SciPost Chemistry

SciPost Chem. 1, 005 (2022) · published 17 June 2022

• doi: 10.21468/SciPostChem.1.2.005
• pdf
• BiBTeX
• RIS
• Submissions/Reports
• 

Abstract

We present here an open-source Julia library for the topological identification of crystalline materials, with algorithmic and computational improvements over the previously available software in the field, resulting in a speed increase of one order of magnitude. This new algorithm and implementation can therefore be used at large scale in high-throughput screening methodologies. We have validated and benchmarked CrystalNets.jl against a diverse set of crystal databases, covering in particular metal–organic frameworks, aluminophosphates, zeolites, and other inorganic compounds.

• Glasby et al., Topological Characterization of Metal–Organic Frameworks: A Perspective
  Chem. Mater., acs.chemmater.4c00762 (2024) [Crossref]
• Mourino et al., In Search of Covalent Organic Framework Photocatalysts: A DFT‐Based Screening Approach
  Adv Funct Materials 33, 2301594 (2023) [Crossref]
• Oktavian et al., Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations
  Nat Commun 15, 3898 (2024) [Crossref]
• Glasby et al., DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining
  Chem. Mater. 35, 4510 (2023) [Crossref]
• Glasby et al., Augmented Reality for Enhanced Visualization of MOF Adsorbents
  J. Chem. Inf. Model. 63, 5950 (2023) [Crossref]
• Terrones et al., Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
  J. Am. Chem. Soc. 146, 20333 (2024) [Crossref]
• Burner et al., ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
  Chem. Mater. 35, 900 (2023) [Crossref]
• Oktavian et al., Computational Characterization of Zr-Oxide MOFs for Adsorption Applications
  ACS Appl. Mater. Interfaces 14, 56938 (2022) [Crossref]
• Cooley et al., Machine learning insights into predicting biogas separation in metal-organic frameworks
  Commun Chem 7, 102 (2024) [Crossref]