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

3. Supramolecular Systems Chemistry

Raucci, Umberto, et al. Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing, and Cloud Computing. Annual Review of Physical Chemistry. 74/313, 2023. National Accelerator Lab, Menlo Park, CA, and Stanford University scientists lay out tutorial research pathways with the aim of advancing beneficial chemical innovations by means of these novel title abilities. As a result, a new frontier opens as a new (second) substantial (re)creation.

Modern quantum chemistry algorithms are now able to quite enhance the prediction of nove molecular formulations and properties. Despite this progress, performing such calculations is not readily accessible since a domain expertise, programming skills, and powerful hardware is required. In this review, we discuss how to create practical platforms that can compute quantum chemistry properties such as artificial intelligence–driven input methods, and extended reality visualization. (Excerpt)

Reiher, Markus. Special Issue on Quantum Information in Chemistry. International Journal of Quantum Chemistry. 115/19, 2015. An ETH Zurich physical chemist and group leader introduces a cross-fertilization of quantum information processing methods with chemical research which is increasingly adopting a systems, network, and computational character. Some entries are Geometric Phases in Quantum Information, Orbital Entanglement in Quantum Chemistry, and The Radical Pair Mechanism and the Avian Chemical Compass.

Restrepo, Guilleromo. Spaces of mathematical chemistry. Theory in Biosciences. 143/4, 2024. A MPI Mathematics in the Sciences chemical historian and natural philosopher explains how these geometric properties can serve to scope out new research trajectories going forward in a systemic mode.

In an effort to expand the domain of mathematical chemistry and inspire research beyond the realms of graph theory and quantum chemistry, we explore five mathematical chemistry spaces and their interconnectedness. These spaces comprise the chemical space, which encompasses substances and reactions; the space of reaction conditions, spanning the physical and chemical aspects involved in chemical reactions; the space of reaction grammars, which encapsulates the rules for creating and breaking chemical bonds; the space of substance properties, covering all documented measurements regarding substances; and the space of substance representations, composed of the various ontologies for characterising substances.

Ruben, Mario, et al. Hierarchical Self-Assembly of Supramolecular Spintronic Modules into 1D- and 2D- Architectures with Emergence of Magnetic Properties. Chemistry: A European Journal. 11/1, 2005. Deep in the primary scientific literature a new universe is being uncovered whose matter is not a lumpen dross but which organizes itself in a progressive scale of complex, animate systems.

Ruiz-Mirazo, Kepa, et al. Prebiotic Systems Chemistry: New Perspectives for the Origins of Life. Chemical Reviews. 114/1, 2014. Ruiz-Mirazo, University of the Basque Country systems biophysicist and philosopher, Carlos Briones, Spanish National Research Council astrobiologist, and Andreas de la Escosura, Autonomous University of Madrid biochemist survey this historic project to reconstruct how living, evolving systems came to be. With 85 pages, 1,000 references, and a reach beyond biomolecules to chemical, thermodynamic and physical substrates and energies involved, it is a most comprehensive review to date. For starters, as now common, “life” is said to be represented by three main features – replication, metabolism, and compartments. In addition, conducive “prebiotic” conditions of complex biochemicals, autocatalysis, far-from-equilibrium states, morphogenesis, along with self-organizing and assembling dynamics from which life arose need to be included. Such emergent systems composed of “a great diversity of components with multiple interactions between them,” give rise to “global properties and dynamic behaviors.”

A more integral, expansive scenario is thus laid out, which quite implies an independent fertile nature able to do this. We also cite in Organic Universe and Origin of Life as a good example of the incipient cosmic revolution from insensate machine to organic genesis. But not yet, e.g., “metabolic machinery” is still used. While not the authors charge, the issue of what kind of overall reality must there be to inherently spawn living creatures, pregnant with people, does not enter. The contradiction of trying to explain and ground life, evolution and persons in a barren, hostile universe as the rarest accident must be soon resolved. However to add, might the whole 21st century project be equated with a further transition to a planetary progeny?

Although finding consensus about the nature and definition of life is a very difficult issue, and will remain as a subject of debate probably for a long time, there is nowadays relatively widespread agreement on which features should be shared by the simplest living systems. They must possess a genetic apparatus able to store and transmit information to their progeny, some sort of metabolism for gathering nutrients and energy from the environment, and a selectively permeable boundary that separates and distinguishes them from that environment. Hence, in order to explain how the first organisms might have appeared on Earth, or elsewhere, it is necessary to develop chemistries that enable the synthesis of information-bearing polymers, protometabolic networks, and protocellular compartments under compatible prebiotic conditions. Moreover, there is the need for finding thermodynamically and kinetically plausible pathways to integrate the three kinds of subsystems into far-from-equilibrium, autonomous agents with open-ended evolution capacities. (349)

Sadowski, Peter, et al. Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction. Journal of Chemical Information and Modeling. 56/2135, 2016. UC Irvine and ExxonMobil Research computational chemists including Pierre Baldi achieve a novel synthesis of these physical and neural methods, which are reported herein, and in Mind Over Matter. As a result, an efficient, quantum learning approach serves to parse the “grammar” of chemical reactions by these natural language processes. See also in this journal Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships by Junshui Ma, et al (55/263, 2015).

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source–sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source–sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions. (Abstract)

Saitou, Naruya, Naruya. Introduction to Evolutionary Genomics. International: Springer, 2018. A National Institute of Genetics, Mishima, Japan population geneticist provides after 2013 a second edition which further joins our homo sapiens genetic endowment with a deep ancestry in life’s emergent development. Three main sections are Basic Processes of Genome Evolution, Evolving Genomes, and Methods for Evolutionary Genomics.

Topics and Features: Introduces the basics of molecular biology, covering protein structure and diversity, as well as DNA replication, transcription, and translation; Examines the phylogenetic relationships of DNA sequences, and the processes of mutation, neutral evolution, and natural selection; Presents a brief evolutionary history of life, surveying the key features of the genomes of prokaryotes, eukaryotes, viruses and phages, vertebrates, and humans; Reviews the various biological “omic” databases, and discusses the analysis of homologous nucleotide and amino acid sequences; Provides an overview of the experimental sequencing of genomes and transcriptomes, and the construction of phylogenetic trees.

Salles, Airton, et al. A Self-Organizing Chemical Assembly Line. Journal of the American Chemical Society. 135/51, 2013. University of Cambridge complexity chemists in coauthor Jonathan Nitschke’s group describe how principles of biological self-organization can similarly be applied to “inorganic” material dynamic reactivities. View Nitschke’s website to learn about members, projects such as Complex Topologies, and more publications.

Chemical syntheses generally involve a series of discrete transformations whereby a simple set of starting materials are progressively rendered more complex. In contrast, living systems accomplish their syntheses within complex chemical mixtures, wherein the self-organization of biomolecules allows them to form “assembly lines” that transform simple starting materials into more complex products. Here we demonstrate the functioning of an abiological chemical system whose simple parts self-organize into a complex system capable of directing the multistep transformation of the small molecules furan, dioxygen, and nitromethane into a more complex and information-rich product. The novel use of a self-assembling container molecule to catalytically transform a high-energy intermediate is central to the system’s functioning. (Abstract)

Sayama, Hiroki. Swarm Chemistry. Artificial Life. 15/1, 2009. In a special issue on Artificial Chemistry, a SUNY Binghamton bioengineer finds these generic complex systems principles to be similarly suitable across chemical domains.

We propose swarm chemistry, a new artificial chemistry framework that uses artificial swarm populations as chemical reactants. Reaction in swarm chemistry is not determined by predefined reaction rules as commonly assumed in typical artificial chemistry studies, but is spontaneously achieved by the emergence of a new spatiotemporal pattern of collective behavior through the kinetic interaction between multiple chemical species.

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.

Scholes, Gregory. Quantum-like states on complex synchronized networks. arXiv:2405.07950. A Princeton chemist whom by way of his lab group (scholes.princeton.edu) is a pioneer researcher for an beneficial integration of macro/micro, classical and quantum chemical reactivities. This entry is a latest review, search arXiv for more work such as Foundations of Quantum Information for Physical Chemistry at 2311.12238.

Recent work suggests that interesting quantum-like probability laws, including interference effects, can be manifest in classical systems. Here we propose a model for quantum-like (QL) states and bits. We propose a way that complex systems can host robust states to process information in a QL fashion. It is shown that QL states are networks based on k-regular random graphs which can encode information for QL like processing. Although the emergent cases are classical, they have properties analogous to quantum states. The possibility of a QL advantage for computer operations and new kinds of function in the brain are discussed as open questions.

The Scholes Group studies how complex molecular systems in chemistry and biology interact with light. We are interested to learn the mechanisms for photo-initiated processes like solar energy conversion. We are also working out how quantum-mechanical phenomena influence function. We study a broad range of projects which include questions in quantum information science, photobiomodulation medicine, quantum electrodynamics, photosynthesis, solar energy conversion, and photo-activated catalysis in synthetic chemistry.

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)

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