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

3. Supramolecular Systems Chemistry

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)

Singh, Abhishek, et al. Non-Equilibrium Self-Assembly for Living Matter-Like Properties. Nature Reviews Chemistry. 8/723, 2024. Indian Institute of Education and Research, Kolkata biochemists scope out an ambitious endeavor by way of the latest nonlinear complexity understandings of dynamic living systems. Once this frontier knowledge is properly achieved, in some manner of (Alan)Turing turn, they say that a new phase of its intentional, guided beneficial application can begin. But not really, we add, if life remains as some sort of mechanical process. It is a main aim of this resource site to flesh out and document this nascent Ecopernican genesis revolution

The soft and wet machines of life emerged as the spatially enclosed ensemble of biomolecules with replicating capabilities along with metabolic reaction cycles that operate at far-from-equilibrium. This Review maps the discoveries on this possible integration of reaction networks, self-reproduction and compartmentalization under non-equilibrium conditions. Although challenges lie ahead in terms of molecular diversity, information transfer, adaptation and selection that are required for open-ended evolution, emerging strategies can extend our growing understanding of the chemical emergence of the biosphere. (Excerpt)

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)

Wang, Tuowei, et al. Matryoshka: Optimization of Dynamic Diverse Quantum Chemistry Systems via Elastic Parallelism Transformation. arXiv:2412.13203. Nine Microsoft Research, Beijing and Tsinghua University theorists propose to study nature’s fundamental substances by far-removed neural net computational methods. The title word is the name of Russian dolls so as to imply a whole scale nested self-similarity, all the way down and up. Here is another good example of a December 2024 integral consilient synthesis from physics to people and planet.

AI infrastructures like Graphic Processing Units (GPU), have made performance advances for deep learning. Scientific computing such as for quantum chemistry wherein computational patterns are more diverse, pose a problem to GPU methods. In this paper, we propose Matryoshka, a novel technique for the efficient parsing of quantum chemistry dynamics. Matryoshka is built around three transformation aspects (Permutation, Deconstruction, and Combination). The Block Constructor serves as the central orchestrator, while the Graph Compiler generates code through an automated compilation process. The Workload Allocator then schedules workloads. (Excerpts)

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)

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