Machine Learning for Experimentation does not have to be hard
Matterhorn streamlines the integration of Machine Learning in your experimentation process
Matterhorn in 3 steps
This 3 minute tutorial demonstrates the three fundamental steps of experimental material discovery:
- Upload data and specify variables
- Build Machine Learning models for estimation
- Calculate the next best experiment with Bayesian Optimisation
Get a feeling for what data-driven material development can feel like. Understand the advantages and limitations of Machine Learning and learn how Bayesian Optimisation can work in your laboratory.
Start TutorialBlog
(Hackathon) CA$2000 in Prizes available for Bayesian Optimisation
Matterhorn Studio is offering up to CA $2000 in prizes for the best BO projects, as part of the BO Hackathon hosted by Merck Group and Acceleration Consortium.
March 14, 2024
(Funding) IUK grant supports development of cutting-edge ML models at Matterhorn Studio
In May, we received a grant from IUK, as part of the Transformative Technologies series, to refine our machine learning models for biosynthetic materials.
June 12, 2023
(Podcast) Welt der Werkstoffe - talk, Folge 24, Jakob Zeitler: Einsatz von KI in der Werkstoffforschung
I had the chance to talk about Machine Learning in Material Science with Professor Bonnet on his show "Welt der Werkstoffe".
Feb. 1, 2023
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01 How much data do I need?
The more the better! Most labs will already have a dataset to build from. Importantly, the data needs to be of high quality, i.e. low noise. Ideally, it is composed of continuous variables and few dimensions, as categorical variables are generally harder to optimise.
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02 Do I need to know any Machine Learning?
Matterhorn is indifferent to how much Machine Learning capabilities your lab currently offers. Feel free to bring your own Data Scientist, but if you are in the early stages of ML-driven material science we are happy to support you with recruiting or providing consultation.
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03What is Bayesian Optimisation for Material Design?
Bayesian Optimisation is an algorithm that guarantees the statistically most efficient path to optimal material performance. It uses the uncertainty in the solution space to decide which experiment to run next.
Measurable impact on your experimentation workflow
See paper Autonomous experimentation systems for materials development
Time up to 10x faster
Cost up to 100x cheaper
Success up to 5x higher success rate
Features
Material discovery requires several stepping stones. Matterhorn takes care of your data management and visualisation needs. User-friendly modeling and optimisation interfaces seamlessly integrate in any laboratory workflow.
Data Management
Manage your data and variables in a single storage for efficient exchange across your team
Optimisation
Schedule your next experiment, efficiently. Choose from a variety of search strategies for optimal success rate.
Get in touch
We will happily guide you through the emerging space of data-driven material discovery. We look forward to learn from your experience and problem space.
