Causal AI for Accelerated Battery Materials Development

全球化学品行业规模达43亿美元，涵盖消费品、特种化学品、基础化学品、制药等广泛的应用范围。该行业公司的研发支出占收入的2%至15%不等。化学行业各垂直行业的研发时间表和成本都非常高。在农用化学品方面，一种新的保护产品的研制周期为11.3年以上，所需投资为1亿至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-这极大地缩短了新化学品上市的时间，至少需要5-10年。工业化学品研究的迭代周期长和劳动密集型的一个关键原因是依赖于实验研究。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，新材料或改良材料和工艺批量放大期间的质量和性能评估，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由于供应商遵循的生产工艺差异而导致的材料特性变化的影响所掩盖，在整个价值链的测试和测量技术以及复杂的多方面的副作用中，我们的目标是将因果推理的破坏性技术与人工智能模型相结合，以创建一种新形式的因果AI，能够为化学产品的开发提供洞察力。我们将专门为电池开发过程提供可操作的见解，目的是在对环保能源存储具有深远影响的尖端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.