For sedentary organisms with localized reproduction, spatially clustered growth drives the invasive advance of a favorable mutation. We model competition between two alleles where recurrent mutation introduces a genotype with a rate of local propagation exceeding the resident's rate. We capture ecologically important properties of the rare invader's stochastic dynamics by assuming discrete individuals and local neighborhood interactions. To understand how individual-level processes may govern population patterns, we invoke the physical theory for nucleation of spatial systems. Nucleation theory discriminates between single-cluster and multi-cluster dynamics. A sufficiently low mutation rate, or a sufficiently small environment, generates single-cluster dynamics, an inherently stochastic process; a favorable mutation advances only if the invader cluster reaches a critical radius. For this mode of invasion, we identify the probability distribution of waiting times until the favored allele advances to competitive dominance, and we ask how the critical cluster size varies as propagation or mortality rates vary. Increasing the mutation rate or system size generates multi-cluster invasion, where spatial averaging produces nearly deterministic global dynamics. For this process, an analytical approximation from nucleation theory, called Avrami's Law, describes the time-dependent behavior of the genotype densities with remarkable accuracy.
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http://dx.doi.org/10.1016/j.tpb.2006.06.006 | DOI Listing |
Nat Commun
January 2025
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
The kinetics of dislocation reactions, such as dislocation multiplication, controls the plastic deformation in crystals beyond their elastic limit, therefore critical mechanisms in a number of applications in materials science. We present a series of large-scale molecular dynamics simulations that shows that one such type of reactions, the nucleation of dislocation at free surfaces, exhibit unconventional kinetics, including unexpectedly large nucleation rates under compression, very strong entropic stabilization under tension, as well as strong non-Arrhenius behavior. These unusual kinetics are quantitatively rationalized using a variational transition state theory approach coupled with an efficient numerical scheme for the estimation of vibrational entropy changes.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Department of Earth Sciences, Utrecht University, Princetonlaan 8A, 3584 CB Utrecht, The Netherlands.
The significance of iron sulphide (FeS) formation extends to "origin of life" theories, industrial applications, and unwanted scale formation. However, the initial stages of FeS nucleation, particularly the impact of solution composition, remain unclear. Often, the iron and sulphide components' stoichiometry in solution differs from that in formed particles.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA.
Gypsum (CaSO·2HO) plays a critical role in numerous natural and industrial processes. Nevertheless, the underlying mechanisms governing the formation of gypsum crystals on surfaces with diverse chemical properties remain poorly understood due to a lack of sufficient temporal-spatial resolution. Herein, we use in situ microscopy to investigate the real-time gypsum nucleation on self-assembled monolayers (SAMs) terminated with -CH, -hybrid (a combination of NH and COOH), -COOH, -SO, -NH, and -OH functional groups.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
Department of Physics and Astronomy and Thomas Young Centre, University College London, London WC1E 6BT, United Kingdom.
Atomic-scale understanding of important geochemical processes including sorption, dissolution, nucleation, and crystal growth is difficult to obtain from experimental measurements alone and would benefit from strong continuous progress in molecular simulation. To this end, we present a reactive neural network potential-based molecular dynamics approach to simulate the interaction of aqueous ions on mineral surfaces in contact with liquid water, taking Fe(II) on hematite(001) as a model system. We show that a single neural network potential predicts rate constants for water exchange for aqueous Fe(II) and for the exergonic chemisorption of aqueous Fe(II) on hematite(001) in good agreement with experimental observations.
View Article and Find Full Text PDFNano Lett
January 2025
Department of Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR).
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