Collective variables for the study of crystallisation

2021/01/08 by Tarak Karmakar, Michele Invernizzi, Valerio Rizzi +1 · 45 citations
Chemistry · Materials Science · Physics and Astronomy · #Artificial intelligence #Chemistry #Computational chemistry #Computer science #Crystallization #Crystallization and Solubility Studies #Degrees of freedom (physics and chemistry) #Linear discriminant analysis #Machine Learning in Materials Science #Metadynamics #Molecular dynamics #Physics #Sampling (signal processing) #Scale (ratio) #Statistical physics #Theoretical and Computational Physics #Thermodynamics #cond-mat.soft

paper · pdf · doi:10.1080/00268976.2021.1893848

published in Molecular Physics 119(19-20) (Taylor & Francis) · 23 pages, 3 figures

arxiv created 2021/01/08 · openalex created_date 2021/01/18 · openalex publication_date 2021/03/01 · arxiv updated 2021/03/25 · openalex updated_date 2026/08/05

Abstract

The phenomenon of solidification of a substance from its liquid phase is of the greatest practical and theoretical importance, and atomistic simulations can provide precious information towards its understanding and control. Unfortunately, the time scale for crystallisation is much larger than what can be explored in standard simulations. Enhanced sampling methods can overcome this time scale hurdle. Many such methods rely on the definition of appropriate collective variables able to capture the slow degrees of freedom. To this effect, we introduce collective coordinates of general applicability to crystallisation simulations. They are based on the peaks of the three-dimensional structure factor that are combined non-linearly via the Deep Linear Discriminant Analysis machine learning method. We use these collective variables in the context of the on-the-fly probability enhanced sampling method that is a recent evolution of metadynamics. We demonstrate the validity of this approach by studying the crystallisation of a multicomponent system, Sodium Chloride, and a molecular system, Carbon Dioxide.

Citations

• Polymorphic transitions in single crystals: A new molecular dynamics method
• PLUMED 2: New feathers for an old bird

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• Collective Variables for Crystallization Simulations-from Early Developments to Recent Advances. [europepmc]
• Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset. [europepmc]
• Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin. [europepmc]
• Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations. [europepmc]
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications. [europepmc]

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