From Materials Database to Materials Bank: Assetizing Data for AI Driven Materials Innovation

2026-06-30

AI Summary

This work addresses the limitations of conventional materials databases, which lack systematic mechanisms for data valuation and assetization, thereby hindering efficient industrial translation. The study proposes a novel “Materials Bank” paradigm that extends traditional databases with a value-filtering and assetization layer. Through a multidimensional BankCard framework, candidate materials are standardized into upgradable assets characterized by scientific validity, synthetic feasibility, application readiness, and industrial value. Integrating AI-driven screening, automated experimental validation, and multicriteria assessment, this framework establishes a closed-loop innovation ecosystem that bridges raw data to deployable products. The approach demonstrably enhances the identification efficiency of high-potential materials and accelerates their industrial adoption, offering a scalable, AI-enabled decision infrastructure for next-generation materials discovery and development.

Abstract

Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critical gap between massive data accumulation and actionable innovation, hindering the identification of high-potential materials and industrial translation. To address this bottleneck, we propose an industrialization-oriented Materials Bank, a dedicated valuefiltering and assetization layer that operates beyond traditional databases. It does not merely curate high-quality data but systematically elevates qualified candidates into standardized, upgradable materials assets via a multi-dimensional BankCard framework covering scientific validity, synthesis feasibility, application readiness, and industrial value. By unifying databases, AI models, automated experimentation, and multi-criteria assessment into a cohesive closed-loop ecosystem, the Materials Bank establishes a clear trajectory from data to knowledge, candidate, asset, and product. It serves not as an enhanced database or screening tool, but as a decision infrastructure bridging academic discovery and industrial demand, offering a scalable paradigm to accelerate AI-driven materials innovation and deliver tangible real-world impact.

Problem

• materials database
• data assetization
• AI-driven materials innovation
• industrial translation
• value filtering

Innovation

• Materials Bank
• data assetization
• AI-driven materials discovery
• value filtering
• closed-loop innovation ecosystem