In many scientific fields, there is an interest in understanding the way in which chemical networks evolve. The chemical networks which researchers focus upon have become increasingly complex, and this has motivated the development of automated methods for exploring chemical reactivity or conformational change in a "black-box" manner, harnessing modern computing resources to automate mechanism discovery. In this work, we present a new approach to automated mechanism generation which couples molecular dynamics and statistical rate theory to automatically find kinetically important reactions and then solve the time evolution of the species in the evolving network. The key to this chemical network mapping through combined dynamics and ME simulation approach is the concept of "kinetic convergence", whereby the search for new reactions is constrained to those species which are kinetically favorable at the conditions of interest. We demonstrate the capability of the new approach for two systems, a well-studied combustion system and a multiple oxygen addition system relevant to atmospheric aerosol formation.
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http://dx.doi.org/10.1021/acs.jctc.1c00335 | DOI Listing |
J Neurosci Methods
January 2025
Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address:
Background: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
Microkinetic modeling of heterogeneous catalysis serves as an efficient tool bridging atom-scale first-principles calculations and macroscale industrial reactor simulations. Fundamental understanding of the microkinetic mechanism relies on a combination of experimental and theoretical studies. This Perspective presents an overview of the latest progress of experimental and microkinetic modeling approaches applied to gas-solid catalytic kinetics.
View Article and Find Full Text PDFFluids Barriers CNS
January 2025
Medical Image Processing Department, CHU Amiens-Picardie University Hospital, Amiens, France.
Background: The pressure gradient between the ventricles and the subarachnoid space (transmantle pressure) is crucial for understanding CSF circulation and the pathogenesis of certain neurodegenerative diseases. This pressure can be approximated by the pressure difference across the aqueduct (ΔP). Currently, no dedicated platform exists for quantifying ΔP, and no research has been conducted on the impact of breathing on ΔP.
View Article and Find Full Text PDFSci Adv
January 2025
Laboratory of Neurobiology of Emotions, Nencki-EMBL Partnership for Neural Plasticity and Brain Disorders-BRAINCITY, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland.
Being part of a social structure offers chances for social learning vital for survival and reproduction. Nevertheless, studying the neural mechanisms of social learning under laboratory conditions remains challenging. To investigate the impact of socially transmitted information about rewards on individual behavior, we used Eco-HAB, an automated system monitoring the voluntary behavior of group-housed mice under seminaturalistic conditions.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.
Emerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors are essential. However, due to a lack of general design principles, their resilience against extra-terrestrial high-energy radiation can currently not be predicted. In this work, the discovery of radiation hard materials is accelerated by combining the strengths of high-throughput, lab automation and machine learning.
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