通过预测溶质-溶剂相互作用改进结晶的机器学习方法

Crystals Pub Date : 2024-05-24 DOI:10.3390/cryst14060501
Aatish Kandaswamy, S. Schwaminger
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引用次数: 0

摘要

结晶在确定制药、食品饮料和化学制造等各行业产品的质量和功能方面发挥着至关重要的作用。结晶过程的效率和结果受到溶质与溶剂相互作用的显著影响,这种相互作用决定了结晶产品的纯度、大小和形态。这些属性反过来又会影响产品的功效、安全性和消费者接受度。优化结晶条件的传统方法往往是经验性的,耗时长,而且对复杂化学体系的适应性较差。本研究利用机器学习技术预测和优化溶质与溶剂之间的相互作用,从而提高结晶效果,从而应对这些挑战。本综述通过整合监督、非监督和强化学习模型,为理解和控制结晶过程提供了一种新方法。机器学习不仅能提高产品质量和生产效率,还能最大限度地减少浪费和能源消耗,从而促进更可持续的工业实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions
Crystallization plays a crucial role in defining the quality and functionality of products across various industries, including pharmaceutical, food and beverage, and chemical manufacturing. The process’s efficiency and outcome are significantly influenced by solute–solvent interactions, which determine the crystalline product’s purity, size, and morphology. These attributes, in turn, impact the product’s efficacy, safety, and consumer acceptance. Traditional methods of optimizing crystallization conditions are often empirical, time-consuming, and less adaptable to complex chemical systems. This research addresses these challenges by leveraging machine learning techniques to predict and optimize solute–solvent interactions, thereby enhancing crystallization outcomes. This review provides a novel approach to understanding and controlling crystallization processes by integrating supervised, unsupervised, and reinforcement learning models. Machine learning not only improves product the quality and manufacturing efficiency but also contributes to more sustainable industrial practices by minimizing waste and energy consumption.
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