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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Earth Life Emerge
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VIII. Earth Earns: An Open Participatory Earthropocene to Astropocene CoCreative Future

1. Mind Over Matter and Energy: Quantum, Atomic, Chemical, Astronomic Realms

Ball, Philip. Coming Alive. Nature Materials. March, 2021. The British science writer notes this Royal Society research initiative known as Animate Materials with a general intent to facilitate an historic advance from inorganic design to an accord with nature’s living substantiality.

Defining ‘animate’ is as fraught as defining ‘life’. But the working group behind the Royal Society initiative proposes three general principles underpinning the term. These materials will be active: able to change their properties or perform some action, in the manner of gels or alloys that change shape in response to stimuli. They will be adaptive, responding to changes in the environment in a way that benefits function. Ultimately, developing these
properties in materials systems might demand a recapitulation of life itself: the development of materials systems that show some of the defining properties of living ones, from replication to adaptation. In the end, the objective is a qualitative shift in the art of making: from designing and building to growing and sustaining. (P. Ball)

This Royal Society report on human-made materials that emulate the properties of living systems – outlines the potential of this technology to deliver major change in sectors from infrastructure to medicine and clothing. Animate materials could signal a future in which roads can self-heal, assemble into household objects and living buildings can harvest carbon dioxide to generate power and purified water. The report sets out a roadmap to enable cross-disciplinary collaborations and identify opportunities, as well as ensuring that sustainability and circularity can support a greener future. (search Royal Society Animate Materials)

Bharadwaj, Sachin and Katepalli Sreenivasan. Quantum Computation of Fluid Dynamics. arXiv:2007.09147. In a paper to appear in the Pramana-Journal of Physics of the Indian Academy of Sciences (Springer), NYU mathematicians (KS is the emeritus dean of the NYU Tandon School of Engineering, which originally was the Polytechnic Institute of Brooklyn that I graduated from in 1960) scope out this open frontier as previous classical and quantum realms come together with breakthrough benefit.

Studies of strongly nonlinear dynamical systems such as turbulent flows call for superior computational prowess. With the advent of quantum computing, a plethora of quantum algorithms have demonstrated, both theoretically and experimentally, more powerful computational possibilities than their classical counterparts. Starting with a brief introduction to quantum computing, we will distill a few key tools and algorithms from the huge spectrum of methods available, and evaluate possible approaches of quantum computing in fluid dynamics. (Abstract)

Biamonte, Jacob, et al. Quantum Machine Learning. Nature. 549/195, 2017. In a special Quantum Software segment, JB, now at the Skolkovo Institute of Science and Technology, Moscow, Peter Wittek, Institute of Photonic Sciences, Barcelona, Nicola Pancotti, MPI Quantum Optics, Patrick Rebentrost, Seth Lloyd, MIT and Nathan Wiebe, Microsoft, press the frontiers of how to program this basic realm to properly access a computational prowess far beyond conventional devices. This advance involves a novel synthesis of recurrent neural nets which possess algorithmic, complex dynamic system, information processing affinities with perceived quantum phenomena. (An earlier version of this paper is at arXiv:1611.09347, noted in Quantum Complex Systems.) Some other issue entries are Programming Languages and Compiler Design for Realistic Quantum Hardware by Frederic Chong, et al, and Quantum Computational Supremacy by Aram Harrow and Ashley Montanaro, each Abstract next.

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable. (Biamonte Abstract)

Quantum computing sits at an important inflection point. For years, high-level algorithms for quantum computers have shown considerable promise, and recent advances in quantum device fabrication offer hope of utility. A gap still exists, however, between the hardware size and reliability requirements of quantum computing algorithms and the physical machines foreseen within the next ten years. To bridge this gap, quantum computers require appropriate software to translate and optimize applications (toolflows) and abstraction layers. Given the stringent resource constraints in quantum computing, information passed between layers of software and implementations will differ markedly from in classical computing. Quantum toolflows must expose more physical details between layers, so the challenge is to find abstractions that expose key details while hiding enough complexity. (Chong Abstract)

The field of quantum algorithms aims to find ways to speed up the solution of computational problems by using a quantum computer. A key milestone in this field will be when a universal quantum computer performs a computational task that is beyond the capability of any classical computer, an event known as quantum supremacy. This would be easier to achieve experimentally than full-scale quantum computing, but involves new theoretical challenges. Here we present the leading proposals to achieve quantum supremacy, and discuss how we can reliably compare the power of a classical computer to the power of a quantum computer. (Harrow & Montanaro Abstract)

Bindi, Luca, et al. Producing Highly Complicated Materials: Nature Does It Better. Reports on Progress in Physics. 83/10, 2020. Universita degli Studi di Firenze, Universite de Lorraine, St Petersburg State University, École Polytechnique Federale de Lausanne, and Universita di Pisa materials scientists, quite a panEuropean team, post a 40 page survey of ways that collaborative human agency can take up and continue Nature’s structural creativity going forward. As noted, this new phase can also intentionally apply complex systems principles, along with instrumentation advances. An array of complex compounds, aperiodic crystals, and many more are described in deep textual and visual detail.

Through the years, mineralogical studies have produced a tremendous amount of data on the atomic arrangement and mineral properties. Quite often, structural analysis has elucidated the role played by minor components, giving insights into the physico-chemical conditions of crystallization and the description of unpredictable structures that represented a body of knowledge for assessing their technological potentialities. Using such a rich database, further steps became appropriate and possible into more advanced knowledge. These frontiers assume the name of modularity, complexity, aperiodicity, and matter organization at unconventional levels, and will be discussed in this review. (Abstract)

Bonacci, Walter, et al. Modularity of a Carbon-Fixing Protein Organelle. Proceedings of the National Academy of Sciences. 109/478, 2012. Among myriad such biochemical advances, we cite this paper by systems biologists from Harvard University and the University of California, Berkeley. Of special notice could be not only the employ of biomolecules but also these common, formative, network interrelations between them. By which may respectfully commence, on appearances, a second intentional genesis creation.

Bacterial microcompartments are proteinaceous complexes that catalyze metabolic pathways in a manner reminiscent of organelles. Although microcompartment structure is well understood, much less is known about their assembly and function in vivo. We show here that carboxysomes, CO2-fixing microcompartments encoded by 10 genes, can be heterologously produced in Escherichia coli. In vivo, the complexes were capable of both assembling with carboxysomal proteins and fixing CO2. Characterization of purified synthetic carboxysomes indicated that they were well formed in structure, contained the expected molecular components, and were capable of fixing CO2 in vitro. In doing so, we lay the groundwork for understanding these elaborate protein complexes and for the synthetic biological engineering of self-assembling molecular structures. (478)

Bose, S. K., et al. Evolution of a Designless Nanoparticle Network into Reconfigurable Boolean Logic. Nature Nanotechnology. 10/12, 2015. We cite as another instance among many this entry by University of Twente, Netherlands researchers of future potentials just opening for human avail by way as an intentional continuance of nature’s procreative complex network systems.

Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized.

Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. (Abstract)

Braga, Dario. Crystal Engineering. Chemical Communications. 22/2751, 2003. In this regard a professor at the University of Bologna envisions the “bottom-up construction of functional materials from molecular or ionic building blocks.”

Brown II, Charles. Mimicking Matter with Light. Scientific American. June, 2023. Experiments that imitate materials with light reveal the quantum basis of exotic physical effect. A Yale University physicist explains at wave length how he and his team are undertaking an advanced excursion into these natural realms as they become accessible to our deep Earthuman quantification. We record in Mind/Matter as a latest example of a novel Scientific Ecosmican participant cocreativity.

The Brown Research Group studies single-, few-, and many-body quantum physics by simulation experiments which realize complex quantum systems as a way to understanding their ordered phases and dynamics. Our group traps ultracold atoms in optical lattice potentials, which is the spatially periodic potential the atoms experience in the intensity standing wave of several intersecting lasers. (Excerpt)

Canfield, Paul. New Materials Physics. Reports on Progress in Physics. 83/016001, 2020. The DOE Ames (Iowa) Laboratory senior condensed matter physicist introduces and surveys this open frontier of the historic transfiguration of cosmic substance from its long, contingent phase to a radically intentional, informed, sustainable, biocreative futurity. As a spokesperson for this national and global research community, a new era of material recreation and enhancement beckons whence all manner of formulations can be beneficially made anew.

This review presents a survey of, and guide to, New Materials Physics research. It begins with an overview of the goals of New Materials Physics and then presents important ideas and techniques for the design and growth of new materials. An emphasis is placed on the use of compositional phase diagrams to inform and motivate solution growth of single crystals. The second half of this review focuses on the vital process of generating actionable ideas for the growth and discovery of new materials and ground states. Motivations ranging from (1) wanting a specific compound, to (2) wanting a specific ground state to (3) wanting to explore for known and unknown unknowns, will be discussed and illustrated with abundant examples. The goal of this review is to inform, inspire, an even entertain, as many practitioners of this field as possible. (Abstract)

Humanity needs to find the materials that will ease is growing needs for reliable, renewable, clean, energy and/or will allow for greater insight into the mysteries of collective and, in some cases, emergent states. In this talk I will present a broad overview of New Materials Physics and discuss the three basic motivations for making n advance: wanting a specific compound; wanting a specific ground state; searching for known and unknown unknowns. Materials discussed will span superconductors, quasicrystals, heavy fermions, fragile magnets, topological electronic systems, local moment magnets and more. (PC 2017 APS talk)

Cao, Shan Cecilia, et al. Nature-Inspired Hierarchical Steels. Nature Scientific Reports. 8/5088, 2018. A seven person team from UC Berkeley, City University of Hong Kong, Zhejiang University, Virginia Tech, and MIT describe an imaginative, biomimetic combine of nature’s strongest organic materials with a 21st century steel metallurgy to achieve much improved qualities. The clever project is seen to augur for a new range of extra-superalloys.

Materials can be made strong, but as such they are often brittle and prone to fracture when under stress. Inspired by the exceptionally strong and ductile structure of byssal threads found in certain mussels, we have designed and manufactured a multi-hierarchical steel, based on an inexpensive austenitic stainless steel, which defeats this “conflict” by possessing both superior strength and ductility. These excellent mechanical properties are realized by structurally introducing sandwich structures at both the macro- and nano-scales. Our experiments and micromechanics simulation results reveal a synergy of mechanisms underlying such exceptional properties. This synergy is key to the development of vastly superior mechanical properties, and may provide a unique strategy for the future development of new super strong and tough, lightweight and inexpensive structural materials. (Abstract excerpt)

Carrasquilla, Juan and Roger Melko. Machine Learning Phases of Matter. Nature Physics. 13/5, 2018. Perimeter Institute for Theoretical Physics researchers conceive a meld of physical principles, cerebral architectures and computational acumen as an effective way to venture upon a new era of intentional material procreation.

Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo. (Abstract excerpt)

Ceder, Gerbrand and Kristin Persson. The Stuff of Dreams. Scientific American. December, 2013. MIT and Lawrence Berkeley National Laboratory materials scientists write of awesome quantum and computational capabilities so as to create anew nature’s chemical substances for a better life, world and future. For a technical report, see The Materials Project by Anubhav Jain, et al, including Gerbrand and Persson, in the new online journal APL Materials (1/1, 2013), where it is dubbed a “materials genome approach.”

Engineered materials such as chip-grade silicon and fiber-optic glass underpin the modern world. Yet designing new materials has historically involved a frustrating and inefficient amount of guesswork. Streamlined versions of the equations of quantum mechanics – along with supercomputers that, using those equations, virtually test thousands of materials at a time – are eliminating much of that guesswork. Researchers are now using this method, called high-throughput computational material design, to develop new batteries, solar cells, fuel cells, computer chips, and other technologies. (Summary)

Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.

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