(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

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

3. Supramolecular Systems Chemistry

Sayama, Hiroki. Swarm Chemistry Evolving. Fellermann, Harold, et al, eds. Artificial Life XII: Proceedings of the Twelfth International Conference. Cambridge: MIT Press, 2010. The SUNY Binghampton bioengineer updates his laboratory project (Google keywords for info) to reconceive active chemical phenomena in terms of complex adaptive systems.

Moreover, to demonstrate that macro-level ecological/evolutionary dynamics of self-organizing swarm patterns can arise out of micro-level processes embedded in particle interactions, we further introduced minimal mechanisms for variation and competition of recipes when they are transmitted between particles. With these additional mechanisms, the Swarm Chemistry world has become capable of producing fully autonomous ecological and evolutionary behaviors of self-organized “super-organisms” made of a number of swarming particles.

Schwerdtfeger, Peter, et al. The Periodic Table and the Physics that Drives It. Nature Reviews Chemistry. 4/7, 2020. Massey University, New Zealand and University of Helsinki (Pekka Pyykko) theorists consider how the formation of chemical elements can necessarily be traced in an analogic way to deep physical forces such as relativistic electronic-structure theory, nuclear-structure theory and the astrophysical origins.

The periodic table can be seen as parallel to the Standard Model in particle physics, in which elementary particles can be ordered according to their intrinsic properties. The underlying theory to describe the interactions between particles comes from quantum field theory and its inherent symmetries. In the periodic table, the elements are placed into a certain period and group based on electronic configurations that originate from principles for the electrons surrounding a positively charged nucleus. In this Perspective, we critically analyse the periodic table of elements and the current status of theoretical predictions and origins for the heaviest elements, which combine both quantum chemistry and physics. (Abstract excerpt)

Segler, Marwin, et al. Planning Chemical Synthesis with Deep Neural Networks and Symbolic AI. Nature. 555/604, 2018. Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität, and International Centre for Quantum and Molecular Structures, Shanghai University researchers seek novel chemical creations by way of applying natural materiality principles by their affinity with cerebral cognition and brain-based artificial intelligence methods.

To plan the design of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis at present is slow and of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion to a policy network guide, and a filter to pre-select the most promising steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for many molecules, thirty times faster than the traditional computer-aided search method. (Abstract edits)

Showalter, Kenneth and Irving Epstein. From Chemical Systems to Systems Chemistry: Patterns in Space and Time. Chaos. 25/9, 2015. For a 25th Anniversary issue focus Perspectives on Nonlinear Science, West Virginia University and Brandeis University scientists review the passage, akin to other domains as biology and genetics, from many separate pieces to their integral dynamics. By this adjustment, novel properties such as collective behavior, networks, and emergence can be perceived. By so doing, a “chimera” of orderly synchronization and unsynchronized disorder is seen to occur. Again, the 2015 recognition of nature’s universality proceeds apace.

Chemical systems display a remarkable range of nonlinear phenomena in time and space. These include temporal oscillations, multistability and chaos as well as stationary (Turing) spatial patterns, and a variety of traveling and standing waves. During the past twenty-five years, experimental and theoretical chemists have learned much about the properties and mechanisms associated with these strange and beautiful patterns, and they have begun to link their understanding with discoveries in biology, physics, engineering, and other fields. There remain many challenges for those who seek to weave together chemical reactions, transport phenomena, and external forces in new and exciting ways. (Abstract excerpts)

Stankiewicz, Johanna and Lars Henning Eckardt. Chemobiogenesis 2005 and Systems Chemistry Workshop. Angewandte Chemie. 45/342, 2006. Extraordinary insights into an innately dynamic materiality which leads to biological precursors are arising in central Europe as evidenced by this conference report. Leading researchers such as Peter Schuster, Eors Szathmary, Antonia Lazcano, Reza Ghadiri, and Steen Rasmussen were in attendance. A “prebiotic robustness” via the spontaneous coevolution of peptides and chemical energetics is seen to cause the emergence of homochirality (molecules of similar handedness) and nucleotides. Self-organizing catalytic networks will spawn non-Brownian self-reproducing vesicles. Such protocells can then be seen as a supersystem phase of a complex nonlinear chemistry. Chembiogenesis 2007 is to be held in Dubrovnik, Croatia in May.

Steinbock, Oliver, et al. The Fertile Physics of Chemical Gardens. Physics Today. March, 2016. As the lead image of this section conveys, a colorful summary of the project described in From Chemical Gardens to Chemobrionics by Laura Barge, et al, noted above. Here Steinbock, a Florida State University chemist, Julyan Cartwright, Spanish National Research Council systems theorist, and Laura Barge, a Jet Propulsion Laboratory astrobiologist, illustrate how intricate structural forms will emerge, via natural self-organizing propensities, from “inorganic” chemicals such as metal salt crystals in a sodium silicate solutions. A primordial analog might be the way life arose from similar hydrothermal vents. Are we altogether lately realizing that cosmic materiality is intrinsically fertile and pregnant, as Christian de Duve would say, with life and people?

The diverse chemical inventory that can lead to the formation of self-organized chemical garden structures clearly suggests universal principles that are more rooted in physics than in chemistry. We must understand the relevant physics not only to control the growth of chemical gardens but also to harness the many possible applications of self-assembling inorganic tubes that can incorporate diverse materials. (46) The self-organization of chemical garden formation is a nonequilibrium situation fueled by steep concentration gradients that in most other scenarios would quickly dissipate. That fundamental characteristic is shared with living systems that have mastered the art of controlling and utilizing such far-from-equilibrium conditions for materials synthesis and other engineering feats. (49)

Stulz, Eugen and Guido Clever. DNA in Supramolecular Chemistry and Nanotechnology. New York: Wiley, 2015. University of Southampton, UK, and Georg-August University, Germany chemists present a technical book-length edition of this novel nucleotide-based systems chemistry.

This book covers the emerging topic of DNA nanotechnology and DNA supramolecular chemistry in its broader sense. By taking DNA out of its biological role, this biomolecule has become a very versatile building block in materials chemistry, supramolecular chemistry and bio-nanotechnology. Many novel structures have been realized in the past decade, which are now being used to create molecular machines, drug delivery systems, diagnosis platforms or potential electronic devices. The book combines many aspects of DNA nanotechnology, including formation of functional structures based on covalent and non-covalent systems, DNA origami, DNA based switches, DNA machines, and alternative structures and templates. This broad coverage is very appealing since it combines both the synthesis of modified DNA as well as designer concepts to successfully plan and make DNA nanostructures.

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)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7  Next