ArticleChemical science2021
Data-efficient machine learning for molecular crystal structure prediction.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Efficient Organic Molecular Crystal Structure Prediction Using the Density Functional Tight-Binding Method.ACS physical chemistry Au · 2026Article
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.Molecules (Basel, Switzerland) · 2026Article
- Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.Chemical science · 2026Article
- Machine-Learning Interatomic Potentials Achieving CCSD(T) Accuracy for Systems with Extended Covalent Networks and van der Waals Interactions.Journal of chemical theory and computation · 2026Article
- Deep learning generative model for conditional crystal structure prediction of sodium amide.npj computational materials · 2026Article
- Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes.Nature communications · 2025Article
- Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science.The journal of physical chemistry letters · 2025Review
- Lattice Energy Partitions in Crystals of Flexible Molecules and the 40% Limit.Journal of the American Chemical Society · 2025Article
- Deciphering the complexities of crystalline state(s) with molecular simulations.Communications chemistry · 2025Review
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations.The journal of physical chemistry. A · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Exploring New Algorithms for Molecular Vibrational Spectroscopy Using Physics-Informed Program Synthesis.Journal of chemical theory and computation · 2025Article
- Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.Faraday discussions · 2025Article
- Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations.Communications physics · 2025Article
- Polymorph sampling with coupling to extended variables: enhanced sampling of polymorph energy landscapes and free energy perturbation of polymorph ensembles.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- Large-Scale Parameter Estimation for Crystal Structure Prediction. Part 1: Dataset, Methodology, and Implementation.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level-for which an efficient implementation is available-with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP.
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