机器学习在纳米材料风险评估中的应用
Overview of Application of Machine Learning in Field of Nanomaterials Risk Assessment
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摘要: 纳米材料的广泛应用使其排放量急剧增大，将对生态环境及人类健康造成潜在危害。因此，对纳米材料进行风险评估十分必要。纳米材料风险评估中涉及的动物实验成本高，周期长，难以满足风险评估的数据需求。为填补数据空缺，研究人员引入了计算机建模的方法对纳米材料的理化性质、毒理学效应进行预测，取得了一定的效果。机器学习作为目前计算机建模的先进方法，在纳米材料风险评估领域展现出了良好的应用价值与前景。本文首先介绍了机器学习在纳米材料风险评估领域的应用方法及建模流程，其次结合国内外研究现状，综述了机器学习在纳米材料风险评估领域的应用实例，介绍了机器学习在纳米材料性质、毒理学效应预测中的主要进展，最后根据机器学习在纳米材料风险评估领域的应用现状，指出了该领域面临的挑战与未来发展前景。Abstract: The wide application of nanomaterials has led to a sharp increase in emissions, which has potential risk to the ecological environment and human health. Therefore, risk assessment of nanomaterials is particularly important. Animal experiment involved in the risk assessment of nanomaterials is costly and time-consuming, leading to difficulties in satisfying the data requirements of risk assessment. In order to fill the data gap, computer modeling methods have been introduced to predict the physical and chemical properties and toxicological effects of nanomaterials. Machine learning (ML), as the current advanced method of computer modeling, has shown good performance and prospects in the field of nanomaterials risk assessment. This article firstly introduces the application methods and modeling process of machine learning in nanomaterials risk assessment. Secondly, based on the research status at home and abroad, the examples of machine learning applied in current issue are reviewed to exhibit the main progress of machine learning in predicting nanomaterial properties and toxicological effects. Finally, according to current studying status of machine learning in nanomaterials, the prospects and challenges in this field is summarized.
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Key words:
- nanomaterials /
- machine learning /
- risk assessment /
- physicochemical property /
- toxicological effects
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