Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current generative AI models for materials discovery, which now drive exploration of vast chemical and structural spaces, optimize candidates exclusively for structural stability and functional properties, with no integration of environmental assessment at any stage of the design loop. Prospective and ex-ante life cycle assessment methods exist and have been applied to emerging technologies, but they operate as standalone downstream analyses, not as active constraints within generative or active-learning pipelines. The result is that environmental feedback, even when produced, arrives after design decisions have been made rather than informing them. The disconnect between atomic-scale design and lifecycle assessment (LCA) reflects fundamental challenges: (i) data scarcity across heterogeneous sources, (ii) scale gaps from atoms to industrial systems, (iii) uncertainty in synthesis pathways, and (iv) the absence of frameworks that co-optimize performance with environmental impact. In this Perspective, we propose integrating upstream ML-assisted materials discovery with downstream LCA into the ML-LCA framework, comprising five components: information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning polymers, glass, photoresists, and cement demonstrate both necessity and feasibility while identifying material-specific integration challenges.

翻译：人工智能（AI）已经变革了材料发现领域，通过生成模型和替代筛选实现了化学空间的快速探索。然而，当前用于材料发现的生成式AI模型（现已驱动着对广阔化学和结构空间的探索）仅基于结构稳定性和功能特性来优化候选材料，在设计循环的任何阶段均未整合环境评估。前瞻性和预判性生命周期评估方法虽已存在并应用于新兴技术，但它们作为独立的下游分析运行，而非作为生成或主动学习流程中的主动约束条件。其结果是，即使产生了环境反馈，也是在设计决策做出之后才出现，而非在设计过程中提供参考。原子尺度设计与生命周期评估之间的脱节反映了根本性挑战：（i）跨异构源的数据稀缺性，（ii）从原子到工业系统的尺度鸿沟，（iii）合成路径的不确定性，以及（iv）缺乏能同时优化性能与环境影响的协同框架。在本展望中，我们提出将上游机器学习辅助的材料发现与下游生命周期评估整合到ML-LCA框架中，该框架包含五个组成部分：用于构建材料-环境知识库的信息提取模块、链接属性与可持续性指标的协同数据库、桥接原子属性与生命周期影响的多尺度模型、带有不确定性量化的制造路径集成预测，以及支持性能-可持续性同步导航的不确定性感知优化。涵盖聚合物、玻璃、光刻胶和水泥的案例研究既证明了必要性与可行性，也识别了材料特定的整合挑战。