Materials science research is hampered by serious data and process bottlenecks. Key datasets are scattered across public databases such as Materials Project, electronic laboratory notebooks, academic papers, and patents. Each source uses a different format, making unified collection and analysis extremely difficult. R&D cycles remain long and expensive, as discovery still relies heavily on trial and error, often taking months or years to validate new materials. General‑purpose large models cannot solve this problem: they lack professional knowledge of chemistry, structure, and synthesis pathways, leading to vague or inaccurate answers. At the same time, research collaboration brings compliance concerns. Sensitive formulas require desensitization, experimental processes are hard to trace, and technical reporting is cumbersome, all of which slow down the transformation of research results.
Our Solution: Volcengine Materials Science Large Model with Full‑Link Data Intelligence
To address these challenges, we are fine‑tuning a vertical Materials Science Large Model that understands the language of chemistry, materials, and processes. By continuously pre‑training on domain‑specific corpora and experimental data, the model delivers precise answers on material structures, performance parameters, and synthesis pathways—going far beyond the capabilities of generalized AI.
In parallel, we are building a whole‑link Data Intelligence Platform to unify and standardize materials information. The platform aggregates data from ICSD, ELN, research publications, and patents into one system, harmonizes naming conventions, normalizes structural formats such as CIF and POSCAR, and performs deduplication, data cleaning, and kinship tracking. Sensitive information can be anonymized automatically, while compliance auditing and traceability functions ensure secure, transparent collaboration.
Application Impact: Professional, Efficient, Compliant
The project transforms materials research by replacing fragmented workflows with a single intelligent platform. Researchers gain rapid, trusted answers grounded in domain knowledge, while organizations accelerate discovery cycles, reducing reliance on costly trial and error. With built‑in compliance safeguards and streamlined reporting, collaborative research becomes both faster and more secure, increasing the speed at which innovations reach practical application.