{"ID":23560532,"CreatedAt":"2026-09-18T04:32:43.450744143Z","UpdatedAt":"2026-09-18T04:32:43.450744143Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19833","arxiv_id":"2609.19833","title":"The Roadmap of Inorganic Computational Materials Databases: Capabilities, Credibility, Coverage, and the Open Frontier","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.","short_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...","url_abs":"https://arxiv.org/abs/2609.19833","url_pdf":"https://arxiv.org/pdf/2609.19833v1","authors":"[\"Miao Liu\",\"Jianghao Jin\",\"Tenglong Lu\",\"Jianguo Si\",\"Yin Shi\",\"Sheng Meng\",\"Weihua Wang\"]","published":"2026-09-17T07:38:03Z","proceeding":"cond-mat.mtrl-sci","tasks":"[\"cond-mat.mtrl-sci\",\"cs.CE\",\"physics.comp-ph\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}