Computational materials databases map current capabilities and gaps
The Roadmap of Inorganic Computational Materials Databases: Capabilities, Credibility, Coverage, and the Open Frontier
Computational Engineering, Finance, and Science
Summary
Scientists rely on big collections of computer-generated data to discover new inorganic materials. The authors surveyed existing software and databases to see what material properties can be accurately and cheaply computed, and which remain difficult to catalog widely. They found that while many common properties are well covered, several important ones like thermal conductivity and quantum transport lack systematic databases. The authors suggest a step-by-step plan to improve and expand these materials databases over the next decade.
What this means in practice
- •For materials engineers: Build better predictive tools by incorporating expanding computational databases of well-studied properties for material design workflows.
- •For semiconductor device teams: Explore underdeveloped computational property families like electron-phonon interactions to model advanced device behaviors.
A position paper. It proposes an approach and reports no results.
Authors
Miao Liu, Jianghao Jin, Tenglong Lu, Jianguo Si, Yin Shi, Sheng Meng, Weihua Wang
Abstract
Computational materials databases have become central infrastructure for data-driven discovery of inorganic materials, yet their growth remains strikingly uneven across property families. This perspective synthesizes a systematic survey of mainstream density functional theory (DFT) software, the computational cost and credibility of nineteen material-property families, and the coverage of existing computational databases, into a coherent picture of where the field stands and where it should go. We show that the ecosystem of first-principles codes is methodologically mature: for nearly every property of technological interest, at least one production-grade code can compute it.The binding constraint is no longer methodological capability but the economics of trust - which properties can be computed cheaply enough, and accurately enough, to be harvested at database scale. Mapping database coverage onto a Gartner-style readiness cycle reveals a sharp divide: ground-state structure, energetics, elasticity, and topology have reached routine production, while nine property families - including NMR/EPR parameters, core-level spectra, electron-phonon properties, thermal conductivity, and quantum transport - remain without any systematic computational database. We argue that these blank zones define the scientific opportunity of the next decade, and we propose a three-horizon roadmap: consolidating coverage and interoperability in the near term, industrializing mid-cost properties through surrogate-accelerated workflows in the medium term, and conquering the high-cost frontier through machine-learned interatomic potentials, autonomous computing infrastructure, and community governance in the long term.