Artificial Intelligence

Explainability
Artificial Intelligence, Blog

AI Explainability: Decoding the Black Box

Introduction to AI Explainability In the realm of modern technology, Artificial Intelligence (AI) stands out as a paradigm-shifting field that affects various industries, from healthcare and finance to transportation and materials science. Nevertheless, as we further entrust AI with more intricate tasks, the pressing question of AI explainability and its importance arises. AI Explainability, sometimes […]

Materials data
Artificial Intelligence, Blog

Best practices in building training datasets for AI

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 […]

Crystal generation
Artificial Intelligence, Blog

The Power of Generative AI in Materials Science

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. […]

Property prediction
Artificial Intelligence, Blog

The Importance of Predictive AI Models in Materials Science

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 […]

Material representations
Artificial Intelligence

A Journey Through Material Representations

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 […]

Evolution computational materials science
Artificial Intelligence

Evolution of Computational Tools in Materials Science

Introduction: The role of computational tools in materials science Materials science is a field that focuses on the study of the nature and properties of materials and how they can be used in various applications. Over the past two decades, the field has undergone tremendous growth, and this is largely due to the advancements made […]