Matterhorn Studio: Your first step towards AI-driven sustainable materials development (with a focus on scale-up of bioengineering)

Matterhorn Studio：迈向人工智能驱动的可持续材料开发的第一步（重点是生物工程的规模化）

• 批准号: 10076202
• 金额: $ 6.3万
• 依托单位: ZEITLER TECHNOLOGIES LIMITED
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10076202
• 关键词: Matterhorn; Studio; Your; first; step

Matterhorn accelerates resource efficient materials design, by intelligently scheduling experiments with the help of Machine Learning (ML). Born out of UCL's AI Centre, we give materials companies access to world-class academic advances in Machine Learning driven Design of Experiments (DOE). We provide a freely available software that seamlessly integrates with a user-friendly data platform for managing experiments ("Matterhorn STUDIO", http://matterhorn.studio).Historically, developing resource efficient materials is a process driven by theory and intuition. Inevitably, due to the ever increasing complexity of the materials, returns from such theoretical analysis and intuition-based experimentation are diminishing, therefore increasing development costs with fewer successes.Recent advances in DOE methods have played a central role in revolutionising laboratories, for example, pharmaceutical industries can afford to implement a "closed loop" of experimentation, reaching "Level 4" of Matthew Reeve's Digital Maturity Framework. A fully automated "Level 4" laboratory frees the scientists to work on other more complex tasks. Recent reports claim 10 to 100 times faster materials development with a 10 to 100 times reduction of costs (acceleration.utoronto.ca/).Unfortunately, most labs cannot afford the investments required to achieve "closed loop" experimentation. Matterhorn enables these labs to upgrade to Level 1 and 2 instead. We have observed that hiring a single data scientist is often the first step for early adopters, that want to upgrade their labs to higher levels. These data scientists are the main beneficiary of Matterhorn, since it solves their problem of deciding which algorithm to use and how to securely manage the data.Moving forward, we would like to share Matterhorn with the wider materials community in their efforts towards resource efficient materials. This grant will help make that possible by helping us develop our platform where materials scientist can make their first steps in machine learning. Matterhorn will provide dedicated models for a wide set of material fields such as bioengineering or solid-state chemistry. With the help of an easy to use platform and accessible tutorials, we hope to inspire and support the next generation of material scientist to develop their skills in data-driven materials discovery and advance progress in the UK and global materials ecosystem as a whole, while providing a dedicated platform to take care of their data-management, experimentation schedule and team collaboration.

Matterhorn 通过在机器学习 (ML) 的帮助下智能安排实验，加速资源高效的材料设计。我们诞生于伦敦大学学院的人工智能中心，为材料公司提供机器学习驱动的实验设计 (DOE) 领域世界一流的学术进展。我们提供免费软件，可与用户友好的数据平台无缝集成，用于管理实验（“Matterhorn STUDIO”，http://matterhorn.studio）。从历史上看，开发资源高效材料是一个由理论和直觉驱动的过程。不可避免的是，由于材料的复杂性不断增加，此类理论分析和基于直觉的实验的回报正在减少，因此开发成本增加，成功率却越来越低。美国能源部方法的最新进展在实验室革命中发挥了核心作用，例如，制药行业有能力实施“闭环”实验，达到 Matthew Reeve 数字成熟度框架的“4 级”。完全自动化的“4 级”实验室使科学家能够腾出时间来完成其他更复杂的任务。最近的报告声称材料开发速度提高了 10 到 100 倍，成本降低了 10 到 100 倍 (acceleration.utoronto.ca/)。不幸的是，大多数实验室无法承担实现“闭环”实验所需的投资。马特宏峰使这些实验室能够升级到 1 级和 2 级。我们观察到，对于想要将实验室升级到更高水平的早期采用者来说，聘请一名数据科学家通常是第一步。这些数据科学家是 Matterhorn 的主要受益者，因为它解决了他们决定使用哪种算法以及如何安全管理数据的问题。展望未来，我们希望与更广泛的材料界分享 Matterhorn 在开发资源高效材料方面所做的努力。这笔赠款将帮助我们开发我们的平台，让材料科学家可以在机器学习方面迈出第一步，从而帮助实现这一目标。马特宏峰将为生物工程或固态化学等广泛的材料领域提供专用模型。借助易于使用的平台和易于访问的教程，我们希望激励和支持下一代材料科学家发展数据驱动材料发现的技能，并推动英国和全球材料生态系统的整体进步，同时提供专用平台来处理数据管理、实验安排和团队协作。