Key Takeaways:
1. From July 1-3, 2026, the Future Technology Development Conference for the Coatings Industry was held in Ningbo, with AI becoming the absolute theme of the conference.
2. The world's first "corrosion big data + AI deep learning" computing platform debuted, covering 6 climate zones and 1500+ monitoring points.
3. The University of Science and Technology Beijing's "Materivo" autonomous experimentation platform can automatically recommend formulations and optimize processes, with epoxy coating adhesion loss after salt spray of only 3.3%.
From July 1 to 3, 2026, the 2026 Coatings Industry Future Technology Development Conference was held in Ningbo, Zhejiang. This technical summit, hosted by the China National Coatings Industry Association, sent a clear signal: AI is fundamentally changing the underlying logic of coatings R&D. From formula design to corrosion prediction, from laboratory automation to globally networked monitoring, the penetration speed of AI far exceeds industry expectations.
Corrosion Big Data + AI: From 8 Years of Trial and Error to Precise Prediction
Answer directly:Traditional coating R&D relies on the "trial-and-error method" — designing formulations → preparing panels → exposure testing → evaluation → adjusting formulations. One cycle typically takes several years, and the development cycle of a high-performance anti-corrosion coating can reach 8–16 years. The involvement of AI is compressing this process to 1/3 to 1/5 of the original.
Mechanism Details: The most notable achievement at the conference was the three major platforms built by the National Materials Corrosion and Protection Scientific Data Center: the world's first pA-level high-throughput corrosion micro-detector, a global corrosion networked monitoring platform covering 6 climate zones with 1500+ monitoring points, and the first AI computing platform integrating corrosion data with deep learning.
The working principle of this system is: corrosion data accumulated over decades (such as corrosion rates and failure modes of different materials in various environments) is fed into a deep learning model, where the AI automatically extracts the non-linear correlations between corrosion factors and coating failure, thereby predicting the service life of the coating at the formulation design stage—rather than waiting eight years to see the results.
AI-Driven Autonomous Experimentation: Formulations No Longer Rely on the "Master Craftsman"
The "Materivo" autonomous experimentation platform presented by the team of Professor Dawei Zhang at the University of Science and Technology Beijing became the focus of the event. The platform can autonomously recommend coating formulations, automate synthesis and testing, and self-optimize based on test results—forming a complete closed loop. A high-barrier epoxy coating developed by the team using this platform showed only 3.3% loss of adhesion after salt spray testing, and the R&D efficiency was improved by at least one order of magnitude compared to traditional methods.
Academician Xue Qunji of the Chinese Academy of Engineering pointed out in his conference report: "The coatings industry has never been so closely integrated with national strategic needs as it is today." What he referred to is not only the support of anti-corrosion coatings for marine engineering and nuclear power equipment, but also the fact that the AI-driven materials R&D paradigm itself is becoming a core element of national scientific and technological competitiveness.
From Lab to Plant: Three Key Scenarios for AI Implementation
| Scenario | Technical Path | Current Maturity |
|---|---|---|
| Intelligent Formula Design | Machine learning predicts properties of resin-curing agent-filler ternary systems | Entered industrial validation |
| Coating Lifetime Prediction | Deep learning models based on environmental data and accelerated testing | Commercialized in some scenarios |
| Production Quality Control | Machine vision + AI real-time detection of coating defects | Scaled application |
It is worth noting that HG/T 6346-2025 "Digital Transformation Model and Assessment for the Chemical Industry" was implemented in February 2026, and leading coating enterprises have participated in the standard drafting; subsequent digital transformation assessment and certification will be carried out—this means AI applications are shifting from a "bonus item" to a "mandatory task."
Source:2026 Coatings Industry Future Technology Development Conference, China National Coatings Industry Association, National Scientific Data Center for Corrosion and Protection of Materials
Frequently Asked Questions
Q: Can AI completely replace coating R&D engineers?
Not in the short term. AI currently performs well in formulation recommendation and property prediction, but human expert judgment is still required for original discovery of new materials and handling non-standard issues in process scale-up. AI is an assistive tool rather than a replacement.
Can small and medium-sized coating enterprises afford to use AI?
Cloud-based deployment is lowering the barrier. Some AI platforms offer a SaaS model—requirements are uploaded and computed by cloud models, with payment per use. However, core data accumulation remains the biggest shortcoming for SMEs.
Reference sources:2026 Coatings Industry Future Technology Development Conference Report, China National Coatings Industry Association, National Materials Corrosion and Protection Scientific Data Center, HG/T 6346-2025
Release Date: July 5, 2026