Skip to content
Open access

A century of structures: historical fidelity and computational fitness in the Inorganic Crystal Structure Database (ICSD).

Oct 2026 · Acta Crystallographica. Section B: Structural Science, Crystal Engineering and Materials · 0 citations
Medicine

TL;DR

This work assesses 464 ICSD structures from 1913 to 1929 using two independent tools: Eir v1.4.2 (bond valence sums, global instability index) and MaplePy (Madelung part of lattice energy).

Abstract

The earliest entries in the Inorganic Crystal Structure Database (ICSD) are both primary historical documents and active computational inputs. They record how the Braggs, Goldschmidt, Vegard, and others established the structural foundations of inorganic chemistry; they are also served, without distinction, to automated pipelines, machine-learning training sets, and high-throughput workflows. We assess 464 ICSD structures from 1913 to 1929 using two independent tools: Eir v1.4.2 (bond valence sums, global instability index) and MaplePy (Madelung part of lattice energy). The pre-1925 median global instability index is 0.077 valence unit (v.u.), below that of modern rock salt reference data (mean 0.192 v.u.); the Braggs' 1913 NaCl gives 0.005 v.u., Bragg's 1922 corundum 0.001 v.u. Early crystallographers were doing simple structures and getting them right. The problem is not quality but metadata. A small number of entries carry genuine topological errors (BeO in NaCl type, SnTe in zinc blende) whose corrective information already exists in database Comments fields but is not machine-readable, not reflected in quality classifications, and relatively invisible to downstream users. We propose six automated fitness-for-purpose flags that surface these cases without removing or modifying any historical record.

Read PDF

Similar papers

Open access Sep 2026

The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.

This study detected anomalies in experimental structures that had already passed all prior validation, as well as limitations in the reliability of the MLIP PES calculations, and similarity descriptors were calculated to quantify the differences between the original and optimized structures.

M. Hušák, F. Fňukal, J. Čejka · 0 citations
Preprint Jul 2026

Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning

This work demonstrates how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculatio...

Timo Reents, Marnik Bercx, Giovanni Pizzi · 0 citations

Efficient crystal structure prediction using universal neural network potential with diversity preservation in genetic algorithms

This study integrates a universal NNP with a GA-based CSP method, which integrates a universal NNP designed to expand convex hull volumes while preserving the diversity of crystal structures, and demonstrates the validity of PFP across a wide range of crystal structures and element combinations.

Takuya Shibayama, H. Imamura, Katsuhiko Nishimra et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, HARP (Hamiltonian Read-out for Proper...

Wenhao He, Xu Chen, Noah Song et al. · 0 citations
Preprint Aug 2026

Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond

It is demonstrated that explicitly incorporating the domain knowledge of the interatomic bonds can significantly and systematically improve the prediction of $\beta$-ZnS/NaCl stability and an increasing amount of bond-informed recursion features improves the predictive accuracy.

Rohan D. Kumar, M. Forti, A. Naik et al. · 0 citations
Aug 2026

The Critical Role of Tilting Descriptors in Data‐Driven Property Prediction of Orthorhombic Perovskites

This work introduces a verified and interpretable pathway for high‐throughput screening of orthorhombic perovskites and provides fundamental insight into the descriptor‐property connections governing different perovskite polymorphs.

Q. Fatima, A. A. Haidry, Usaid Ahmad et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.