Machine learning is now applied in virtually every sphere of life for data analysis and interpretation. The main strengths of the method lie in the relative ease of the construction of its structures and its ability to model complex non-linear relationships and behaviours. While application of existing materials have enabled significant technological advancement there are still needs for novel materials that will enable even greater achievement at lower cost and higher effectiveness. The physics underlining the phenomena involved in materials processing and behaviour however still pose considerable challenge and yet require solving. Machine learning can facilitate the achievement of these new aspirations and desires by learning from existing knowledge and data to fill in gaps that have so far been intractable for various reasons including cost and time. This paper reviews the applications of machine learning to various aspects of materials design, processing, characterisation, and some aspects of fabrication and environmental impact evaluation.

机器学习现在几乎应用于生活的每个领域，用于数据分析和解释。该方法的主要优势在于其结构的构建相对容易，并且能够对复杂的非线性关系和行为进行建模。虽然现有材料的应用已经实现了重大的技术进步，但仍然需要能够以更低的成本和更高的效率取得更大成就的新型材料。然而，强调材料加工和行为所涉及的现象的物理学仍然提出了相当大的挑战，但仍需要解决。机器学习可以通过从现有知识和数据中学习来填补迄今为止因各种原因（包括成本和时间）而难以解决的空白，从而促进实现这些新的愿望和愿望。本文回顾了机器学习在材料设计、加工、表征以及制造和环境影响评估的某些方面的各个方面的应用。