无机计算材料数据库路线图：能力、可信度、覆盖范围与开放前沿

中文摘要 AI 辅助

计算材料数据库已成为无机材料数据驱动发现的核心基础设施，然而其在不同性质家族间的增长仍极不均衡。本展望文章将主流密度泛函理论（DFT）软件的系统性调查、十九个材料性质家族的计算成本与可信度，以及现有计算数据库的覆盖范围综合成一幅连贯的图景，展示该领域当前所处的位置及未来应走向何方。我们表明，第一性原理代码生态系统在方法论上已趋成熟：对于几乎每一项具有技术意义的性质，至少有一种生产级代码能够计算它。因此，制约因素不再是方法论能力，而是信任的经济学——哪些性质能够以足够低的成本和足够高的精度被计算，从而在数据库规模上进行采集。将数据库覆盖范围映射到类似Gartner风格的就绪周期上，揭示出明显的分野：基态结构、能量学、弹性和拓扑已达到常规生产水平，而九个性质家族——包括核磁共振/电子顺磁共振参数、芯能级谱、电子-声子性质、热导率和量子输运——仍缺乏任何系统性的计算数据库。我们认为，这些空白区域定义了未来十年的科学机遇，并提出了一个三阶段路线图：短期内巩固覆盖范围和互操作性，中期通过代理加速工作流实现中等成本性质的工业化，长期则通过机器学习原子间势、自主计算基础设施和社区治理攻克高成本前沿。

英文摘要

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.

发表机构

• Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）
• Dongguan Institute of Materials Science and Technology（东莞材料科学高等研究院）

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