Material Science and Chemicals R&D
DataStories for Material Science and Chemicals R&D
Innovation has always been at the heart of chemical companies – whether it’s about discovering new specialty materials, new differentiated applications or better, faster and more economical process technologies. Effective innovation requires hard work and systematic effort over extended periods, and yet success is not always guaranteed.
Are you facing increasing pressure to cut development costs?
Are you expected to deliver shorter payback times on your R&D projects?
Need to respond to new market demands faster than your competitors?
Need to move the needle now, not in the future?
Labs are continuously modernized with cutting-edge digital equipment and more data is generated than ever before. This data holds the key to unlocking shorter avenues to discovery and commercialization, and yet deriving actionable insights from data is hard.
Anyone who has ever collected experimental data to push product development and optimize performance will recognize at least one of these challenges:
1. Data is far from being textbook perfect
It contains many factors of unknown significance, missing values, combinations of text and numbers, and resides in various systems and formats. And most importantly, it describes non-linear relationships, which cannot be captured by linear models.
2. Combinatorial explosion
Most projects on new materials and formulations involve planning and performing experiments, and the more the better. However, more data does not always mean more information. You need to deal with the millions of possible parameter combinations while operating under time and cost pressures.
3. A gap between R&D and data scientists
It’s hard to teach data scientists the realities of modern R&D and real-world experimental science. A good researcher specializing in a certain area builds knowledge and expertise over many years. It’s impossible to match that.
DataStories helps you to accelerate innovation through systematic data-driven experimentation and discovery.
We work with scientists and engineers to help them streamline product development and make smarter decisions through a modern approach to experimentation and discovery facilitated by cutting-edge data technologies.
Iterative model-guided experimentation and formulation
Our methodology is based on smart design of experiments logic and our advanced machine learning algorithms. Designing and running intelligent model-guided experiments is the only way to cut through the combinatorial explosion towards faster commercialization cycles, making sure that every experiment provides valuable data to inform next steps.
Advanced analytics software for scientists and engineers
Our software uses machine learning to augment the specialist knowledge of scientists and engineers, allowing them to analyze and interpret experimental data. Build reliable predictive models, find the factors which drive desired properties and performance, and evaluate millions of possible formulations to identify the ones which have the highest potential for success.
What are the benefits of the DataStories approach?
1. Faster and efficient compound design and formulation
Discover which inputs and ingredients truly affect the desired properties or performance of your product. Uncover non-obvious experimental settings or composition designs for even better performance.
Save up to 30% of experimentation costs by running focused experimental designs in areas that would provide maximal information and integrate these insights continuously with previously generated data and models. Improve the effectiveness of your output by sampling much wider spaces of extended material descriptors and environmental conditions.
2. Spot good and bad projects
Save valuable time and resources by quickly identifying dead-ends with little chance to deliver expected results. Focus on innovations with the largest potential.
3. Develop your internal data capability
Data scientists can never completely replace the knowledge of experts when it comes to innovation. However, in a data-driven world, everyone needs to become a little more data literate. Build internal data analytics capability and lead with innovation.