Balancing the benefits and undesirable environmental impacts is essential for ensuring successful applications of emerging nano-agrochemicals. However, there is a lack of transparent and explainable trade-off methodologies in this safety-sensitive field. Here, an explainable machine learning-driven multi-objective optimization approach is proposed to maximize the performance and minimize undesirable implications of seed nanopriming. The root dry weight under salinity stress and the relative concentration of the constituent elements of used nanoparticles in shoots are considered prospective indicators of the benefit and risk, respectively. An ensemble strategy of model explanation, based on self-explainable models, is employed to obtain more reliable, unbiased, and trustworthy results with small datasets. Multi-objective optimization is employed to select potential treatments among numerous generated candidates based on the predictions of explainable machine learning models. Furthermore, model explanations are combined with prior knowledge to explain this selection process and elucidate the factors’ effects on the benefit and risk. The explanation results highlight the importance of considering the well-known concentration-dependent effect of nanoparticles in conjunction with other factors such as Zeta potential and surface area, which is further verified by statistical analysis. Together, this study provides a promising approach to accelerating the discovery, assessment, and regulation of nanomaterials and may facilitate their sustainable applications in agriculture and the environment.

中文翻译：

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通过可解释的机器学习优化纳米农用化学品的利益与风险权衡：超越浓度


平衡效益和不良环境影响对于确保新兴纳米农用化学品的成功应用至关重要。然而，在这个安全敏感领域缺乏透明且可解释的权衡方法。在这里，提出了一种可解释的机器学习驱动的多目标优化方法，以最大限度地提高性能并最大限度地减少种子纳米引发的不良影响。盐胁迫下的根干重和芽中所用纳米粒子的组成元素的相对浓度分别被认为是效益和风险的前瞻性指标。采用基于可自解释模型的模型解释集成策略，可以利用小数据集获得更可靠、无偏见和值得信赖的结果。采用多目标优化，根据可解释的机器学习模型的预测，在众多生成的候选者中选择潜在的治疗方法。此外，模型解释与先验知识相结合来解释这个选择过程并阐明因素对收益和风险的影响。解释结果强调了将众所周知的纳米粒子浓度依赖性效应与 Zeta 电位和表面积等其他因素结合起来考虑的重要性，这一点通过统计分析得到了进一步验证。总之，这项研究为加速纳米材料的发现、评估和监管提供了一种有前景的方法，并可能促进其在农业和环境中的可持续应用。