MaterialsZone launches integrated, AI-guided materials discovery platform

Materials informatics company MaterialsZone (Tel Aviv, Israel) has launched its AI-Guided Product Development feature, providing MaterialsZone users with direct access to AI-generated experiment suggestions to streamline development cycles within their existing workflow.

Founded in 2018, MaterialsZone was developed with extensive expertise in material science, data science and software development to offer a versatile, industry-agnostic solution. A cloud-based materials discovery platform, MaterialsZone aims to empower researchers and organizations by leveraging AI to enhance materials data use and value creation. 

The company works with customers across a wide variety of industries, ranging from chemicals and advanced materials to fast-moving consumer goods. Moreover, its platform is reported to be very flexible in terms of the types of data and data structure it supports, including supporting fiber-reinforced composite materials data.

With the launch of its AI-Guided Product Development feature, MaterialsZone expects to empower researchers with greater autonomy in their experimentation processes and enhance their ability to align development efforts with R&D timelines.

Building on successful use cases, the feature transforms trial-and-error-based experimentation by providing real-time experiment recommendations to guide researchers through iterative improvements. According to the company, an AI-driven feedback loop gradually narrows the parameter space, accelerating progress toward achieving product requirements and researcher goals while considering critical material and process constraints, including cost optimization and carbon footprint reduction.

As each suggested experiment is completed and documented within the MaterialsZone platform, the AI model is used to refine recommendations according to the latest data, enhancing precision and efficiency. Available to researchers and technicians, this cycle integrates data enrichment, machine learning, experiment synthesis and feedback, optimizing development and reducing experimental cycles — all within a no-code framework.

“By putting the power directly in the hands of our end users, we enable them to achieve their goals faster, more effectively and with greater accuracy,” says Ori Yudilevich, CPO of MaterialsZone.