Causal AI for Accelerated Battery Materials Development

全球化工产业规模达4.3万亿美元，涵盖了广泛的应用范围，包括消费品、特种化学品、基础化学品、药品等。该行业的公司在研发上的支出占收入的比例从2%到15%不等。众所周知，化工行业各个垂直领域的开发时间和成本都很高。在农用化学品方面，一种新的保护产品的交付时间超过11.3年，需要投资1 - 2.5亿美元。在电池材料中，评估每种新材料或配方迭代所需的循环数据可能需要长达数月的时间，这大大缩短了新化学物质上市的时间，至少需要5-10年。工业化学研究迭代周期长、劳动强度大的一个关键原因是对实验研究的依赖。大多数化学研究和开发——无论是新材料的基础研究、新供应商的资格认证、新材料或改进材料和工艺的批量扩大期间的质量和性能评估，还是根据特定客户要求调整现有平台——都涉及实验室实验。这是由于众所周知的理论化学反应被材料性能变化的影响所掩盖，这些变化是由于供应商所遵循的生产过程的差异、整个价值链的测试和测量技术以及复杂的多步骤生产过程中的副反应。我们的目标是将因果推理的颠覆性技术与人工智能模型相结合，创造一种新的因果人工智能形式，能够为化学产品的发展提供洞察力。我们将专门为电池开发过程提供可操作的见解，目的是在对环境友好型能源存储产生深远影响的前沿领域验证我们的模型。然后，该模型将扩展到化学工业中的其他商业应用，以加快其研发时间表。

The global chemicals industry stands at US$4.3T, covering a broad application range encapsulating consumer products, speciality chemicals, basic chemicals, pharmaceuticals, etc. The spending on research and development varies from 2% to up to 15% of revenue in companies in this industry.Development timelines and costs across verticals within the chemical industry are notoriously high. In agrochemicals, the lead time for a new protection product stands at over 11.3 years with required investment of $100-250 million. In battery materials, cycling data needed to assess each new material or formulation iteration can take up to months - contributing greatly to the time to market for new chemistries being a minimum of 5-10 years.A key reason for the long iteration cycles and labour intensiveness of industrial chemical research is the reliance on experimental research. Most chemical research and development - be it fundamental research for novel materials, qualification of new suppliers, quality and performance assessment during batch size scale-up for new or modified materials and processes, tweaking of existing platforms as per specific customer requirements - involves laboratory experimentation. This is due to well-understood theoretical chemical reactions being eclipsed by the effects of variations in material properties due to differences in production processes followed by suppliers, in testing and measurement techniques across the value chain and side reactions across complex multi-step production processes.Our goal is to integrate the disruptive technology of causal inference with artificial intelligence models to create a new form of Causal AI that is able to provide insight into the development of chemical products. We will specifically provide actionable insights into the process of battery development with the goal of validating our model in a cutting edge sector that has far reaching impacts on environmentally friendly energy storage. This model will then be extended to other commercial applications within the chemicals industry to accelerate their R&D timescales.