Abstract
The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young’s modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.
Highlights
The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training
We present here a general method wherein high-throughput molecular dynamics simulations are coupled with machine learning methods to predict the relationship between glass composition and stiffness
By comparing the performance of select machine learning (ML) algorithms—polynomial regression (PR), LASSO, random forest (RF), and artificial neural network (ANN)—we show that the artificial neural network algorithm offers the highest level of accuracy
Summary
The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training. The stiffness of glass (e.g., its Young’s modulus E) plays a critical role in flexible substrates and roll-to-roll processing of displays, optical fibres, architectural glazing, ultra-stiff composites, hard discs and surgery equipment, or lightweight construction materials[1,2,3,4] Addressing these challenges requires the discovery of new glass compositions featuring tailored mechanical properties[5,6]. The virtually infinite number of possible glass compositions render largely inefficient traditional discovery methods based on trial-and-error Edisonian approaches[10]. To overcome this challenge, the development of predictive models relating the composition of glasses to their engineering properties is required[9]. MD is a brute-force method, that www.nature.com/scientificreports/
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