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Growth Summit 2026: How Polymerize Is Enabling Practical AI Adoption in Materials R&D

On February 5th, Polymerize held Growth Summit 2026 at the Korea Science and Technology Center in Seoul, bringing together 200 leaders from materials and chemical companies, research institutions, and academia to discuss how artificial intelligence can be practically applied to materials R&D.

The summit centered on a shared challenge across the industry: How can AI move beyond experimentation and become a reliable driver of R&D efficiency and innovation?

Through keynote sessions, product demonstrations, and in-depth customer case studies, the event illustrated how materials R&D is shifting from experience-driven trial and error toward data-driven systems that continuously learn and improve.

The End of Trial & Error: Introducing the System of Intelligence

In the opening keynote, Polymerize Co-founder and CEO Kunal Sandeep challenged one of the most deeply rooted assumptions in materials R&D, that progress must rely on repeated trial and error.

Despite the exponential growth of computational power, Kunal noted that bringing new materials from discovery to commercialization can still take decades. The root cause is not a lack of tools, but the way decisions are made: researchers often rely on intuition and manual iteration to determine the next experiment, making R&D inherently slow and probabilistic.

Kunal argued that this approach is no longer sustainable as The era of trial and error is coming to an end.

To move beyond it, he introduced the concept of a System of Intelligence (SOI): a system that does more than store experimental data. An SOI continuously learns from both successful and failed experiments, uses that knowledge to predict outcomes, and recommends the most promising next steps. The objective is not to eliminate experimentation, but to reduce unnecessary trials and transform R&D from a probability-driven process into a more deterministic one.

Your Journey to SOI: From Data to Decisions

Building on this vision, Polymerize Co-founder and CTO Dr. Abhijit Salvekar followed with a session focusing on how organizations can realistically move toward a System of Intelligence.

Dr. Abhijit emphasized that SOI is not a single tool or model, but a journey that starts with structured data, evolves through learning loops, and ultimately supports decision-making at scale. In materials and chemical R&D, where experiments are expensive and datasets are often small, generic AI models fall short.

Polymerize addresses this challenge through domain-specific AI models designed for small-data environments, combined with continuous learning workflows that treat both success and failure as valuable assets. Over time, these learning loops allow R&D teams to accumulate institutional knowledge and improve decision quality without increasing experimental burden.

As Dr. Abhijit explained, AI should not function as a black box. Instead, it should act as an extension of the researcher’s thinking, reducing repetitive work and exploration costs while enabling scientists to focus on high-impact experimental design and judgment.

Product Updates and Live Demonstrations

Following the keynote sessions, Polymerize’s Product Manager Claris Chin, AI Engineer Aleksandr Potashnikov, and Solutions Implementation Manager Jiwon Choi presented the latest product roadmap and conducted live demonstrations of the Polymerize platform.

The demonstrations highlighted several core capabilities:

Automatic structuring of Technical Data Sheets (TDS)

Property prediction based on molecular structures (SMILES)

Natural language–driven experiment execution (Pixa)

These capabilities allow AI to support R&D decisions before experiments are conducted, screening candidate material formulations, narrowing research directions, and reducing the time researchers spend on manual data preparation.

A key highlight was Pixa, Polymerize’s AI Agent. By accepting natural language input, Pixa demonstrated end-to-end R&D support, from data processing and result generation to recommending next experiments. This illustrated how AI can be embedded directly into daily research workflows as a practical research assistant.

Customer Case Studies: How SOI Is Applied in Practice

In the afternoon session, customer speakers shared detailed experiences of how Polymerize has been applied within their R&D organizations to enable AI-driven decision-making.

Representatives from Samyang, Gulf Marine, and DL Chemical, including Jongung Byeon, Chan Chao Xiang, and HyeokSoon Jang, described how R&D digitalization laid the foundation for AI adoption. Their presentations highlighted concrete use cases such as material formulation optimization, process condition refinement, and systematic reuse of experimental data across teams.

Rather than isolated pilot projects, Polymerize was positioned as part of the core R&D infrastructure, supporting consistent decision-making and accelerating learning across multiple projects.

Further examples reinforced this impact. Yusuke Kato from Maxell shared how Polymerize was used to drive innovation in adhesive applications, enabling faster exploration of formulation space. Dr. Ouyang from Three Trees reported that after adopting Polymerize, the company achieved an overall 10–20% reduction in R&D time and cost, demonstrating measurable business impact beyond technical improvements.

Academic Validation Beyond Generic AI

The summit concluded with academic perspectives from Professor Sangyong Nam of Gyeongsang National University and Professor JinHong Mok of Dongguk University, who shared how Polymerize has been applied in academic research environments.

Notably, Professor Nam presented a direct comparison between predictions generated by ChatGPT and Polymerize for the same experimental challenge. While generic AI models produced plausible responses, Polymerize delivered predictions that were experimentally actionable, helping researchers identify experiments that directly addressed real R&D bottlenecks.

This comparison underscored a key message of the summit: domain-specific systems of intelligence are essential for turning AI into a practical research tool.

From Vision to Execution

Across keynotes, product demonstrations, and customer case studies, Growth Summit 2026 demonstrated that Polymerize’s System of Intelligence is already being applied in real materials R&D environments. From material development and process optimization to long-term knowledge accumulation, Polymerize is enabling organizations to move beyond trial and error toward faster, more informed, and more scalable R&D decision-making.