ArticleFaraday discussions2025
Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.
Article in Faraday discussions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Engineering Small-Molecule Proton-Transfer Ferroelectrics by Crystal Structure Prediction: Design Limits at the Salt-Cocrystal Boundary.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Accelerating Prediction of Complex Molecular Crystals by Sensible Selection of Asymmetric Units.Journal of chemical theory and computation · 2026Article
- Fast Molecular Crystal Structure Prediction Using Sampling by Analogy to Previously Predicted Landscapes.Journal of chemical theory and computation · 2026Article
- Can Machine Learning Predict the Space Group Preference of Organic Molecules?Crystal growth & design · 2026Article
- From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes.Chemical science · 2026Article
- Open Molecular Crystals 2025 (OMC25) dataset and models.Scientific data · 2026Article
- Crystal Structure Prediction for Aprotic Ionic Liquids - Searching for the Unknown.Crystal growth & design · 2026Article
- Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation.Nature communications · 2025Article
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
- Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.Crystal growth & design · 2025Article
- A universal foundation model for transfer learning in molecular crystals.Chemical science · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- One Size Fits All? Development of the CPOSS209 Data Set of Experimental and Hypothetical Polymorphs for Testing Computational Modeling Methods.Crystal growth & design · 2025Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure re-optimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.