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 ar
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The traditional development of new materials is a slow and costly process, as the search space for materials is immense and experimentation is expensive (time and money), spanning several decades for a single material. With the success of Artificial Intelligence in its application in domains such as imaging, there is a growing interest in adapting these techniques for use in Materials Science. SMART MATERIAL is an Artificial Intelligence platform that facilitates the use of cutting-edge tools to accelerate the discovery of new materials.
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 ar
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
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 cha
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 provid
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, st
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)
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