Transforming matter
Introduction to best practices Machine learning (ML) models are becoming increasingly popular for a wide range of applications in materials science. Within this field, ML models are often classified as predictive and generative. Predictive models can predict certain properties or characteristics of unseen materials from a sufficient amount of material-property training data. Generative models go […]
Data-driven materials science is an emerging field that has shown significant progress in recent years. As highlighted in a previous post, the recent and impressive advancements in technology and computational capabilities and the increase in demand for new better materials promote that the number of material databases and material data entries grow year by year. […]
Introduction to generative AI models in Materials Science Generative AI models are shaking up the world of Materials Science. This type of Artificial Intelligence technology is changing the game for researchers and scientists, and it’s important to understand why. First, let’s take a step back and think about what we mean by generative AI models. […]
Artificial Intelligence (AI) is transforming the field of Materials Science, and predictive AI models are playing a significant role in this transformation. These models can provide valuable insights into the behavior and properties of materials, leading to the discovery and optimization of new and existing materials. In this blog post, we will explore the various […]
Databases play a critical role in today’s materials science and materials engineering. They serve as the backbone of research, enabling scientists and engineers to access, store, and analyze large amounts of data from various sources, including experiments, simulations, and literature. For instance, databases like ICSD or Materials Project provide very diverse information on a vast […]
Material representations play a crucial role in machine learning applications for materials science. The way materials are represented as input data to artificial intelligence (AI) methods has a significant impact on the accuracy and efficiency of the machine learning algorithms. The choice of material representation depends on the type of problem being addressed and the […]
