マテリアルズ・インフォマティクス 2023-2033年

（詳細は目次のページでご確認ください）

●　全体概要と結論

●　マテリアルズ・インフォマティクスのイントロダクション

●　技術評価

□ AIと機械学習

□ 内部データインフラ

□ 外部データリポジトリ

□ ハイスループット実験法と特性解析

□ 統合計算材料工学（ICME）を含む計算材料科学

□ 自動運転ラボ

●　企業分析

□ MI開発企業の包括的リスト

□ 戦略的アプローチの批判的評価

□ 提携関係と資金調達

□ 国と国際的コンソーシアムと取り組み

●　市場概観

●　用途と事例検証

□ 有機エレクトロニクス、バッテリー、合金、ポリマー、ナノマテリアル等

●　インタビューに基づく24社の企業概要

 

 

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

 

This report provides key insights and commercial outlooks for this emerging field. Built upon technical primary interviews with 24 players, readers will get a detailed understanding of the players, business models, technology, and strategies in this industry. The revenue of firms offering MI services is forecast to 2033, with 13.7% CAGR expected until then. Case studies in numerous applications highlight the wide range of areas in materials science where MI adds value. Analysis of the underlying technologies demystifies this fast-growing area of the R&D digital transformation.

 

 

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 optimization of how they are processed.

 

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

 

This is not straightforward and is emerging from its 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 scientists' R&D processes whilst making use of their domain expertise. 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.

 

 

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:

 

 

IDTechEx have classified the projects undertaken into eight 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.

 

 

Ignoring this R&D transition is a major oversight for any company that designs materials or designs with materials: awareness of the potential significant missed opportunities in the mid- to long-term is growing rapidly. 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 licensing 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 the 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.

 

 

 

 

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 a comprehensive range of application areas 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.

 

 

This market report is released at a point in time where the 10-year outlook is prime for rapid adoption, with the headcount of the average MI firm growing by 91% from 2021-22. 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.

 

In recent years there has been significant progression in external companies providing MI solutions, more key partnerships and end-user engagements, new consortium and academic advancements, and new companies emerging. All of this is tracked, explained and analyzed throughout this industry leading report on the topic.

 

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.

 

Technology Trends:

 

Company Analysis:

 

Applications and Market Outlook

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