMPANY PROFILES