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Data science shows that entropy correlates with accelerated zeolite crystallization in Monte Carlo simulations.
J Chem Phys
December 2024
Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA.
We have performed a data science study of Monte Carlo (MC) simulation trajectories to understand factors that can accelerate the formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, MC simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates the crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [Bores et al., Phys.
View Article and Find Full Text PDFMotivation: Microbiome compositional data are often collected from several body sites and exhibit dependency among them. Analyzing microbial compositions from different sites jointly allows for effective borrowing of information by exploiting the underlying cross-site correlation, which can lead to more effective statistical analysis, especially when the sample size at one or both sites is limited. To this end, we introduce a joint model for microbiome compositions at two (or more) sites within the same subjects.
View Article and Find Full Text PDFDuality of Particle-Hole and Particle-Particle Theories for Strongly Correlated Electronic Systems.
J Phys Chem Lett
December 2024
Institute of Physics, Lodz University of Technology, ul. Wolczanska 217/221, 93-005 Lodz, Poland.
We propose a novel approach to electron correlation for multireference systems. It is based on particle-hole (ph) and particle-particle (pp) theories in the second-order, developed in the random phase approximation (RPA) framework for multireference wave functions. We show a formal correspondence (duality), between contributions to the correlation energy in the ph and pp pictures.
View Article and Find Full Text PDFComparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data.
J Chem Phys
November 2024
Faculty of Science, Yamagata University, 1-4-12 Kojirakawa, Yamagata 990-8560, Yamagata, Japan.
Article Synopsis
- The study investigates the short-range and intermediate-range order in GeO2 glass using molecular dynamics and machine-learning interatomic potential alongside reverse Monte Carlo fitting of neutron diffraction data.
- The analysis includes various methods such as structure factors, coordination number, and persistent homology to compare the models' structural differences.
- Findings indicate that while both approaches provide similar two-body correlations, they differ significantly in structural ordering, particularly in ring size distributions, with RMC showing broader distributions compared to the narrower distributions from neural network potential molecular dynamics.
Anisotropy parameters from shapes of ion-ion correlation features of fragmenting molecules.
Sci Rep
November 2024
Department of Physics, University of Gothenburg, Origovägen 6B, Gothenburg, 412 58, Sweden.
When a molecule loses two electrons, Coulomb repulsion makes the resulting doubly charged system likely to fragment into two singly charged ions. These monocations can be detected in a correlated fashion using multiplex time-of-flight spectroscopy. The island shapes in the ion-ion coincidence maps derived from such two-body dissociations contain detailed information on the physical processes underlying the fragmentation.
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