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市場調查報告書
商品編碼
1028744

材料信息學 2022-2032年

Materials Informatics 2022-2032

出版日期: | 出版商: IDTechEx Ltd. | 英文 173 Slides | 商品交期: 最快1-2個工作天內

價格
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  • 簡介
  • 目錄
簡介

標題
材料信息學 2022-2032
材料科學研發中以數據為中心的設計和發現方法。數據基礎設施和機器學習方面的顯著進步。玩家簡介、技術進步、市場前景、商業模式和案例研究。

材料信息學是一種研發範式轉變,能夠促進發現並縮短上市時間。

材料信息學代表了研發範式轉變,從根本上加快了從創新到市場的時間。有多種戰略方法,並且已經有一些顯著的成功案例;錯過這種轉變的代價將非常高。

這份報告提供了市場的關鍵見解和展望。通過技術初級訪談,讀者將詳細了解參與者、商業模式、技術和應用領域。

材料信息學 (MI) 涉及使用以數據為中心的方法進行材料科學研發。有多種戰略方法,並且已經有一些顯著的成功案例;採用現在正在進行中,如果錯過這種轉變將代價高昂。

這份報告提供了這一新興領域的關鍵見解和商業前景。在技術初級訪談的基礎上,讀者將詳細了解參與者、商業模式、技術和應用領域。

什麼是材料信息學?

材料信息學是使用以數據為中心的方法來推進材料科學。這可以採取多種形式並影響研發的所有部分(假設 - 數據處理和獲取 - 數據分析 - 知識提取)。

首先,MI 的基礎是使用數據基礎設施並利用機器學習解決方案來設計新材料、發現特定應用的材料以及優化它們的處理方式。

MI 可以加速創新的“正向”方向(實現輸入材料的特性),但理想化的解決方案是實現“反向”方向(根據所需特性設計材料)。

這不是直截了當的,仍處於初級階段。在許多情況下,數據基礎設施並不全面,而且 MI 算法對於給定的實驗數據來說往往過於不成熟。挑戰與其他 AI 主導的領域(例如自動駕駛汽車或社交媒體)不同,玩家經常處理稀疏、高維、有偏見和嘈雜的數據;利用領域知識是大多數方法的重要組成部分。

與某些人可能認為的相反,這不會取代研究科學家;如果整合得當,MI 將成為一套加速其研發過程的使能技術。對於許多人來說,夢想的最終目標是讓人類監督一個自動駕駛實驗室;儘管仍處於早期階段,但 MI 的發展促進了關鍵改進、分拆公司的形成和成功案例。

為什麼是現在?

這不是一種新方法,許多行業幾十年來一直採用類似的設計方法。但是,這種變革性技術目前正在影響材料科學領域的主要原因有以下三個:

  • 利用其他行業的 AI 驅動解決方案的改進。
  • 數據基礎設施的改進,從開放訪問的數據存儲庫到基於雲的研究平台。
  • 意識、教育以及跟上潛在創新步伐的需求。

IDTechEx 將承擔的項目分為八個主要類別,在報告中詳細列出。其中,在您的研發過程中採用先進的機器學習技術具有三個重複的優勢:加強對候選者的篩选和範圍研究領域,減少開發新材料的實驗數量(從而縮短上市時間),以及尋找新材料或關係。訓練數據可以基於內部實驗、計算模擬和/或來自外部數據存儲庫;增強的實驗室信息學和高通量實驗或計算可以成為許多項目不可或缺的一部分。

本報告著眼於 MI 機器學習的關鍵進展、成功案例以及最終用戶如何積極參與其中。

有哪些戰略方法?

對於任何設計材料或使用材料進行設計的公司來說,忽視這種研發轉型是一項重大疏忽。影響不會立即顯現,但從中長期來看,錯失的機會將是巨大的。這可能是將有競爭力的產品推向市場、開發供應鏈的多功能性、尋找下一代機會或培養多元化業務部門或材料組合的能力。

許多參與者已經開始採用三種核心方法:完全在內部運營、與外部公司合作或作為財團的一部分聯合起來。

報告中詳細評估了這些方法中的每一種;選擇開始採用 MI 很重要,選擇正確的路徑至關重要。

外部 MI 參與者可以來自多個起點,如下圖所示。MI 參與者還可以選擇成為擁有強大先進材料組合的許可公司,以及最終用戶將 MI 作為服務提供。從地域上看,許多采用這項技術的最終用戶是日本公司,許多新興的外部公司來自美國,最著名的財團和學術實驗室分佈在日本和美國。

此 IDTechEx 報告中包含所有主要公司的基於訪談的簡介。

不同MI玩家的總結。來源:材料信息學 2022-2032。

哪些應用領域成功地使用了它?

有機電子、電池成分、增材製造合金、聚氨酯配方和納米材料開發都是 MI 具有直接影響的領域的例子。廣泛的材料用例意味著從電子製造商到化工公司都在工業上採用。

存在普遍的挑戰,但每個應用領域都會有一定的考慮,無論是現有數據的可用性、領域知識、結構-屬性關係的複雜性等等。

本報告的最後一部分依次詳細介紹了每個應用領域,重點介紹了關鍵發展、商業用例和著名出版物。這讓最終用戶有機會專注於他們感興趣的特定領域的案例研究,並告知 MI 參與者要探索的領域。

我將從報告中學到什麼?

本市場報告發布的時間點是 10 年展望是快速採用的主要時間點。這份報告遠遠超出了互聯網上的內容,提供了基於初步訪談的關鍵商業前景以及該主題和許多相關應用領域的專業知識。

近年來,在提供 MI 解決方案的外部公司、更多關鍵合作夥伴和最終用戶參與、新的聯盟和學術進步以及新公司的出現方面取得了重大進展。這份行業領先的報告對所有這些內容進行了跟踪、解釋和分析。

提供了市場預測、參與者簡介、投資、路線圖和全面的公司名單,這對於任何想在該領域取得成功的人來說都是必不可少的讀物。

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目錄

1. 執行摘要和結論

  • 1.1. 什麼是材料信息學?
  • 1.2. 重要行業活動概覽
  • 1.3. 最新的關鍵新聞和發展
  • 1.4. 材料設計和開發各個階段的人工智能機會
  • 1.5。材料科學數據的問題
  • 1.6. 算法進步的關鍵領域
  • 1.7. 材料信息學玩家 - 類別
  • 1.8。戰略方法的結論和展望
  • 1.9. 主要球員
  • 1.10. 外部供應商的主要合作夥伴和客戶
  • 1.11. 著名的 MI 聯盟
  • 1.12。項目類別
  • 1.13. 公司簡介 - 指向 24 個 IDTechEx 公司簡介的鏈接

2. 簡介

  • 2.1. 常用縮寫
  • 2.2. 什麼是材料信息學?
  • 2.3. 材料信息學 - 為什麼是現在?
  • 2.4. ML/AI 在材料科學領域能做什麼?
  • 2.5. 材料信息學 - 類別定義
  • 2.6. 科學和工程中更廣泛的信息學空間
  • 2.7. 科學和工程中更廣泛的信息學空間
  • 2.8. MI 在整個材料範圍內的主要挑戰
  • 2.9. 傳統合成方法的閉環
  • 2.10. 高通量虛擬篩選 (HTVS)
  • 2.11. 機器學習在化學和材料科學中的優勢 - 加速
  • 2.12. ML 在化學和材料科學中的優勢 - 範圍界定和篩選
  • 2.13. ML 在化學和材料科學中的優勢 - 新物種和關係
  • 2.14. 化學和材料科學的數據基礎設施

3. 技術評估

  • 3.1. 概述
    • 3.1.1. 材料信息學算法的輸入和輸出
    • 3.1.2. 材料信息學需要什麼?
    • 3.1.3. 技術方法總結
    • 3.1.4. 實驗數據的不確定性破壞了分析
    • 3.1.5。QSAR 和 QSPR:回歸分析的作用
  • 3.2. MI算法
    • 3.2.1. MI算法概述
    • 3.2.2. 描述符和模型訓練
    • 3.2.3. 自動特徵選擇
    • 3.2.4. 開發與探索
    • 3.2.5. MI 算法的類型 - 監督與非監督
    • 3.2.6. MI算法的類型——典型的監督模型
    • 3.2.7. MI算法的類型 - 貝葉斯優化
    • 3.2.8. MI 算法的類型 - 無監督案例研究
    • 3.2.9. MI 算法的類型 - 生成式與判別式
    • 3.2.10。MI算法的類型——深度學習
    • 3.2.11。無機化合物的生成模型
    • 3.2.12。如何使用小型材料數據集
    • 3.2.13。使用小型材料數據集進行深度學習
    • 3.2.14。算法進步的關鍵領域
  • 3.3. 建立數據基礎設施
    • 3.3.1. 數據基礎設施對 MI 至關重要
    • 3.3.2. 針對化學和材料科學的發展
  • 3.4. 外部數據庫
    • 3.4.1. 數據存儲庫 - 組織
    • 3.4.2. 數據存儲庫 - 趨勢
    • 3.4.3. 利用數據存儲庫
    • 3.4.4. 文本提取和分析
    • 3.4.5。數據挖掘出版物和專利
    • 3.4.6. 註釋和提取相關信息
  • 3.5。MI 與物理實驗和表徵
    • 3.5.1. 獲得大量物理實驗數據
    • 3.5.2. 高通量光譜
    • 3.5.3. 原位光譜學發展
  • 3.6. MI與計算材料科學
    • 3.6.1. 化學和材料科學研發模擬
    • 3.6.2. ICME 和機器學習的作用
    • 3.6.3. 生成和使用最大的計算材料科學數據庫
    • 3.6.4. 利用基於雲的仿真進行探索性設計
    • 3.6.5。利用量子計算的潛力
    • 3.6.6。材料發現的計算自治
  • 3.7. 自主實驗室
    • 3.7.1. 未來 - 完全自主的實驗室
    • 3.7.2. 未來——“電腦”
    • 3.7.3. 探索化學空間的化學計算機
    • 3.7.4。實驗室自動化的工作流程管理
    • 3.7.5。自主高通量實驗
    • 3.7.6。商業自動駕駛實驗室
    • 3.7.7。移動自主機器人
    • 3.7.8。逆向合成到機器人執行
    • 3.7.9。化學自主的三大技術支柱

4. 公司分析

  • 4.1. 重要行業活動概覽
  • 4.2. 最新的關鍵新聞和發展
  • 4.3. 材料信息學玩家 - 類別
  • 4.4. 戰略方法的結論和展望
  • 4.5。材料信息學玩家 - 概述
  • 4.6. 外部供應商的主要合作夥伴和客戶
  • 4.7. 與工程仿真軟件合作
  • 4.8. 私人公司籌集的資金
  • 4.9. 顯著的市場增長
  • 4.10。完整的球員名單 - 私人公司
  • 4.11. 主要球員
  • 4.12. 完整球員名單 - 公共組織
  • 4.13. 支持建立內部能力
  • 4.14. 在內部進行操作
  • 4.15。商業逆合成預測因子
  • 4.16。著名的 MI 聯盟
  • 4.17。公私合作
  • 4.18。材料基因組計劃 (MGI)
  • 4.19. 材料基因組工程 (MGE)
  • 4.20。世界各地的其他關鍵舉措和研究中心
  • 4.21。通過合成生物學開發材料
  • 4.22。COVID-19 和材料信息學 (MI)
  • 4.23。逐個行業的影響

5. 應用和案例研究

  • 5.1. 案例研究 - 概述
  • 5.2. 市場預測
  • 5.3. 材料信息學路線圖
  • 5.4. 項目類別
  • 5.5。材料信息學 - 按成熟度劃分的市場滲透率
  • 5.6. 顯微鏡:加速過程和合成用途
  • 5.7. 改進同步加速器光源的使用
  • 5.8。鋁和鈦合金
  • 5.9. 金屬玻璃合金
  • 5.10. 鎳基高溫合金
  • 5.11. 高熵合金
  • 5.12。金屬間化合物
  • 5.13. 塗料
  • 5.14。有機電子 - OLED
  • 5.15。有機電子 - RFID
  • 5.16。有機電子 - OPV
  • 5.17。有機電子 - 超越
  • 5.18。催化劑
  • 5.19. 離子液體
  • 5.20。超導體
  • 5.21。有毒化學品
  • 5.22。儲能:鋰離子電池
  • 5.23。聚合物和復合材料
  • 5.24。高分子信息學
  • 5.25。潤滑劑
  • 5.26。熱電
  • 5.27。有機金屬化合物
  • 5.28。二維材料
  • 5.29。納米加工
  • 5.30。量子點
  • 5.31。其他納米材料
  • 5.32。金屬-絕緣體過渡化合物
  • 5.33。光吸收器和太陽能電池
  • 5.34。鈣鈦礦光伏
  • 5.35。自組裝單分子層
  • 5.36。超材料

6. 公司簡介

  • 6.1. 公司簡介 - 指向 24 個 IDTechEx 公司簡介的鏈接
目錄
Product Code: ISBN 9781913899738
1028744

Title:
Materials Informatics 2022-2032
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 represents an R&D paradigm shift by fundamentally accelerating the time from innovation to market. There are multiple strategic approaches and already some notable success stories; missing this transition will be very costly.

This report provides key insights and outlooks on the market. Through technical primary interviews, readers will get a detailed understanding of the players, business models, technology, and the application areas.

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 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.

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.

                  Summary of different MI players. Source: Material Informatics 2022-2032.

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 informs MI players on what areas to explore.

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.

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 analysed 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.

Analyst access from IDTechEx

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TABLE OF CONTENTS

1. EXECUTIVE SUMMARY AND CONCLUSIONS

  • 1.1. What is materials informatics?
  • 1.2. Overview of significant industry activity
  • 1.3. Latest key news and developments
  • 1.4. AI opportunities at every stage of materials design and development
  • 1.5. Problems with materials science data
  • 1.6. Key areas of algorithm advancements
  • 1.7. Materials informatics players - categories
  • 1.8. Conclusions and outlook for strategic approaches
  • 1.9. Main players
  • 1.10. Key partners and customers of external providers
  • 1.11. Notable MI consortia
  • 1.12. Project categories
  • 1.13. Company Profiles - links to 24 IDTechEx company profiles

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 materials 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. Overview
    • 3.1.1. Inputs and outputs of materials informatics algorithms
    • 3.1.2. What is needed for materials informatics?
    • 3.1.3. Summary of technology approaches
    • 3.1.4. Uncertainty in Experimental Data Undermines Analysis
    • 3.1.5. QSAR and QSPR: The role of regression analysis
  • 3.2. MI algorithms
    • 3.2.1. Overview of MI algorithms
    • 3.2.2. Descriptors and training a model
    • 3.2.3. Automated feature selection
    • 3.2.4. Exploitation vs Exploration
    • 3.2.5. Types of MI algorithms - supervised vs unsupervised
    • 3.2.6. Types of MI algorithms - typical supervised models
    • 3.2.7. Types of MI algorithms - Bayesian optimization
    • 3.2.8. Types of MI algorithms - unsupervised case study
    • 3.2.9. Types of MI algorithms - generative vs discriminative
    • 3.2.10. Types of MI algorithms - deep learning
    • 3.2.11. Generative Models for Inorganic Compounds
    • 3.2.12. How to work with small material datasets
    • 3.2.13. Deep learning with small material datasets
    • 3.2.14. Key areas of algorithm advancements
  • 3.3. Establishing a data infrastructure
    • 3.3.1. A data infrastructure is critical for MI
    • 3.3.2. Developments targeted for chemical and materials science
  • 3.4. External databases
    • 3.4.1. Data repositories - organisations
    • 3.4.2. Data repositories - trends
    • 3.4.3. Leveraging data repositories
    • 3.4.4. Text Extraction and Analysis
    • 3.4.5. Data mining publications and patents
    • 3.4.6. Annotating and extracting the relevant information
  • 3.5. MI with physical experiments and characterisation
    • 3.5.1. Achieving high-volumes of physical experimental data
    • 3.5.2. High-throughput spectroscopy
    • 3.5.3. In-situ spectroscopy developments
  • 3.6. MI with computational materials science
    • 3.6.1. Simulations for chemistry and materials science R&D
    • 3.6.2. ICME and the role of machine learning
    • 3.6.3. Generating and Using the Largest Computational Materials Science Database
    • 3.6.4. Explorative Design Utilising Cloud-Based Simulation
    • 3.6.5. The potential in leveraging quantum computing
    • 3.6.6. Computation Autonomy for Materials Discovery
  • 3.7. Autonomous labs
    • 3.7.1. The future - fully autonomous labs
    • 3.7.2. The future - "Chemputer"
    • 3.7.3. A Chemputer to explore chemical space
    • 3.7.4. Workflow management for laboratory automation
    • 3.7.5. Autonomous High Throughput Experimentation
    • 3.7.6. Commercial self-driving-laboratories
    • 3.7.7. Mobile Autonomous Robot
    • 3.7.8. Retrosynthesis through to robot execution
    • 3.7.9. Three technology pillars to chemical autonomy

4. COMPANY ANALYSIS

  • 4.1. Overview of significant industry activity
  • 4.2. Latest key news and developments
  • 4.3. Materials informatics players - categories
  • 4.4. Conclusions and outlook for strategic approaches
  • 4.5. Materials Informatics players - Overview
  • 4.6. Key partners and customers of external providers
  • 4.7. Partnerships with engineering simulation software
  • 4.8. Funding raised by private companies
  • 4.9. Significant market growth
  • 4.10. Full player list - private companies
  • 4.11. Main players
  • 4.12. Full player list - public organisations
  • 4.13. Support in building in-house capability
  • 4.14. Taking the operation in-house
  • 4.15. Commercial retrosynthesis predictors
  • 4.16. Notable MI consortia
  • 4.17. Public-private collaborations
  • 4.18. Materials Genome Initiative (MGI)
  • 4.19. Materials Genome Engineering (MGE)
  • 4.20. Additional key initiatives and research centres around the world
  • 4.21. Materials development via synthetic biology
  • 4.22. COVID-19 and materials informatics (MI)
  • 4.23. Sector-by-sector impact

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. Microscopy: Accelerating process and synthetic uses
  • 5.7. Improving the use of Synchrotron Light Sources
  • 5.8. Aluminium and titanium alloys
  • 5.9. Metallic glass alloys
  • 5.10. Nickel-base superalloys
  • 5.11. High-entropy alloys
  • 5.12. Intermetallics
  • 5.13. Coatings
  • 5.14. Organic electronics - OLED
  • 5.15. Organic electronics - RFID
  • 5.16. Organic electronics - OPV
  • 5.17. Organic electronics - beyond
  • 5.18. Catalysts
  • 5.19. Ionic Liquids
  • 5.20. Superconductors
  • 5.21. Toxic chemicals
  • 5.22. Energy storage: Lithium-ion batteries
  • 5.23. Polymers and composites
  • 5.24. Polymer Informatics
  • 5.25. Lubricants
  • 5.26. Thermoelectrics
  • 5.27. Organometallics
  • 5.28. 2D materials
  • 5.29. Nanofabrication
  • 5.30. Quantum Dots
  • 5.31. Other nanomaterials
  • 5.32. Metal-insulator transition compounds
  • 5.33. Light absorbers and solar cells
  • 5.34. Perovskite photovoltaics
  • 5.35. Self-assembled monolayers
  • 5.36. Metamaterials

6. COMPANY PROFILES

  • 6.1. Company Profiles - links to 24 IDTechEx company profiles