Chemicals are embedded in nearly every aspect of modern society, yet their production poses substantial sustainability concerns. Achieving a sustainable chemical industry requires detailed Life Cycle Assessment (LCA); however, current assessments face many unknowns due to limited, partly inconsistent, and untransparent data coverage since existing Life Cycle Inventory (LCI) databases account for only a tiny fraction of traded chemicals. Here, we introduce the Chemical RetrosYnthesiS for Transparent Assessment of Life-cycles (CRYSTAL) framework, which automatically generates consistent and transparent LCI data for organic chemicals based on their molecular structure using retrosynthesis and machine-learned gate-to-gate inventories. Using the predictive power of CRYSTAL, we create a consistent database for more than 70000 organic chemicals, comprising over 110000 transparent LCI datasets that quantify both feedstock and energy demands, together with associated auxiliary materials, biosphere flows, and waste flows. From this comprehensive database, we identify 50 key environmental hotspots driving high impacts of organic chemical production across multiple environmental categories and pivotal hub chemicals that are most critical for downstream chemical production. In providing this comprehensive data foundation, the CRYSTAL framework offers systematic guidance for targeted engineering and policy interventions. Its transparent, modular nature is designed to shift chemical LCA from a reliance on "unknown unknowns" to a collaboratively improvable mapping of "known unknowns".

翻译：化学品几乎渗透到现代社会的每个角落，但其生产过程带来了严重的可持续性问题。实现可持续的化学工业需要详细的生命周期评估（LCA）；然而，由于现有生命周期清单（LCI）数据库仅覆盖了交易化学品中的极小部分，导致数据覆盖有限、部分不一致且不透明，当前评估面临诸多未知因素。在此，我们提出了用于生命周期透明评估的化学逆合成（CRYSTAL）框架，该框架基于分子结构，利用逆合成和机器学习得到的门到门清单，自动生成一致且透明的LCI数据。借助CRYSTAL的预测能力，我们为超过70000种有机化学品创建了一个一致的数据库，包含超过110000个透明的LCI数据集，量化了原料和能源需求，以及相关的辅助材料、生物圈流和废物流。基于这一全面数据库，我们识别出50个关键环境热点，这些热点在多个环境类别中驱动着有机化学品生产的高影响，以及对于下游化学品生产最为关键的核心枢纽化学品。通过提供这一全面的数据基础，CRYSTAL框架为有针对性的工程和政策干预提供了系统性指导。其透明、模块化的特性旨在将化学品LCA从依赖"未知的未知"转变为可协作改进的"已知的未知"映射。