SuntheticsML is a machine-learning platform that utilizes a Modified Bayesian Optimization approach to analyze small datasets, enabling the rapid development of chemicals and materials. By requiring as few as five data points, it accelerates research and development processes by up to 32 times, significantly reducing the number of experiments needed for optimization.
Funding
$4.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
HVNSSVFounders
Product
Problem
Traditional research and development in the chemical industry is slow, expensive, and resource-intensive, often requiring extensive experimentation to optimize chemical processes, formulations, and materials. Existing methods, such as Design of Experiments (DOE), can be insufficient when dealing with complex systems or limited data, leading to high R&D costs and missed opportunities for innovation.
Solution
SuntheticsML offers a machine-learning platform that accelerates research and development by analyzing small datasets to optimize chemical processes and material development. The platform uses a modified Bayesian Optimization approach, requiring as few as five data points to build predictive models and guide experimentation. By suggesting optimal experiments, SuntheticsML reduces the number of experiments needed, decreases R&D costs, and accelerates the time to market. The cloud-based platform is user-friendly, reaction-agnostic, and does not require coding knowledge, making it accessible to scientists across various disciplines.
Target Audience
The primary users are scientists, researchers, and R&D teams in pharmaceuticals, chemicals, materials science, energy, food, and cosmetics industries seeking to optimize chemical processes, formulations, and material development.
Features
- Modified Bayesian Optimization approach tailored for small datasets
- User-friendly web application accessible without coding or ML expertise
- Ability to start analysis with as few as 5 data points
- Automated selection of optimal machine learning models for each dataset
- Data visualization tools for identifying trends and parameter effects
- Guided experimental campaigns with suggestions for optimal performance
- Seamless integration with existing R&D workflows
- Cloud-based platform for easy access and collaboration