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.

Matter Horn通过借助机器学习（ML）智能调度实验来加速资源效率的材料设计。我们出生于UCL的AI中心，我们使材料公司获得了机器学习驱动的实验设计（DOE）的世界一流学术进步。我们提供了一个免费的软件，该软件与用于管理实验的用户友好数据平台无缝集成（“ MatterHorn Studio”，http://matterhorn.studio）。从历史上讲，开发资源效率的材料是理论和直觉驱动的过程。不可避免地，由于材料的复杂性不断增加，从这种理论分析和基于直觉的实验的回报正在减少，因此增加的发展成本会随着成功的较少而增加的成本。完全自动化的“ 4级”实验室可以使科学家们从事其他更复杂的任务。最近的报告要求材料开发10到100倍，而成本降低了10至100倍（utoronto.ca/）。 Matter Horn使这些实验室可以升级到1级和2级。我们已经观察到，雇用单个数据科学家通常是早期采用者的第一步，他们希望将其实验室升级到更高的水平。这些数据科学家是Matter Horn的主要受益者，因为它解决了他们确定要使用哪种算法以及如何安全管理数据的问题。向前迈进，我们想与更广泛的材料社区共享Matter Honn，以努力为资源高效的材料提供努力。这笔赠款将通过帮助我们开发平台，在该平台上，材料科学家可以在机器学习中迈出第一步，从而有助于实现这一目标。 Matter Horn将为多种物质领域（例如生物工程或固态化学）提供专门的模型。借助易于使用的平台和可访问的教程，我们希望激发和支持下一代物质科学家在数据驱动的材料发现方面发展他们的技能，并在英国和整个全球材料生态系统中发展，同时提供一个专门的平台，以照顾他们的数据管理，实验时间表和团队协作。