Marine Key Materials Intelligent Accelerator "MarineMat AI" Released
June 26, 2026 06:51 China Science Daily
Reporter Zhang Nan reported that on June 26, the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences and the National Science Library of the Chinese Academy of Sciences jointly released "MarineMat AI," an artificial intelligence research assistant for marine key materials.
This intelligent tool, designed for the field of marine key materials, aims to use AI technology to bridge the gap between literature, data, and knowledge, building a "data highway" for core application scenarios of marine key materials. It helps researchers free themselves from tedious literature retrieval and data comparison, allowing them to focus more on innovative research and engineering application breakthroughs.
The first version of the software and database currently released mainly focuses on wear-resistant and corrosion-resistant metal-ceramic composite material systems, which are an important support direction for key components of deep-sea drilling equipment. Around this system, MarineMat AI has developed three core functional modules: Q&A, material search, and performance comparison.
Among them, the Q&A module makes papers "speak." For researchers, the most time-consuming part of reading a paper is often not the abstract and conclusions, but finding the key descriptions related to the research question from dozens of pages of text. The Q&A module of MarineMat AI is designed for this. As long as the original paper contains descriptions related to the question, the system can accurately locate the paper and provide targeted answers. Each answer is accompanied by clear sources and citations to ensure traceability and verifiability.
The material search module makes searching flexible and efficient. This module starts from the actual retrieval habits of materials scientists and integrates multiple material query methods. Once the material is locked, core information and data summaries structured are obtained to help users efficiently make an initial relevance judgment; for content that requires in-depth verification, users can easily jump to the relevant expressions in the original text for seamless review. This design significantly improves the efficiency of finding relevant papers while balancing information richness and traceability, making "fast search" and "complete search" no longer contradictory.
The performance comparison module makes data "clear at a glance." A major pain point in materials research is that data units and test conditions vary in different literature, making horizontal comparison difficult. The core capability of this module is to map the same performance parameters from different sources to the same unit, while normalizing and standardizing related data, so that cross-literature data can be effectively compared and analyzed on the same scale, achieving a reverse research approach of "first set performance goals, then trace optimal solutions."
