An alternative exchange strategy for parallel tempering simulations is introduced. Instead of attempting to swap configurations between two randomly chosen but adjacent replicas, the acceptance probabilities of all possible swap moves are calculated a priori. One specific swap move is then selected according to its probability and enforced. The efficiency of the method is illustrated first on the case of two Lennard-Jones (LJ) clusters containing 13 and 31 atoms, respectively. The convergence of the caloric curve is seen to be at least twice as fast as in conventional parallel tempering simulations, especially for the difficult case of LJ31. Further evidence for an improved efficiency is reported on the ergodic measure introduced by Mountain and Thirumalai [J. Phys. Chem. 93, 6975 (1989)], calculated here for LJ13 close to the melting point. Finally, tests on two simple spin systems indicate that the method should be particularly useful when a limited number of replicas are available.
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http://dx.doi.org/10.1063/1.2036969 | DOI Listing |
Int J Biol Macromol
November 2024
Division of Computational Chemistry, Lund University, Naturvetarvägen 24, SE-223 62 Lund, Sweden; LINXS - Institute of advanced Neutron and X-ray Science, Lund University, Scheelevägen 19, 223 70 SE-Lund, Sweden. Electronic address:
Coacervates of oppositely charged milk proteins are used in functional food development, mainly to encapsulate bioactives. To uncover the driving forces behind coacervates formation, we study the association of lactoferrin and β-lactoglobulin at amino-acid level detail, using molecular simulations. Our findings show that inter-protein electrostatic interactions dominate and are, surprisingly, equally divided between an isotropic part, due to monopole-monopole attraction of the oppositely charged proteins, and an anisotropic part due to uneven surface charge distributions.
View Article and Find Full Text PDFNat Commun
October 2024
Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA.
Domain-specific hardware to solve computationally hard optimization problems has generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising Machines (IM) on the 3-Regular 3-Exclusive OR Satisfiability (3R3X), as a representative hard optimization problem. We first introduce a multiplexed architecture that emulates all-to-all network functionality while maintaining highly parallelized chromatic Gibbs sampling.
View Article and Find Full Text PDFBiointerphases
September 2024
School of Chemistry and Chemical Engineering, Guangdong Provincial Key Lab for Green Chemical Product Technology, South China University of Technology, Guangzhou 510640, People's Republic of China.
Nanoscale
September 2024
Department of Chemical Engineering and Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
We use machine learning (ML) to classify the structures of mono-metallic Cu and Ag nanoparticles. Our datasets comprise a broad range of structures - both crystalline and amorphous - derived from parallel-tempering molecular dynamics simulations of nanoparticles in the 100-200 atom size range. We construct nanoparticle features using common neighbor analysis (CNA) signatures, and we utilize principal component analysis to reduce the dimensionality of the CNA feature set.
View Article and Find Full Text PDFJ Comput Chem
December 2024
Department of Chemistry, Adamas University, Kolkata, India.
A system associated with several number of weak interactions supports numerous number of stable structures within a narrow range of energy. Often, a deterministic search method fails to locate the global minimum geometry as well as important local minimum isomers for such systems. Therefore, in this work, the stochastic search technique, namely parallel tempering, has been executed on the quantum chemical surface of the system for -8 to generate global minimum as well as several number of local minimum isomers.
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