In the rapidly evolving world of AI within materials and chemicals industries, Bayesian optimization (BO) has re-emerged as a powerful tool. In this blog post, we will explore why BO may not be suitable for all AI purposes in this domain.
This flowcharts helps you decide between AI options for product development.
AI looks for patterns in cause and effect. So it needs detailed, accurate information on the inputs, in our case, a full materials and processing history of the product, and outputs, the properties of the final product that you care about.
As the power of AI becomes ever more visible, there are inevitably worries about its use.
Find out about AI model accuracy and quantified uncertainty and the impact these have on business outcomes.
Find out why FAIR data standards are important in the corporate world as well as academia.
Design spaces define the search area for AI models. This blog explains what they are, and how they can be used in AI powered materials research.
To kick off our blog, we’ll start with the fundamentals. Materials informatics is the application of data science, machine learning, and scientific domain knowledge to gain novel scientific, processing, or business insights. Let’s break down the three components of a successful materials informatics initiative. Smart Data Infrastructure A smart data infrastructure is crucial as AI […]
