VIII. Earth Earns: An Open Participatory Earthropocene to Astropocene CoCreative Future

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

Yazdi, S. M. Hossein, et al. DNA-Based Storage. IEEE Transactions on Molecular, Biological and Multi-Scale Communications. 1/3, 2016. It seems, as evinced by new journals as this (search IEEE T-MBMC), that our sense of what constitutes a genome is steadily expanding. In typical entry, a team of University of Illinois theorists, who originally hail from Iran, Singapore, Spain, China, and Yugoslavia, study “chemical oligonucleotide” synthesis as if they were parsing literature with a mind to “edit” and improve. See also in issue 1/2, 2015, Coordinated Spatial Pattern Formation in Biomolecular Communications Networks by Yutaka Hori, et al for more potentials.

We provide an overview of current approaches to DNA-based storage system design and of accompanying synthesis, sequencing and editing methods. We also introduce and analyze a suite of new constrained coding schemes for both archival and random access DNA storage channels. The analytic contribution of our work is the construction and design of sequences over discrete alphabets that avoid pre-specified address patterns, have balanced base content, and exhibit other relevant substring constraints. These schemes adapt the stored signals to the DNA medium and thereby reduce the inherent error-rate of the system. (Yazdi Abstract)

This paper proposes a control theoretic framework to model and analyze the self-organized pattern formation of molecular concentrations in biomolecular communication networks, emerging applications in synthetic biology. In biomolecular communication networks, bionanomachines, or biological cells, communicate with each other using a cell-to-cell communication mechanism mediated by a diffusible signaling molecule, thereby the dynamics of molecular concentrations are approximately modeled as a reaction-diffusion system with a single diffuser. We first introduce a feedback model representation of the reaction- diffusion system and provide a systematic local stability/instability analysis tool using the root locus of the feedback system. The instability analysis then allows us to analytically derive the conditions for the self-organized spatial pattern formation, or Turing pattern formation, of the bionanomachines. (Hori Abstract)

Yeo, Jingjie, et al. Materials-by-Design: Computation, Synthesis, and Characterization from Atoms to Structures. Physica Scripta. 93/5, 2018. In a special Focus Issue on 21st Century Frontiers, MIT Laboratory for Atomistic and Molecular Mechanics scientists including Markus Buehler and Francisco Martin-Martinez (see below) post a wide-ranging survey of how deeper understandings of natural principles can initiate a new material and social creation for a much better future. A particular case is a use of block co-polymers of tandemly repeating units of elastin-like protein sequences to prepare “artificial” silk fabrics.

In the 50 years that succeeded Richard Feynman's exposition of the idea that there is 'plenty of room at the bottom' for manipulating individual atoms for the synthesis and manufacturing processing of materials, the materials-by-design paradigm is being developed gradually through synergistic integration of experimental material synthesis and characterization with predictive computational modeling and optimization. This paper reviews how this paradigm creates the possibility to develop materials according to specific, rational designs from the molecular to the macroscopic scale. These include recombinant protein technology to produce peptides and proteins with tailored sequences encoded by recombinant DNA, self-assembly processes induced by conformational transition of proteins, additive manufacturing for designing complex structures, and qualitative and quantitative characterization of materials at different length scales. (Abstract excerpt)

Some philosophical thoughts: surviving on a small planet with limited resources to support our increasing global population is probably one of the greatest challenges humanity has faced so far. A large part of the problem is that our economy is driven by many technologies that are not sustainable at all. Most of the greatest solutions to technological problems have been already solved by nature, which is a source of inspiration. My research contributes to a big picture that creatively integrates bio-inspiration, nanotechnology, multi-scale modeling, process engineering and additive manufacturing to address such challenges. (Fransisco Martin-Martinez website)

Yuan, H. Y., et al. Quantum Magnonics: When Magnon Spintronics meets Quantum Information Science. Physics Reports. April, 2022. We choose this entry by five researchers based in the Netherlands, China, and Spain as an example of how current frontier mind/matter studies of deep physical phenomena keep becoming more amenable and animate, The entry signifies the prowess of our Earthuman facilities to learn all about and move toward a grand integral uniVerse synthesis.

Spintronics and quantum information science are two promising candidates for innovating information processing technologies. Their combination can enable solid-state platforms for realizing multi-functional quantum tasks. Significant advances in the entanglement of quasi-particles and in designing high-quality qubits and photonic cavities for quantum information processing provide a physical basis to integrate magnons with quantum systems. From these endeavours, the interdisciplinary field of quantum magnonics emerges, which combines spintronics, quantum optics and quantum information science. s We discuss how magnonic systems can be integrated with quantum cavity photons, superconducting qubits, nitrogen-vacancy centers, and phonons for coherent information transfer and collaborative information processing. (Excerpt)

Zhang, Jing, et al. Quantum Feedback: Theory, Experiments, and Applications. Reports on Progress in Physics. Online March, 2017. A collaboration of Chinese and American computational physicists post a 60 page technical paper in this title regard. Our interest is the appearance of novel, seemingly limitless human capabilities which are proceeding to take over from here, as apparently intended because we can, these most fundamental depths of cosmic material creation.

The control of individual quantum systems is now a reality in a variety of physical settings. Feedback control is an important class of control methods because of its ability to reduce the effects of noise. In this review we give an introductory overview of the various ways in which feedback may be implemented in quantum systems, the theoretical methods that are currently used to treat it, the experiments in which it has been demonstrated to date, and its applications. In the last few years there has been rapid experimental progress in the ability to realize quantum measurement and control of mesoscopic systems. We expect that the next few years will see further rapid advances in the precision and sophistication of feedback control protocols realized in the laboratory. (Abstract)

Zhang, R. H., et al. An Informatics Guided Classification of Miscible and Immiscible Binary Alloy Systems. Nature Scientific Reports. 7/9577, 2017. We note this entry by a nine person international group from China, the Czech Republic and USA as an example of how a full avail of computational methods can serve the frontiers of programmable materials research and formulation.

The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design. (Abstract)

Zheludev, Nikolay. The Road Ahead for Metamaterials. Science. 328/582, 2010. The deputy director of the Optoelectronics Research Centre and Centre for Nanostructured Photonic Metamaterials, University of Southampton, acclaims this opening frontier vista whence human collaborative intellect, as regnant mind may begin to take over matter, might imagine a new, second, natural creation. At their website http://www.nanophotonics.org.uk/niz/publications/ can be found many typical, technical publications.

The next stage of this technological revolution will be the development of active, controllable, and nonlinear metamaterials surpassing natural media as platforms for optical data processing and quantum information applications. (582) Metamaterials enable us to design our own “atoms” and thus create materials with new properties and functions. Metamaterials are artificial media structured on a size scale smaller than the wavelength of external stimuli. Whereas conventional materials derive their electromagnetic characteristics from the properties of atoms and molecules, metamaterials enable us to design our own “atoms” and thus access new functionalities, such as invisibility and imaging, with unlimited resolution. (582)

Zheng, Xiaolong, et al. Machine Learning Material Properties from the Periodic Table using Convolutional Neural Networks. Chemical Science. 9/8426, 2018. In this Royal Society of Chemistry journal, Hangzhou Dianzi University and Northwest University, Xi'an computational chemists achieve a novel application of this multiplex connective method by which to better study the atomic elements in the 21st century.

In recent years, convolutional neural networks (CNNs) have achieved great success in image recognition with powerful feature extraction ability. Here we show that CNNs can learn the inner structure and chemical information in the periodic table. Using the periodic table as representation, and full-Heusler compounds in the Open Quantum Materials Database (OQMD) as training and test samples, a multi-task CNN was trained to output the lattice parameter and enthalpy of formation. Our results indicate that the two-dimensional inner structure of the periodic table was learned by the CNN as useful chemical information. (Abstract excerpt)

Zhou, Quan, et al. Learning Atoms for Materials Discovery. Proceedings of the National Academy of Sciences. 115/E6411, 2018. A team of Chinese-American physicists at Stanford University and Temple University describe a sophisticated endeavor to apply machine computation and neural net learning to initiate a novel phase of crystal and chemical creativity. In the process, they noticed that the methods seem to take on a literary and linguistic semblance, which in turn extended to quantum phases. In the references can be found Distributed Representations of Words and Phrases, and GloVe: Global Vectors for Word Representation, along with Quantum-Chemical Insights from Deep Tensor Neural Networks (search Schutt). In regard, a further entry is achieved of how much the cosmos to culture span is innately textual in nature. It then occurred to me that the term “atomic” might be able to accrue an “at-omics” identity.

Exciting advances have been made in artificial intelligence (AI) during recent decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition, and natural language understanding. In this work, we show that our unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials. These learned properties are represented in terms of high-dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups consistent with human knowledge. We use the atom vectors as basic input units for neural networks and other ML models designed and trained to predict materials properties, which demonstrate significant accuracy. (Abstract)

Summary and Outlook: We introduce unsupervised learning of atoms from a database of known existing materials and show the rediscovery of the periodic table by AI. The learned feature vectors not only capture well the similarities and properties of atoms in a vector space, but also show their superior effectiveness over simple empirical descriptors when used in ML problems for materials science. While empirical descriptors are usually designed specifically for a task, our learned vectors from unsupervised learning should be general enough to be applied to many cases. We anticipate their effectiveness and broad applicability can greatly boost the data-driven approaches in today’s materials science, especially for the recently proposed deep neural network methods (29–32), the same as the huge success of word vectors in language modeling. (5-6)

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