ArticleChemical science2018
Machine learning for the structure-energy-property landscapes of molecular crystals.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.Molecules (Basel, Switzerland) · 2026Article
- Fast Molecular Crystal Structure Prediction Using Sampling by Analogy to Previously Predicted Landscapes.Journal of chemical theory and computation · 2026Article
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
- An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction.Crystal growth & design · 2025Article
- Accurate and Efficient Phonon Calculations in Molecular Crystals via Minimal Molecular Displacements.Journal of chemical theory and computation · 2025Article
- MYTHOS: A Python Interface for Surface Crystal Structure Prediction of Organic Semiconductors.Journal of chemical information and modeling · 2025Article
- A universal foundation model for transfer learning in molecular crystals.Chemical science · 2025Article
- Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning.Annual review of materials research · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.Faraday discussions · 2025Article
- The seventh blind test of crystal structure prediction: structure ranking methods.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- The seventh blind test of crystal structure prediction: structure generation methods.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- Catching the π-Stacks: Prediction of Aggregate Structures of Porphyrin.The journal of physical chemistry. A · 2024Article
- Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data.The journal of physical chemistry. A · 2024Article
- Effect of [Crystal growth & design · 2023Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- Impact of Static Distortion Waves on Superlubricity.ACS omega · 2023Article
- Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks.ACS omega · 2023Article
- Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol
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Registered trials
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