Self-Creation of Chemical Structures. Experimental Study.

First Law of Thermodynamics states that every cannot be self-created or destroyed in an isolated system. Chemical systems spontaneously move to steady state. However chemical systems that are open, will create new systems and moving far and far from equilibrium. The simple compounds will spontaneously create unusually complex structures and behaviors. Surprising, general theory of these systems has not been well understanding. Chemical simplest compounds can spontaneously produce complex arrangements, including chemical structures and dynamical behavior. They are building chemical cells that take chemical compounds from outside, next move to the cell, react, and new compounds move outside. Two other compounds may form more tubes that will create tower and next create metropolis. Machines can switch from one system to another. It can move like a snail. It is basic Law of chemical self-creation. We present simple chemical systems that will spontaneously create very complex structured and machines that may be on level of biology and above. In this paper, simple experiments will show that evolution in Universe is simple and create incredible chemical processes. Universal Chemical Machine can produce an infinite number of entities. Chemical organisms are self-created. It is the most important property of matter.

CHEMICAL SCIENCE IN SVERDLOVSK, 1920s–1950s: EMERGENCE OF KEY RESEARCH THEMES AND SCHOOLS

The paper presents an overview analysis of development of chemical research in the city of Sverdlovsk in 1920s–1950s. The author, relying on the theory of frontier modernization, proposes the concepts of frontier and support-point development of Soviet science. The frontier development was associated with peripherality, concentration of efforts in extractive (mining) industries, and a lack of resources for growth. The result of such frontier development was the emergence of a research-educational complex which, by the mid-1930s, included deeply integrated branch research organizations, institutes of the Academy of Sciences, and universities. The leading role was played by physical chemistry of metallurgical processes (and particularly electrochemistry), chemistry of wood and coal, inorganic and analytical chemistry. By the end of 1930s this chemical complex started to lose its frontier characteristics, which is evident from the effort of coal chemists led by I. Ya. Postovsky to develop the pharmaceutical chemistry. Due to the evacuation of enterprises and institutes from the western parts of USSR during the Great Patriotic War, the chemical complex of Sverdlovsk acquired a support-point character associated with the appearance of duplicate centers on the periphery. The new branches of chemical science emerged, for instance, the chemistry of polymers and the chemical machine-building. The implementation of the Soviet atomic project in Urals in late 1940s — early 1950s completed the paradigm shift in development of chemical science in Sverdlovsk, laying the foundation for the transformation of the city into a leading center of materials science.

Dynamics of Global Technological Trends in the Context of Patent Data

In modern dynamic digital conditions of interaction with external stakeholders, the future success of companies directly depends on their innovative activity, effective implementation into business processes of research and development results in the field of new technologies. The rapid development of technologies in recent years accelerates the spread of innovations, which leads to increased competition, so the organization’s ability to implement process innovations to increase its own competitiveness will determine its effectiveness and efficiency in the future. The article explores the main global technological trends by fixating on the technological spheres with the maximum number of published patent applications, as well as the average increase in the number of applications for a ten-year period in the context of technological industries. A comparison of business areas, whose representatives hold the highest rating positions in the annual world researches of innovation and success (brand value) of companies, is carried out. According to the results of the research, the most patentable technological industries are identified, which included, speaking of electrical engineering, the following: electric machines, devices, energy; digital communication and computer technologies. Among chemical technologies, the largest number of published applications was recorded in such sectors as: pharmaceuticals; chemistry of basic materials, chemical engineering. In the field of machine building, the largest number of applications was published in the transport industry sector and in relation to other special machines. The highest values of the average growth of the mentioned indicator are observed in such areas as IT methods in management, food chemistry, ecological technologies, chemical machine building, other special machines and machine-tool building. The regional aspect of technology patenting is also specified, which consists in shifting innovation activity in the emerging technological spheres towards Asian countries. The most active companies in patenting were State Grid Corp of China, Mitsubishi Electric Corp, Toyota Jidosha KK, IBM, Huawei Technologies, Samsung Electronics.

QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules

In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry. In this work, we put forward the QM-symex with 173-kilo molecules. Each organic molecular in the QM-symex combines with the Cnh symmetry composite and contains the information of the first ten singlet and triplet transitions, including energy, wavelength, orbital symmetry, oscillator strength, and other quasi-molecular properties. QM-symex serves as a benchmark for quantum chemical machine learning models that can be effectively used to train new models of excited states in the quantum chemistry region as well as contribute to further development of the green energy revolution and materials discovery.

Representation of Molecular Structures with Persistent Homology Leads to the Discovery of Molecular Groups with Enhanced CO2 Binding

<p>Developing alternative strategies for efficient separation of CO2 and N2 is of general interest for the reduction of anthropogenic carbon emissions. In recent years, machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise and size-consistent molecular representation derived from persistent homology,an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.</p>

Representation of Molecular Structures with Persistent Homology Leads to the Discovery of Molecular Groups with Enhanced CO2 Binding

<p>Developing alternative strategies for efficient separation of CO2 and N2 is of general interest for the reduction of anthropogenic carbon emissions. In recent years, machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise and size-consistent molecular representation derived from persistent homology,an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.</p>

Neurotransmitters and signalling

Chemical transmission at brain synapses is critical to neuronal signalling, and when it goes wrong, psychiatric or neurological disorder likely ensues. Chemical transmission was once considered to involve a simple ‘forward-direction’, on–off signal generated through the interaction between one of a small number of neurotransmitter molecules and their receptor. This concept has evolved to the synapse being viewed as an extremely complex chemical machine that utilizes a vast array of neurotransmitter-specific proteins to assemble, store, mobilize, and break down one or more of potentially hundreds of chemically diverse transmitter molecules. Individual molecules generate signals over different timescales and spatial domains by interacting with multiple receptor types. Moreover, rather than functioning through the use of a single neurotransmitter, most, if not all, synapses likely operate through the co-release of multiple neurotransmitters. Even the idea that signals are transmitted in a ‘forward direction’ is an oversimplification—certain neurotransmitters signal in the reverse direction. These issues, and more, are reviewed here.

Representation of Molecular Structures with Persistent Homology Leads to the Discovery of Molecular Groups with Enhanced CO2 Binding

<p>Developing alternative strategies for efficient separation of CO2 and N2 is of general interest for the reduction of anthropogenic carbon emissions. In recent years, machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise and size-consistent molecular representation derived from persistent homology,an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.</p>