DropForm: AI-guided formulation of bio-based and recycled materials that perform and process like the fossil-matetials they replace

What it is DropForm is Natural Negative's AI platform for designing bio-based and recycled material formulations. It predicts how a formulation will perform and process before anything is made, so partners reach a working material with far fewer lab and factory trials. We specialise in PHA (polyhydroxyalkanoates). The approach applies to moulded parts, films, coatings and fibres, and we are extending it to post-consumer recycled (PCR) plastics.

Why it matters Brands are under pressure to cut fossil plastics, add recycled content and reduce end-of-life impact, but alternative materials are hard to adopt. Fossil plastics have around 70 years of industrial know-how behind them, while bio-based and recycled grades are starting almost from scratch. They often fall short on performance, behave unpredictably in existing equipment, or vary from batch to batch. Development still relies on slow trial and error, typically 18 to 36 months per formulation, consuming material, machine time and budget. Many promising materials never leave the lab.

How it works

1. Define the target: you share the application, required properties, processing route and constraints such as cost or end-of-life requirements.
2. Screen in silico: DropForm combines machine learning trained on real formulation and production data with established polymer science to predict properties and processing behaviour across candidate formulations, then ranks them against your targets.
3. Validate: a shortlist of the most promising candidates is made and tested in the lab, then trialled on your equipment or with your supply chain.
4. Learn: results feed back into the platform, so predictions improve with each round.

Proof to date

• Validated machine learning models achieving greater than 85% property prediction accuracy.
• Formulation trials delivering tensile and impact performance within 10% of petroleum-plastic benchmarks for consumer goods applications.
• Supplier relationships with European PHA producers.
• Funded by Innovate UK, including a feasibility study on drop-in PHA composites for consumer goods and a current project on AI-driven manufacturing intelligence for bio-based materials.
• Currently delivering a funded proof of concept designing bio-based barrier coatings for paper packaging with an industrial partner.
• Active customer projects developing drop-in replacements for fossil plastic components in consumer products.
• A team spanning polymer science, product engineering and production machine learning.

Who it is for

• Brands and product teams replacing fossil plastics or increasing recycled content.
• Material producers and compounders who want new grades to reach market faster.
• Converters, moulders and coaters adopting new materials on existing lines.
• R&D teams with a performance need no off-the-shelf grade can meet.

How we work We typically start with a short, scoped feasibility study: we agree targets and test methods with your team, screen candidates in DropForm, and deliver a validated shortlist with a clear go or no-go recommendation. From there we can move into joint development, sponsored research or a collaborative grant application. To get started, send us your target application, key property and processing requirements, and the material you use today.