Author:

Keywords:

Science & Technology, Physical Sciences, Chemistry, Multidisciplinary, Chemistry, CRYSTAL-STRUCTURE PREDICTION, PROCESS ANALYTICAL TECHNOLOGY, DIRECT NUCLEATION CONTROL, CAMBRIDGE STRUCTURAL DATABASE, POPULATION BALANCE-EQUATIONS, NEAR-INFRARED SPECTROSCOPY, SUPPORT VECTOR REGRESSION, ATR-FTIR SPECTROSCOPY, HIGH-SPEED PREDICTION, NEURAL-NETWORK MODEL, Algorithms, Artificial Intelligence, Automation, Crystallization, Machine Learning, 03 Chemical Sciences, General Chemistry, 34 Chemical sciences, 40 Engineering

Abstract:

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.