Jain Ayush, Armstrong Connor D, Joseph V Roshan, Ramprasad Rampi, Qi H Jerry
School of Material Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
ACS Appl Mater Interfaces. 2024 Apr 10;16(14):17992-18000. doi: 10.1021/acsami.4c00759. Epub 2024 Mar 27.
Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.
增材制造（AM）可以通过热塑性和热固性聚合物所具有的多样特性以及共聚带来的更多优势而得到推进。然而，适用于增材制造的聚合物材料种类有限，并且对于特定应用而言可能并不总是理想的。此外，潜在单体及其组合数量众多，使得通过实验确定树脂成分极其耗时且成本高昂。为了克服这些挑战，我们开发了一种主动学习（AL）方法，以在从刚性到弹性体的三元单体空间中有效选择成分。我们的主动学习算法动态地为后续测试轮次建议单体组成比例，使我们能够高效构建一个强大的机器学习（ML）模型，该模型能够基于成分预测聚合物性能，包括杨氏模量、峰值应力、极限应变和邵氏A硬度，同时将实验次数减至最少。作为我们方法有效性的一个例证，我们使用该机器学习模型来驱动针对特定性能（即杨氏模量）的材料选择。结果表明，该机器学习模型可用于选择杨氏模量目标值至少10%范围内的材料成分。然后，我们使用由该机器学习模型设计的材料3D打印出一个具有柔软“皮肤”和刚性“骨骼”的多材料“手”。这项工作展示了一个很有前景的工具，可用于根据用户规格进行明智的增材制造材料选择，并利用有限的单体空间加速材料发现。
