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市场调查报告书
商品编码
1660645

化学和材料研发虚拟模拟和建模技术的成长机会（2024-2029）

Growth Opportunities in Virtual Simulation and Modeling Technologies for Chemicals and Materials R&D, 2024-2029
出版日期: | 出版商: Frost & Sullivan | 英文 63 Pages | 商品交期: 最快1-2个工作天内

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简介目录

虚拟模拟和建模技术正在透过增强设计、优化流程和永续性来改变化学和材料的研究与开发

虚拟模拟和建模技术正在透过精确设计、测试和优化材料和工艺，改变化学和材料的研究与开发（R&D）。这些技术使得复杂化学反应的建模变得更加容易，并且能够准确预测材料特性，从而使生产过程更加高效，并减少了昂贵且耗时的物理实验的需要。

这份 Frost & Sullivan 报告检验了这些技术的变革潜力，特别关注实现永续性、效率和成本效益的创新。本报告全面分析了化学和材料研究与开发中面临的关键挑战，这些挑战可以透过虚拟模拟和建模技术来缓解。本报告探讨了模拟建模的最新进展，包括人工智慧（AI）和机器学习（ML）。此外，它还检验了这些技术在汽车、航太、製药和建筑等各个领域的应用。该报告概述了更广泛的生态系统，重点介绍了推动发展和采用的关键公司、学术贡献、专利状况和投资活动。它还强调了该行业的驱动因素和限制因素以及市场相关人员和相关人员可以利用的潜在成长机会。

目录

战略问题

  • 成长为何变得越来越艰难？
  • The Strategic Imperative 8（TM）
  • 三大策略挑战对化学和材料研发产业虚拟模拟和建模技术的影响
  • 成长机会推动Growth Pipeline Engine（TM）™
  • 调查方法

成长机会分析

  • 分析范围
  • 调查细分

成长要素

  • 关键问题
  • 成长动力
  • 成长抑制因素

技术分析

  • 技术进步

专利和研究论文

  • 专利概览
  • 研究论文概述

相关人员分析

  • 主要企业
  • 学术机构的重要研究贡献与突破
  • 主要相关人员之间的显着合作

案例研究

  • 案例研究1：Dotmatics 与BASF农业解决方案合作推出「数据到价值」倡议
  • 案例研究2：Kebotix 利用 AI 驱动的结构功能关係建模加速润滑剂开发
  • 案例研究3：Schrödinger 和 Evonik 利用 MD 模拟增强可回收轮胎材料的开发
  • 案例研究4：使用 BosonQ Psi 和 materialsIN Quantum ML 优化混凝土表面裂纹检测

资金筹措和投资

  • 重大投资

分析师观点与未来展望

  • 未来趋势
  • 影响分析
  • 分析师观点

成长机会领域

  • 成长机会1：用于 MD 模拟的量子启发演算法
  • 成长机会二：基于人工智慧的材料研发永续性评估工具
  • 成长机会3：机器人增强自动化，实现预测材料发现
  • 成长机会4：化学和材料製造流程优化的数位双胞胎

附录

  • 技术就绪程度 (TRL)：描述

后续步骤Next steps

  • 成长机会的好处和影响
  • 后续步骤Next steps
  • 免责声明
简介目录
Product Code: DB0B

Virtual simulation and modeling technologies are revolutionizing chemicals and materials R&D by enhancing design, optimizing processes, and driving sustainability

Virtual simulation and modeling technologies are transforming chemicals and materials research and development (R&D) by enabling precise design, testing, and optimization of materials and processes. These technologies facilitate the modeling of complex chemical reactions, allow for accurate predictions of material properties, and make production processes more efficient while curtailing the need for costly and time-consuming physical experiments.

This Frost & Sullivan report examines the transformative potential of these technologies, focusing particularly on innovations that enable sustainability, efficiency, and cost-effectiveness. The report provides a comprehensive analysis of the critical challenges faced in chemicals and materials R&D that can be mitigated through virtual simulation and modeling technologies. It explores the latest advancements in simulation and modeling, including artificial intelligence (AI) and machine learning (ML). Additionally, it examines the application of these technologies across various sectors, such as automotive, aerospace, pharmaceuticals, and construction. The report provides an overview of the broader ecosystem, highlighting the key players, academic contributions, patent landscapes, and investment activities driving development and adoption. It identifies the factors boosting and restraining the industry and the potential growth opportunities arising from this space for market players and stakeholders to leverage.

Table of Contents

Strategic Imperatives

  • Why Is It Increasingly Difficult to Grow?
  • The Strategic Imperative 8™
  • The Impact of the Top 3 Strategic Imperatives on Virtual Simulation and Modeling Technologies in the Chemicals and Materials R&D Industry
  • Growth Opportunities Fuel the Growth Pipeline Engine™
  • Research Methodology

Growth Opportunity Analysis

  • Scope of Analysis
  • Research Segmentation

Growth Generator

  • Key Challenges
  • Growth Drivers
  • Growth Restraints

Technology Analysis

  • Technology Advances

Patent and Research Publications

  • Overview of Patents
  • Overview of Research Publications

Stakeholder Analysis

  • Key Companies
  • Important Research Contributions and Breakthroughs from Academic Institutions
  • Notable Collaborations Between Key Stakeholders

Case Studies

  • Case Study 1: Dotmatics Deploys 'Data to Value' Initiative with BASF Agricultural Solutions
  • Case Study 2: Kebotix Accelerates Lubricant Development with AI-driven Structure-Function Relationship Modeling
  • Case Study 3: Schrodinger and Evonik Enhance Recyclable Tire Materials Development with MD Simulations
  • Case Study 4: BosonQ Psi and materialsIN Optimize Surface Crack Detection in Concrete with Quantum ML

Funding and Investments

  • Key Investments

Analyst Perspective and Future Outlook

  • Future-looking Trends
  • Impact Analysis
  • Analyst Perspective

Growth Opportunity Universe

  • Growth Opportunity 1: Quantum-inspired Algorithms for MD Simulations
  • Growth Opportunity 2: AI-powered Sustainability Assessment Tools for Materials R&D
  • Growth Opportunity 3: Robotics-enhanced Automation for Predictive Materials Discovery
  • Growth Opportunity 4: Digital Twins for Process Optimization in Chemicals and Materials Manufacturing

Appendix

  • Technology Readiness Levels (TRL): Explanation

Next Steps

  • Benefits and Impacts of Growth Opportunities
  • Next Steps
  • Legal Disclaimer