Machine Learning for Explainable Roundtrip Polymer Reaction Engineering

聚合物是每个人日常生活中的重要材料，在许多技术应用中也是如此。它们具有广泛的性质，可以根据生产过程的类型和条件进行定制，理想情况下是通过聚合过程的建模来实现。ML-PRE项目旨在弥合最先进的机器学习(ML)方法及其在聚合物反应工程(PRE)建模和优化中的应用之间的差距。这将实现一种称为往返预估的新方法，涵盖聚合过程的集成建模和聚合过程的反向工程。逆向工程方面对该领域来说尤其新鲜。该方法旨在设计新的可持续生产工艺，开发具有更好甚至新性能的聚合物，该项目的基线是使用动力学蒙特卡罗(KMC)方法对聚合过程进行建模。将使用一个开源的KMC模拟器来生成用于训练和测试ML方法的数据集。ML-PRE的总体目标分为以下科学目标：(1)为聚合建模创建一套连贯且经过验证的可扩展ML模型，促进快速高效的ML模型的模拟学习，扩展KMC模拟器以解决新类型的问题(例如，高温下的丙烯酸酯聚合，扩散控制终止)。(2)创建具有建模和优化能力的基于ML的聚合过程逆向工程方法。(3)建立基于ML的半间歇聚合过程控制器学习模型。(4)创建一种通用的、可移植的方法，通过适当和有效的可解释性技术来增加项目中创建的ML的透明度。从ML的角度来看，主要的创新是由第一个和第四个目标提供的：目标1是关于创建一套连贯的、经过验证的ML模型，其接口旨在支持灵活性，以支持往返PRE中的复杂双向工作流。第二个主要创新是，通过上面的目标4，我们旨在实现并保持为往返PRE创建和验证的ML方法的透明度和可解释性。参考SPP调用的协作矩阵，我们主要解决目标领域#1(最优决策)；通过ML支持的模拟工作，我们还涵盖目标领域#2(在机器学习模型中引入/实施物理定律)的一些方面。W.r.t.我们期望并致力于第1列(现象/微观尺度)机械模型结果的协作矩阵将可转移到机械模型、实验(实际过程)和第3列的优化(流程图/过程)领域，反之亦然。

Polymers are important materials in everybody’s daily life and in numerous technical applications. They have a wide range of properties that can be tailored by the type and the conditions of the production process, ideally by modeling of the polymerization process. The project ML-PRE aims to bridge the gap between state-of-the-art machine learning (ML) methods and their application in modeling and optimization in polymer reaction engineering (PRE). This will enable a novel approach referred to as roundtrip PRE, covering integrated polymerization process modeling and reverse engineering of the polymerization process. The reverse engineering aspect is particularly novel to the field. The overall approach aims at designing new sustainable production processes and developing polymers with better or even new properties.The baseline of the project is the modeling of polymerization processes using kinetic Monte Carlo (KMC) methods. An open-source KMC simulator will be used to generate data sets for training and testing the ML methods. The overall goal is broken down into the following scientific objectives of ML-PRE: (1) To create a coherent and validated suite of scalable ML-based models for polymerization modeling, facilitating fast and efficient simulation-supported learning of ML models, extending the KMC simulator for novel types of problems (e.g. acrylate polymerizations at high temperature, diffusion-controlled termination). (2) To create ML-based approaches for reverse engineering of polymerization processes with modeling and optimization capabilities. (3) To create ML-based models for learning controllers of semi-batch polymerization processes. (4) To create a general and transferable methodology for increasing the transparency of ML created in the project by means of suitable and validated explainability techniques. From the ML perspective, the main innovations are provided by the first and the fourth objective: Objective 1 is about creating a coherent suite of validated ML models with interfaces designed to support the flexibility to support the complex bi-directional workflows in roundtrip PRE. The second main innovation is that through Objective 4 above, we aim at a general and transferable methodology to bring about and maintain transparency and explainability of the ML methods created and validated for roundtrip PRE.Referring to the collaboration matrix of the SPP call we mainly address target area  #1 (optimal decision making); through the work on ML-supported simulation we also cover some aspects of target area #2 (introducing/enforcing physical laws in machine learning models). W.r.t. the collaboration matrix we expect and work towards that results for mechanistic models in the 1st column (Phenomena / Micro-scale) will be transferable to the areas  mechanistic models, experiments (real process), and optimization of the 3rd column (Flowsheet / Process) and vice versa.