4.7 Article

Investigating the solid-liquid phase transition of water nanofilms using the generalized replica exchange method

Journal

JOURNAL OF CHEMICAL PHYSICS
Volume 141, Issue 18, Pages -

Publisher

AMER INST PHYSICS
DOI: 10.1063/1.4896513

Keywords

-

Funding

  1. National Science Foundation [CHE-1114676, CHE-1362524]
  2. Direct For Mathematical & Physical Scien
  3. Division Of Chemistry [1114676, 1362524] Funding Source: National Science Foundation

Ask authors/readers for more resources

The generalized Replica Exchange Method (gREM) was applied to study a solid-liquid phase transition in a nanoconfined bilayer water system using the monatomic water (mW) model. Exploiting optimally designed non-Boltzmann sampling weights with replica exchanges, gREM enables an effective sampling of configurations that are metastable or unstable in the canonical ensemble via successive unimodal energy distributions across phase transition regions, often characterized by S-loop or backbending in the statistical temperature. Extensive gREM simulations combined with Statistical Temperature Weighted Histogram Analysis Method (ST-WHAM) for nanoconfined mW water at various densities provide a comprehensive characterization of diverse thermodynamic and structural properties intrinsic to phase transitions. Graph representation of minimized structures of bilayer water systems determined by the basin-hopping global optimization revealed heterogeneous ice structures composed of pentagons, hexagons, and heptagons, consistent with an increasingly ordered solid phase with decreasing density. Apparent crossover from a first-order solid-liquid transition to a continuous one in nanoconfined mW water with increasing density of the system was observed in terms of a diminishing S-loop in the statistical temperature, smooth variation of internal energies and heat capacities, and a characteristic variation of lateral radial distribution functions, and transverse density profiles across transition regions. (C) 2014 AIP Publishing LLC.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Chemistry, Physical

Preorganized Internal Electric Field Powers Catalysis in the Active Site of Uracil-DNA Glycosylase

Wenwen Diao, Shengheng Yan, James D. Farrell, Binju Wang, Fangfu Ye, Zhanfeng Wang

Summary: In this study, a revised catalytic mechanism of Uracil-DNA glycosylase (UDG) was proposed, and the nature of its strong catalytic efficiency was elucidated using quantum-mechanical/molecular-mechanical calculations, molecular dynamics simulations, and QM calculations. The study revealed the important role of the internal electric field in different stages of the reaction, and the balance between the catalytic effect of substrate phosphate groups and the counterbalancing effect of sodium ions in the internal electric field. Additionally, the specific roles of Asp145 and His148, as well as their contributions driven by the internal electric field, were identified.

ACS CATALYSIS (2022)

Article Chemistry, Physical

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Guowei Qi, Michail D. Vrettas, Carmen Biancaniello, Maximo Sanz-Hernandez, Conor T. Cafolla, John W. R. Morgan, Yifei Wang, Alfonso De Simone, David J. Wales

Summary: Recent advances in biomolecular simulation and global optimization have utilized hybrid restraint potentials to improve the performance and accuracy of various computational methods. These hybrid potentials combine harmonic restraints with molecular mechanics force fields and have shown promising results in molecular dynamics simulations and structure prediction.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

Article Chemistry, Physical

Dynamic Diastereomerism on Chiral Surfaces

Sabine C. Matysik, David J. Wales, Stephen J. Jenkins

Summary: Adsorption of chiral molecules on chiral surfaces leads to diastereomerism, resulting in different adsorption geometries. Through first-principles molecular dynamics simulations, we demonstrate that this diastereomerism is reflected in the desorption motion of chiral molecules from a chiral surface. When desorbing from R-Cu{531}, S-Ala molecules show larger angular momentum and a preference for one rotational direction, while R-Ala molecules do not exhibit such preference. These trends are reversed for desorption from S-Cu{531}. Potential applications include chiral separation techniques and enantiospecific sensors.

JOURNAL OF PHYSICAL CHEMISTRY C (2023)

Article Chemistry, Physical

The role of surface topography in the self-assembly of polymeric surfactants

Meng Liu, James D. Farrell, Xianren Zhang, Jure Dobnikar, Stefano Angioletti-Uberti

Summary: We propose a classical density functional theory model to study the self-assembly of polymeric surfactants on curved surfaces. Phase separation driven by size alone is thermodynamically unfavorable on cylindrical and spherical surfaces. By coupling surface topography and polymeric surfactants, non-uniform patterns can be designed on surfaces.

SOFT MATTER (2023)

Article Chemistry, Multidisciplinary

Amplification Free Detection of SARS-CoV-2 Using Multi-Valent Binding

Appan Roychoudhury, Rosalind J. Allen, Tine Curk, James Farrell, Gina McAllister, Kate Templeton, Till T. Bachmann

Summary: The study presents the development of EIS-based biosensors using multivalent binding for sensitive detection of SARS-CoV-2 RNA. The strategy of multivalent binding was found to enhance biosensor performance, with shorter probes demonstrating the best performance in binding with SARS-CoV-2 RNA.

ACS SENSORS (2022)

Article Chemistry, Physical

Molecular Energy Landscapes of Hardware-Efficient Ansa?tze in Quantum Computing

Boy Choy, David J. Wales

Summary: Rapid advances in quantum computing have provided new opportunities for solving the central electronic structure problem in computational chemistry. In the noisy intermediate-scale quantum (NISQ) era, it is important to use quantum algorithms with short quantum circuits to maximize qubit efficiency. The construction of hardware efficient ansa''tze offers a potential solution, but increasing circuit depth to improve accuracy may result in an abundance of local minima that hinder global optimization. To investigate this phenomenon, we explore the energy landscapes of hardware-efficient circuits and propose a dimensionality reduction procedure that simplifies the energy landscape, speeds up optimization, and retains accuracy for the global minimum from both software and hardware perspectives.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2023)

Article Physics, Multidisciplinary

Role of genome topology in the stability of viral capsids

James Daniel Farrell, Jure Dobnikar, Rudolf Podgornik

Summary: The stability of RNA viruses is influenced by genome topology and the interactions between RNA and capsid proteins. Through modeling, the genome topology is encoded as a graph, with adjacent packaging signals mapped to edges. Through simulations and evaluation of osmotic pressure, it is found that virion stability is dependent on both genome topology and degree of confinement. It is predicted that MS2 bacteriophage would prefer a more linear genome topology.

PHYSICAL REVIEW RESEARCH (2023)

Article Chemistry, Physical

Dynamical Signatures of Multifunnel Energy Landscapes

David J. Wales

Summary: This paper investigates the multifunnel energy landscapes in multifunctional systems, such as molecular switches. The multifunnel organization is decoded from dynamical signatures in the first passage time distribution between reactants and products. Extracting the corresponding dynamical signatures provides direct insight into the organization of the molecular energy landscape, enabling a rational design of target functionality.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2022)

Article Physics, Fluids & Plasmas

Stochastic paths controlling speed and dissipation

Rebecca A. Bone, Daniel J. Sharpe, David J. Wales, Jason R. Green

Summary: In this article, the authors test the relationship between speed and dissipation for stochastic paths far from equilibrium. By proposing a minimal model for a driven system, the authors find that faster processes can dissipate less under far-from-equilibrium conditions due to strong currents.

PHYSICAL REVIEW E (2022)

Article Multidisciplinary Sciences

Nested sampling for physical scientists

Greg Ashton, Noam Bernstein, Johannes Buchner, Xi Chen, Gabor Csanyi, Andrew Fowlie, Farhan Feroz, Matthew Griffiths, Will Handley, Michael Habeck, Edward Higson, Michael Hobson, Anthony Lasenby, David Parkinson, Livia B. Partay, Matthew Pitkin, Doris Schneider, Joshua S. Speagle, Leah South, John Veitch, Philipp Wacker, David J. Wales, David Yallup

Summary: This Primer examines Skilling's nested sampling algorithm and its application in Bayesian inference and multidimensional integration. The principles of nested sampling are summarized and recent developments using efficient nested sampling algorithms in high dimensions are surveyed. Detailed examples from cosmology, gravitational-wave astronomy, and materials science are provided. Finally, the Primer includes recommendations for best practices and a discussion of potential limitations and optimizations of nested sampling.

NATURE REVIEWS METHODS PRIMERS (2022)

Correction Multidisciplinary Sciences

Nested sampling for physical scientists (vol 2, 39, 2022)

Greg Ashton, Noam Bernstein, Johannes Buchner, Xi Chen, Gabor Csanyi, Andrew Fowlie, Farhan Feroz, Matthew Griffiths, Will Handley, Michael Habeck, Edward Higson, Michael Hobson, Anthony Lasenby, David Parkinson, Livia B. Partay, Matthew Pitkin, Doris Schneider, Joshua S. Speagle, Leah South, John Veitch, Philipp Wacker, David J. Wales, David Yallup

NATURE REVIEWS METHODS PRIMERS (2022)

Article Chemistry, Multidisciplinary

Design of self-assembling mesoscopic Goldberg polyhedra

Istvan Horvath, David J. Wales, Szilard N. Fejer

Summary: Palladium ions complexed with nonlinear bidentate ligands can form hollow spherical shells. By using model anisotropic mesoscale building blocks, we can reproduce these structures and identify highly cooperative transitions between different polyhedral structures. The curvature of the ligand particles determines the preferred curvature of the building blocks.

NANOSCALE ADVANCES (2022)

Article Computer Science, Artificial Intelligence

On the capacity and superposition of minima in neural network loss function landscapes

Maximilian P. Niroomand, John W. R. Morgan, Conor T. Cafolla, David J. Wales

Summary: Minima of the loss function landscape in a neural network are locally optimal sets of weights that specialize in different aspects of a learning problem and process input information differently. By combining the predictive power from multiple minima using a meta-network, a better classifier can be produced, leading to improved performance for complex learning problems. Analyzing symmetry-equivalent solutions also provides a means to enhance the efficiency of this approach.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2022)

Article Chemistry, Physical

Finite-size scaling and thermodynamics of model supercooled liquids: long-range concentration fluctuations and the role of attractive interactions

Atreyee Banerjee, Mauricio Sevilla, Joseph F. Rudzinski, Robinson Cortes-Huerto

Summary: We computed partial structure factors, Kirkwood-Buff integrals (KBIs), and chemical potentials of model supercooled liquids with and without attractive interactions. Our results show that attractive interactions favor the nucleation of long-range structures and significantly influence the thermodynamic properties. At higher density, the two systems exhibit similar thermodynamic properties.

SOFT MATTER (2022)

Article Computer Science, Artificial Intelligence

Characterising the area under the curve loss function landscape

Maximilian P. Niroomand, Conor T. Cafolla, John W. R. Morgan, David J. Wales

Summary: This paper compares the use of cross-entropy loss and direct optimization of AUC for evaluating neural network classifiers. It analyzes the characteristics of approximate AUC loss functions and provides a theoretical explanation. The research findings show that the approximate AUC loss function can improve testing AUC, but its minima are less stable.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2022)

No Data Available