One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

针对石化塑料在自然环境中积累的一种可能的解决方案是使用天然成分开发可生物降解的塑料替代品。然而，发现满足特定性能（例如光学透明度、阻燃性和机械弹性）的全天然替代品仍然具有挑战性，这些性能使石化塑料取得了成功。当前的方法仍然依赖于迭代优化实验。在这里，我们展示了一个集成的工作流程，它将机器人技术和机器学习相结合，以加速发现具有可编程光学、热学和机械性能的全天然塑料替代品。首先，命令自动移液机器人准备 286 个具有各种特性的纳米复合材料薄膜，以训练支持向量机分类器。接下来，通过 14 个带有数据增强的主动学习循环，分阶段制造了 135 个纯天然纳米复合材料，建立了人工神经网络预测模型。我们证明，预测模型可以执行双向设计任务：（1）根据全天然纳米复合材料的成分预测其物理化学性质，以及（2）自动化可生物降解塑料替代品的逆向设计，以满足各种用户特定的要求。通过利用模型的预测能力，我们制备了几种全天然替代品，可以替代具有类似特性的不可生物降解的替代品。我们的方法集成了机器人辅助实验、机器智能和模拟工具，以从普遍认为安全的数据库中获取的构建块为基础，加速环保塑料替代品的发现和设计。