Bridging between the phenomenologically distinct biological and physical worlds has been a major scientific challenge since Boltzmann's probabilistic formulation of the second law of thermodynamics. In this review we summarize our recent theoretical attempts to bridge that divide through analysis of the thermodynamic-kinetic interplay in chemical processes and the manner in which that interplay impacts on material stability. Key findings are that the term 'stability' manifests two facets - time and energy - and that stability's time facet, expressed as persistence, is more general than its energy facet. That idea, together with the proposed existence of a logical law of nature, the persistence principle, leads to the mathematically-based insight that stability can come about through either Boltzmann's probabilistic considerations or Malthusian kinetics. Two mathematically-based forms of material persistence then lead directly to the physical likelihood of two material forms, animate and inanimate. Significantly, the incorporation of kinetic considerations into the stability concept appears to bring us closer to enabling two of the central theories in science - the second law of thermodynamics and Darwin's theory of evolution - to be reconciled within a single conceptual framework.
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http://dx.doi.org/10.1039/c5cc06260h | DOI Listing |
Entropy (Basel)
December 2024
Istituto Nazionale di Alta Matematica (INdAM), 00185 Rome, Italy.
The status of the Second Law of Thermodynamics, even in the 21st century, is not as certain as when Arthur Eddington wrote about it a hundred years ago. It is not only about the truth of this law, but rather about its strict and exhaustive formulation. In the previous article, it was shown that two of the three most famous thermodynamic formulations of the Second Law of Thermodynamics are non-exhaustive.
View Article and Find Full Text PDFJ Comput Cogn Eng
November 2024
Department of Computer Science, Utah Valley University, USA.
A restricted Boltzmann machine is a fully connected shallow neural network. It can be used to solve many challenging optimization problems. The Boltzmann machines are usually considered probability models.
View Article and Find Full Text PDFNanoscale Horiz
November 2024
Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
In-sensor computing has gained attention as a solution to overcome the von Neumann computing bottlenecks inherent in conventional sensory systems. This attention is due to the ability of sensor elements to directly extract meaningful information from external signals, thereby simplifying complex data. The advantage of in-sensor computing can be maximized with the sampling principle of a restricted Boltzmann machine (RBM) to extract significant features.
View Article and Find Full Text PDFJ Chem Theory Comput
July 2024
Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, U.K.
Computing free energy differences between metastable states characterized by nonoverlapping Boltzmann distributions is often a computationally intensive endeavor, usually requiring chains of intermediate states to connect them. Targeted free energy perturbation (TFEP) can significantly lower the computational cost of FEP calculations by choosing a set of invertible maps used to directly connect the distributions of interest, achieving the necessary statistically significant overlaps without sampling any intermediate states. Probabilistic generative models (PGMs) based on normalizing flow architectures can make it much easier via machine learning to train invertible maps needed for TFEP.
View Article and Find Full Text PDFJ Chem Phys
June 2024
Department of Mathematics, University of California, Riverside, California 92505, USA.
We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics.
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