III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

3. Supramolecular Systems Chemistry

Ueltzhoffer, Kai, et al. A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks. Entropy. 23/9, 2021. In a paper for a Foundations of Biological Computation issue edited by David Wolpert and Jessica Flack, University College London theorists including Karl Friston describe the apparent activity of natural forces which might impel and foster an oriented emergence of substantial complexities. See also Memory and Markov Blankets by the collegial group et al in this journal. However see Non-equilibrium Thermodynamics and the Free Energy Principle by M. Colombo and P. Palacios in Biology and Philosophy (August 2021) for some issues.

thermodynamically favoured whenever they dissipate free energy that could not be accessed otherwise. These accounts apply as well to relatively simple systems such as convection cells, hurricanes, candle flames, or lightning strikes as they do to complex biological systems. Computational properties such as predictive representations of environmental dynamics can then be linked to the thermodynamics of underlying physical processes. However, the selection of dissipative structures with efficient subprocesses is not well understood. Here we explain how bifurcation-based, work-harvesting processes which sustain complex dissipative structures might be driven towards thermodynamic efficiency. We cite a simple mechanism that leads to self-selection in a chemical reaction network and discuss how this can emerge naturally in a hierarchy of self-similar dissipative forms. (Abstract excerpt)

Unsleber, Jan and Markus Reiher. The Exploration of Chemical Reaction Networks. Annual Review of Physical Chemistry. Volume 71, 2020. ETH Zurich computational chemists survey many ways that the AI deep learning revolution promises to speed up and expand our human studies of nature’s prior materiality, along with beginnings of a new intentional creative phase.

Modern computational chemistry has reached a stage at which broad exploration into chemical reaction space with novel resolution of relevant molecular structures has become possible. Algorithmic advances have shown that such screenings can be automated and routinely carried out. It is the purpose of this overview to categorize the problems that should be targeted and to identify the components and challenges of automated exploration machines so that the existing approaches and future developments can be based on well-defined conceptual principles. (Abstract)

Vicens, Jacques and Quentin Vicens. Origins and Emergences of Supramolecular Chemistry. Journal of Inclusion Phenomena and Macrocyclic Chemistry. 65/1-2, 2009. A succinct history over two millennia of the human encounter with nature’s substantial materiality that is lately able to perceive its true essence as a science of the organized complexity of living systems.

Wagner, Nathaniel and Gonen Ashkenasy. Symmetry and Order in Systems Chemistry. Journal of Chemical Physics. 130/164907, 2009. Ben Gurion University chemists find a natural preference for “higher order catalytic systems” which spawn “self-organization processes relevant to the origin of life, evolution and biological organization.”

Systems chemistry encompasses multidisciplinary research directed toward understanding the chemical roots of biological organization using different kinds of synthetic models including DNA, RNA, peptide nucleic acid (PNA), fatty acids, peptides, and organic abiotic molecules. As opposed to the reductionist “top-down” approach of systems biology, systems chemistry uses a “bottom-up” approach that relies on the design and integration of simple elements as a means of providing new fundamental insights into the emergent self-organizing and dynamic properties of complex systems and living matter. Thus the systems chemistry approach has been applied toward understanding such diverse problems as the origin of life and ealrly molecular evolution, origin of symmetry breaking in biological systems, oscillations and pattern formation in chemical reactions, and the design of dynamic combinatorial libraries and networks of replicating molecules. (164907)

Wei, Jennifer, et al. Neural Networks for the Prediction of Organic Chemistry Reactions. arXiv:1608.06296. We cite this work by Harvard University computational chemists for its notice of common network relations in this biochemical phase, and then how they can be described by cerebral topologies and dynamics. The intensive field of brain architecture and function research has come to be an exemplary model for this ubiquitous propensity. A further insight might be a sense that even nature’s material chemistry is in some way as a learning process, for example the Intelligent Evolution section. See also A Network Theoretical Approach to Understanding Interstellar Chemistry, search Jolley, for another contribution.

Reaction prediction remains one of the great challenges for organic chemistry. Solving this problem computationally requires the programming of a vast amount of knowledge and intuition of the rules of organic chemistry and the development of algorithms for their application. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. In this work, we introduce a novel algorithm for predicting the products of organic chemistry reactions using machine learning to first identify the reaction type. In particular, we trained deep convolutional neural networks to predict the outcome of reactions based example reactions, using a new reaction fingerprint model. Due to the flexibility of neural networks, the system can attempt to predict reactions outside the domain where it was trained. (Abstract)

Wozniak, Michal, et al. Linguistic Measures of Chemical Diversity and the “Keywords” of Molecular Collections. Nature Scientific Reports. 8/7598, 2018. In accord with other scientific fields, Polish Academy of Sciences linguists and chemists including Bartosz Grzybowski (search) perceive an innate commonality between 21st century supramolecular chemistry and written, textual compositions. Through a far-ranging affinity, molecules and letters/words can take on similar identities - to wit parsing a sentence becomes akin to analyzing a reaction. The comparison is also availed for better ways to search vast volumes of chemical literature. As our Genomes and Languages reports, as disparate domains find common ground in these later 2010s via literate cross-translations, they may altogether quantify and express a truly poetic natural essence.

In accord with other scientific fields, Polish Academy of Sciences linguists and chemists including Bartosz Grzybowski (search) perceive an innate commonality between 21st century supramolecular chemistry and written, textual compositions. Through a far-ranging affinity, molecules and letters/words can take on similar identities - to wit parsing a sentence becomes akin to analyzing a reaction. The comparison is also availed for better ways to search vast volumes of chemical literature. As our Genomes and Languages reports, as disparate domains find common ground in these later 2010s via literate cross-translations, they may altogether quantify and express a truly poetic natural essence.

Zaikowski, Lori and Friedrich, Jon, eds. Chemical Evolution across Space & Time: From the Big Bang to Prebiotic Chemistry. Washington, DC: American Chemical Society, 2007. By a shift in perspective, rather than an alien cosmos, an inherent, natural propensity for life and limb can be readily discerned. Along with Robert Hazen and Stuart Kauffman, scientists and educators course from astrobiology to geochemistry, biological precursors, and how to infuse students with this nascent sense that “the universe is alive and kicking.”

The history of the universe has been one of inexorable, inevitable chemical complexification – a sequence of emergent evolutionary episodes from nucleosynthesis, to planet formation, to life. (3, Hazen) Emergent systems occur when energy flows through an assemblage of interacting particles, such as molecules, sand grains, cells or stars. Each individual object, or “agent” in the jargon of emergence, responds only to its environment, yet the behavior of the collective whole is distinct from that of any individual agent. (3, Hazen)

Zenil, Hector, et al. Algorithmic Complexity and Reprogrammability of Chemical Structure Networks. arXiv:1802.05856. We cite this posting by Karolinska Institute, Center for Molecular Medicine, Algorithmic Dynamics Lab researchers as another frontier integration of chemistry and computation. Akin to supramolecular systems herein, into the 21st century, this ancient, original study of nature’s reactive materiality is lately recognizing, as are other fields, an inherent, pervasive presence of the universal network geometries.

Zhu, Liang, et al. Multilayer Network Analysis of Nuclear Reactions. Nature Scientific Reports. 6/31882, 2016. Chinese Academy of Sciences and East China Normal University information physicists achieve a novel extension of these lively network qualities found everywhere else from cosmic webs to quantum, genomic, neural, ecosystem and social phases to nature’s atomic materiality. The same physiological and cerebral system of nested, dynamic nodes and links is, incredibly, in similar formative place even in this substantial realm.

Besides acquiring exact nuclide abundance by solving sets of time-dependent differential equations in the database, it is challenging to treat the nuclear reaction system as a complex network to explore its statistical characteristics. The basic idea is introduced from graph theory, which considers the interacting units in a system as nodes and the relationship between two units as an edge, thus the system can be studied as a graph (network). The topic of complex networks has achieved significant advances since the ‘small world’ and ‘scale-free’ characteristics were found prevalent in many real world systems such as social connections, the Internet and distributed infrastructures. The structure and dynamics of networks mapped from those systems turn out to be distinct from that of regular or random networks, and complex networks outperform them in modeling real world systems. For example, the ‘scale-free’ structure of the Internet, which is hierarchical with many hubs, explains how easy it is for viruses to propagate. These findings help us to understand the systems at more profound levels and have benefited researches in various areas19, hopefully including nuclear reactions. (1)

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