Digital navigation of chemical space for function

功能化学空间的数字导航

• 批准号: EP/V026887/1
• 负责人: Matthew Rosseinsky
• 金额: $ 1108.47万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV026887%2F1
• 关键词: Digital; navigation; chemical; space; function

Materials both enable the technologies we rely on today and drive advances in scientific understanding. The new scientific phenomena produced by novel materials (for example, lithium transition metal oxides) enable the creation of technologies (electric vehicles), emphasising the connection between the capability to create new materials and economic prosperity. New materials offer a route to clean growth that is essential for the future of society in the face of climate change and resource scarcity.To harness the power of functional materials for a sustainable future, we must improve our ability to identify them. This is a daunting task, because materials are assembled from the vast and largely unknown coupled chemical and structural spaces. As a result, we are forced to work mostly by analogy with known materials to identify new ones. This necessarily incremental approach restricts the diversity of outcome from both scientific and technological perspectives. We need to be able to design materials beyond this "paradigm of analogues" if we are to exploit their potential to tackle societal challenges.This project will transform our ability to access functional materials with unprecedented chemical and structural diversity by fusing physical and computer science. We will develop a digital discovery platform that will advance the frontier of knowledge by creating new materials classes with novel structure and bonding and tackle key application challenges, thus focussing the developed capability on well-defined targets of scientific novelty and application performance. The discovery platform will be shaped by the need to identify new materials and by the performance needed in applications. This performance is both enabled by and creates the need for the new materials classes, emphasising the interdependent nature of the project strands.We will strengthen cutting-edge physical science (PS) capability and thinking by exploiting the extensive synergies with computer science (CS), to boost the ability of the physical scientist to navigate the space of possible materials. Computers can assimilate large databases and handle multivariate complexity in a complementary way to human experts, so we will develop models that fuse the knowledge and needs from PS with the insights from CS on how to balance precision and efficiency in the quest for promising regions in chemical space. The development of mixed techniques that use explainable symbolic AI-based automated reasoning and model construction approaches coupled with machine learning is just one example that illustrates how this opportunity goes far beyond interpolative machine learning, itself valuable as a baseline evaluation of our current knowledge.By working collaboratively across the CS/PS interface, we can digitally explore the unknown space, informed and guided by PS expertise, to transform our ability to harvest disruptive functional materials. Only testing against the hard constraints of PS novelty and functional value will drive the discovery platform to the level needed to deliver this aim. As we are navigating uncharted space, the tools and models that we develop will be compass-like guides, rather than satellite navigation-like directors, for the expert PS team. The magnitude of the opportunity to transform materials discovery produces intense international competition with significant investments at pace from industry (e.g., Toyota Research Institute $1bn) and government (e.g., DoE $27m; a new centre at NIMS, Japan, both in 2019). Our transformative vision exploits recent UK advances in autonomous robotic researchers and artificial intelligence-guided identification of outperforming functional materials that are not based on analogues. The scale and flexibility of this PG will ensure the UK is at the forefront of this vital area.

材料既使我们今天所依赖的技术成为可能，也推动了科学认识的进步。由新材料（例如锂过渡金属氧化物）产生的新科学现象使技术（电动汽车）的创造成为可能，强调了创造新材料的能力与经济繁荣之间的联系。面对气候变化和资源短缺，新材料为清洁增长提供了一条途径，这对未来的社会至关重要。为了利用功能材料的力量实现可持续的未来，我们必须提高识别它们的能力。这是一项艰巨的任务，因为材料是由巨大且大部分未知的耦合化学和结构空间组装而成的。因此，我们被迫主要通过与已知材料的类比来识别新的材料。从科学和技术的角度来看，这种必然的渐进式方法限制了结果的多样性。如果我们要利用它们的潜力来应对社会挑战，我们需要能够设计出超越这种“类似物范例”的材料。这个项目将通过融合物理和计算机科学，改变我们获取具有前所未有的化学和结构多样性的功能材料的能力。我们将开发一个数字发现平台，通过创建具有新颖结构和键合的新材料类别来推进知识的前沿，并解决关键应用挑战，从而将开发的能力集中在科学新颖性和应用性能的明确目标上。发现平台将被识别新材料的需求和应用所需的性能所塑造。这种性能既由新材料类实现，也创造了对新材料类的需求，强调了项目链的相互依赖性质。我们将加强前沿物理科学（PS）的能力和思维，利用与计算机科学（CS）的广泛协同作用，提高物理科学家在可能的材料空间中导航的能力。计算机可以吸收大型数据库，并以一种与人类专家互补的方式处理多元复杂性，因此我们将开发模型，将PS的知识和需求与CS的见解融合在一起，以便在寻求化学领域有前景的领域时平衡精度和效率。使用可解释的符号人工智能自动推理和模型构建方法与机器学习相结合的混合技术的发展只是一个例子，说明这个机会远远超出了插值机器学习，它本身作为我们当前知识的基线评估是有价值的。通过CS/PS界面的协同工作，我们可以在PS专业知识的指导下，以数字方式探索未知空间，从而改变我们收获破坏性功能材料的能力。只有针对PS新颖性和功能价值的硬约束进行测试，才能推动发现平台达到实现这一目标所需的水平。当我们在未知的空间中航行时，我们开发的工具和模型将是像指南针一样的向导，而不是像卫星导航一样的指示器，用于专业的PS团队。转变材料发现的巨大机会引发了激烈的国际竞争，行业（例如丰田研究所10亿美元）和政府（例如美国能源部2700万美元；2019年在日本NIMS建立一个新中心）的大量投资。我们的变革性愿景利用了英国在自主机器人研究人员和人工智能引导下识别性能优异的功能材料方面的最新进展，这些材料不是基于类似物。该项目的规模和灵活性将确保英国在这一重要领域走在前列。

项目成果

Control of Polarity in Kagome-NiAs Bismuthides.

Kagome-NiAs 铋化物极性的控制。

• DOI: 10.1002/anie.202403670
• 发表时间: 2024
• 期刊: Angewandte Chemie (International ed. in English)
• 作者: Gibson QD

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

• DOI: 10.1021/acs.jpca.3c07129
• 发表时间: 2024-01
• 期刊: The Journal of Physical Chemistry. a
• 作者: Patrick W V Butler;R. Hafizi;Graeme M. Day

Automated Technology for Verification and Analysis - 20th International Symposium, ATVA 2022, Virtual Event, October 25-28, 2022, Proceedings

验证和分析自动化技术 - 第 20 届国际研讨会，ATVA 2022，虚拟活动，2022 年 10 月 25-28 日，会议记录

• DOI: 10.1007/978-3-031-19992-9_19
• 发表时间: 2022
• 作者: Hahn E

A Pyrene-4,5,9,10-Tetraone-Based Covalent Organic Framework Delivers High Specific Capacity as a Li-Ion Positive Electrode.

• DOI: 10.1021/jacs.2c02196
• 发表时间: 2022-06-01
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Gao H;Neale AR;Zhu Q;Bahri M;Wang X;Yang H;Xu Y;Clowes R;Browning ND;Little MA;Hardwick LJ;Cooper AI
• 通讯作者: Cooper AI

Machine Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

通过主动学习有机晶体结构预测景观的机器学习潜力

• DOI: 10.26434/chemrxiv-2023-97rmb
• 发表时间: 2023
• 作者: Butler P