Konstantopoulos Georgios, Koumoulos Elias P, Charitidis Costas A
RNANO Lab-Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece.
Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium.
Nanomaterials (Basel). 2022 Aug 1;12(15):2646. doi: 10.3390/nano12152646.
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
机器学习一直是一个新兴的科学领域，满足材料科学与制造领域现代多学科的需求。基于数据分析的纳米材料特性分类和映射，将在提高有效资源管理意识的基础上确保安全和绿色制造。利用人工智能（AI）赋能的预测建模工具，在材料发现和优化方面开辟了新途径，同时还能进一步推动前沿的、数据驱动的纳米材料定制行为特征设计，以满足应用环境的特殊需求。材料行为的物理和数学表示的先前知识，以及已生成测试数据的利用，受到了科学家们的特别关注。然而，对可用信息的探索并不总是易于管理的，而机器智能可以通过高通量多维搜索探索能力高效地（计算资源、时间）应对这一挑战。此外，与环境和生物体的生化相互作用建模已被证明能够将化学结构与接触后的急性或可耐受影响联系起来。因此，在本综述中，提供了近期计算进展的总结，旨在涵盖卓越的研究，并提出在先进纳米材料制造和纳米信息学领域实现无偏见、分散式和数据驱动决策方面面临的挑战，并指出实现快速、安全和设计循环型纳米材料所需的步骤。
