Simulation vs. Understanding: A Tension,in Quantum Chemistry and Beyond. Part B. The March of Simulation,for Better or Worse

In the second part of this Essay, we leave philosophy, and begin by describing Roald's being trashed by simulation. This leads us to a general sketch of artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go-playing program knows. Back to our terrain—we ask “Quantum Chemistry, † ca. 2020?” Then we move to examples of Big Data, machine learning and neural networks in action, first in chemistry and then affecting social matters, trivial to scary. We argue that moral decisions are hardly to be left to a computer. And that posited causes, even if recognized as provisional, represent a much deeper level of understanding than correlations. At this point, we try to pull the reader up, giving voice to the opposing view of an optimistic, limitless future. But we don't do justice to that view—how could we, older mammals on the way to extinction that we are? We try. But then we return to fuss, questioning the ascetic dimension of scientists, their romance with black boxes. And argue for a science of many tongues.     

Simulation vs. Understanding: A Tension,in Quantum Chemistry and Beyond. Part B. The March of Simulation,for Better or Worse

In the second part of this Essay, we leave philosophy, and begin by describing Roald's being trashed by simulation. This leads us to a general sketch of artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go‐playing program knows. Back to our terrain—we ask “Quantum Chemistry, ? ca. 2020?” Then we move to examples of Big Data, machine learning and neural networks in action, first in chemistry and then affecting social matters, trivial to scary. We argue that moral decisions are hardly to be left to a computer. And that posited causes, even if recognized as provisional, represent a much deeper level of understanding than correlations. At this point, we try to pull the reader up, giving voice to the opposing view of an optimistic, limitless future. But we don't do justice to that view—how could we, older mammals on the way to extinction that we are? We try. But then we return to fuss, questioning the ascetic dimension of scientists, their romance with black boxes. And argue for a science of many tongues.     

Looking for an Order of Things: Textbooks and Chemical Classifications in Nineteenth Century France

Abstract

The purpose of this paper is to reconsider the issue of the creativity of textbook writing by exploring the links between nineteenth-century French textbooks and the quest for a classification of elements. The first section presents the elegant combination of didactic and chemical constraints invented by eighteenth-century chemists: the order of learning — from the known to the unknown — and the order of things — from the simple to the complex — were one and the same. In section two we argue that the alleged coincidence did not help the authors of elementary textbooks required for the new schools set up by the French revolution. Hence the variety of classifications adopted in the early nineteenth century. A debate between natural and artificial classifications raised a tension in the 1830s without really dividing the chemical community. Rather it ended up with the adoption of a hybrid classification, combining the rival natural and artificial systems.     

Microclimate monitoring in the Carcer Tullianum: temporal and spatial correlation and gradients evidenced by multivariate analysis; first campaign

Too often microclimate studies in the field of cultural heritage are published without any or scarce information on sampling design, sensors (type, number, position) and instrument validation. Lacking of this fundamental information does not allow an open discussion in the scientific community. This work aims to be an invitation for a different approach.Three main parameters (temperature, humidity, luminance) were monitored in a selected part of a complex construction by an inexpensive self-assembled system along some horizontal and vertical vectors. All data was then processed and analyse by chemometric methods. Some measurements of oxygen, carbon monoxide and dioxide and pressure were also performed.Correlation of some indoor and outdoor data was shown for temperature and humidity. In case of outdoor changes the indoor environment reacted with a certain delay which is position-dependent and more evident for humidity data. The two observed rooms (Carcer and Tullianum) behave differently and the hypogean one is less influenced by the outdoor environment. Instrument validation before and after the campaign, allows to consider detected variations as significant.The fundamental importance of Sampling Design and of instrument validation before and after the monitoring campaign was enhanced. The choice of two main and two minor vectors allowed detection of different behaviour for the two rooms, also permitting to detect for both rooms a trend towards a spontaneous microclimate necessary for a conservation project. In the next campaign we will focus on the choice of the best sampling frequency to use more sophisticated statistical methods.     

Automatic classification of two-dimensional gel electrophoresis pictures by heuristic clustering analysis: a step toward machine learning

The interpretation of two-dimensional gel electrophoresis (2-DGE) profiles can be facilitated by artificial intelligence and machine learning programs. We have incorporated into our 2-DGE computer analysis system (termed MELANIE-Medical Electrophoresis Analysis Interactive Expert system) a program which automatically classifies 2-DGE patterns using heuristic clustering analysis. This program is a step toward machine learning. In this publication, we describe the classification method and the preliminary results obtained with liver biopsy electrophoretograms. Heuristic clustering is also compared to other classification techniques.     

Simulation vs. Understanding: A Tension,in Quantum Chemistry and Beyond. Part A. Stage Setting

We begin our tripartite Essay with a triangle of understanding, theory and simulation. Sketching the intimate tie between explanation and teaching, we also point to the emotional impact of understanding. As we trace the development of theory in chemistry, Dirac's characterization of what is known and what is needed for theoretical chemistry comes up, as does the role of prediction, and Thom's phrase “To predict is not to explain.” We give a typology of models, and then describe, no doubt inadequately, machine learning and neural networks. In the second part, we leave philosophy, beginning by describing Roald's being beaten by simulation. This leads us to artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go‐playing program knows. Back to our terrain—we ask “Quantum Chemistry, ? ca. 2020?” Then move to examples of AI affecting social matters, ranging from trivial to scary. We argue that moral decisions are hardly to be left to a computer. At this point, we try to pull the reader up, giving the opposing view of an optimistic, limitless future a voice. But we don't do justice to that view—how could we? We return to questioning the ascetic dimension of scientists, their romance with black boxes. Onward: In the 3rd part of this Essay, we work our way up from pessimism. We trace (another triangle!) the special interests of experimentalists, who want the theory we love, and reliable numbers as well. We detail in our own science instances where theory gave us real joy. Two more examples‐on magnetic coupling in inorganic diradicals, and the way to think about alkali metal halides, show us the way to integrate simulation with theory. Back and forth is how it should be—between painfully‐obtained, intriguing numbers, begging for interpretation, in turn requiring new concepts, new models, new theoretically grounded tools of computation. Through such iterations understanding is formed. As our tripartite Essay ends, we outline a future of consilience, with a role both for fact‐seekers, and searchers for understanding. Chemistry's streak of creation provides in that conjoined future a passage to art and to perceiving, as we argue we must, the sacred in science.     

Simulation vs. Understanding: A Tension,in Quantum Chemistry and Beyond. Part A. Stage Setting

We begin our tripartite Essay with a triangle of understanding, theory and simulation. Sketching the intimate tie between explanation and teaching, we also point to the emotional impact of understanding. As we trace the development of theory in chemistry, Dirac's characterization of what is known and what is needed for theoretical chemistry comes up, as does the role of prediction, and Thom's phrase “To predict is not to explain.” We give a typology of models, and then describe, no doubt inadequately, machine learning and neural networks. In the second part, we leave philosophy, beginning by describing Roald's being beaten by simulation. This leads us to artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go-playing program knows. Back to our terrain—we ask “Quantum Chemistry, † ca. 2020?” Then move to examples of AI affecting social matters, ranging from trivial to scary. We argue that moral decisions are hardly to be left to a computer. At this point, we try to pull the reader up, giving the opposing view of an optimistic, limitless future a voice. But we don't do justice to that view—how could we? We return to questioning the ascetic dimension of scientists, their romance with black boxes. Onward: In the 3rd part of this Essay, we work our way up from pessimism. We trace (another triangle!) the special interests of experimentalists, who want the theory we love, and reliable numbers as well. We detail in our own science instances where theory gave us real joy. Two more examples-on magnetic coupling in inorganic diradicals, and the way to think about alkali metal halides, show us the way to integrate simulation with theory. Back and forth is how it should be—between painfully-obtained, intriguing numbers, begging for interpretation, in turn requiring new concepts, new models, new theoretically grounded tools of computation. Through such iterations understanding is formed. As our tripartite Essay ends, we outline a future of consilience, with a role both for fact-seekers, and searchers for understanding. Chemistry's streak of creation provides in that conjoined future a passage to art and to perceiving, as we argue we must, the sacred in science.     

Accelerating Optimizing the Design of Carbon-based Electrocatalyst via Machine Learning

In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed-loop system. The approach requires at least three parts including high-throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning-based design of carbon-based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.     

Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Environmental pollution and its drastic effects on human and animal health have urged governments to implement strict policies to minimize damage. The first step in applying such policies is to find reliable methods to detect pollution in various media, including water, food, soil, and air. In this regard, various approaches such as spectrophotometric, chromatographic, and electrochemical techniques have been proposed. To overcome the limitations associated with conventional analytical methods, microfluidic devices have emerged as sensitive technologies capable of generating high content information during the past few years. The passage of contaminant samples through the microfluidic channels provides essential details about the whole environment after detection by the detector. In the meantime, artificial intelligence is an ideal means to identify, classify, characterize, and even predict the data obtained from microfluidic systems. The development of microfluidic devices with integrated machine learning and artificial intelligence is promising for the development of next-generation monitoring systems. Combination of the two systems ensures time efficient setups with easy operation. This review article is dedicated to the recent developments in microfluidic chips coupled with artificial intelligence technology for the evolution of more convenient pollution monitoring systems.     

What Will Chemistry Do in the Next Twenty Years?

The path of chemistry in the future will be determined both by its participation in solving large-scale societal problems and by its generation of new ideas through basic research. This article sketches four of the areas of societal “pull” in which chemistry will play a role in solving applied problems—national security, health care, the environment, and energy—and four areas in which basic research will be especially fruitful—materials chemistry, biological chemistry, computational chemistry, and chemistry exploring the limits of size and speed in chemical phenomena.     

Hybrid Volatolomics and Disease Detection

This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to “volatolomics”—an emerging frontier for fast, risk‐free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so‐called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by “hybrid” approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.     

Computational intelligence techniques in bioinformatics

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.     

Looking for an order of things: textbooks and chemical classifications in nineteenth century France

The purpose of this paper is to reconsider the issue of the creativity of textbook writing by exploring the links between nineteenth-century French textbooks and the quest for a classification of elements. The first section presents the elegant combination of didactic and chemical constraints invented by eighteenth-century chemists: the order of learning - from the known to the unknown - and the order of things - from the simple to the complex - were one and the same. In section two we argue that the alleged coincidence did not help the authors of elementary textbooks required for the new schools set up by the French revolution. Hence the variety of classifications adopted in the early nineteenth century. A debate between natural and artificial classifications raised a tension in the 1830s without really dividing the chemical community. Rather it ended up with the adoption of a hybrid classification, combining the rival natural and artificial systems.     

Computer-assisted interpretation of infrared spectra

Pattern recognition and artificial intelligence programming techniques for the interpretation of infrared spectra are compared in an effort to determine the best technique for assisting the solution of actual structural elucidation problems. For several reasons, artificial intelligence is the method of choice. When information pertaining to a large number of classes is required, an excessively large training set would be needed with pattern recognition procedures. If the program makes a mistake, it must be alterable so that a similar error will not occur again. Artificial intelligence programs are amenable to this correction procedure.     

Intelligent automation of high-performance liquid chromatography method development by means of a real-time knowledge-based approach

We describe the development, attributes and capabilities of a novel type of artificial intelligence system, called LabExpert, for automation of HPLC method development. Unlike other computerised method development systems, LabExpert operates in real-time, using an artificial intelligence system and design engine to provide experimental decision outcomes relevant to the optimisation of complex separations as well as the control of the instrumentation, column selection, mobile phase choice and other experimental parameters. LabExpert manages every input parameter to a HPLC data station and evaluates each output parameter of the HPLC data station in real-time as part of its decision process. Based on a combination of inherent and user-defined evaluation criteria, the artificial intelligence system programs use a reasoning process, applying chromatographic principles and acquired experimental observations to iteratively provide a regime for a priori development of an acceptable HPLC separation method. Because remote monitoring and control are also functions of LabExpert, the system allows full-time utilisation of analytical instrumentation and associated laboratory resources. Based on our experience with LabExpert with a wide range of analyte mixtures, this artificial intelligence system consistently identified in a similar or faster time-frame preferred sets of analytical conditions that are equal in resolution, efficiency and throughput to those empirically determined by highly experienced chromatographic scientists. An illustrative example, demonstrating the potential of LabExpert in the process of method development of drug substances, is provided.     

Artificial Molecular Ratchets: Tools Enabling Endergonic Processes

Non-equilibrium chemical systems underpin multiple domains of contemporary interest, including supramolecular chemistry, molecular machines, systems chemistry, prebiotic chemistry, and energy transduction. Experimental chemists are now pioneering the realization of artificial systems that can harvest energy away from equilibrium. In this tutorial Review, we provide an overview of artificial molecular ratchets: the chemical mechanisms enabling energy absorption from the environment. By focusing on the mechanism type—rather than the application domain or energy source—we offer a unifying picture of seemingly disparate phenomena, which we hope will foster progress in this fascinating domain of science.     

Exploring catalytic solid/liquid interfaces by in situ attenuated total reflection infrared spectroscopy

In situ attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy has gained considerable attention as a powerful tool for exploring processes occurring at solid/liquid and solid/liquid/gas interfaces as encountered in heterogeneous catalysis and electrochemistry. Understanding of the molecular interactions occurring at the surface of a catalyst is not only of fundamental interest but constitutes the basis for a rational design of heterogeneous catalytic systems. Infrared spectroscopy has the exceptional advantage to provide information about structure and environment of molecules. In the last decade, in situ ATR-FT-IR has been developed rapidly and successfully applied for unraveling processes occurring at solid/liquid interfaces. Additionally, the kinetics of complex reactions can be followed by quantifying the concentration of products and reactants simultaneously in a non-destructive way. In this tutorial review we discuss some key aspects which have to be taken into account for successful application of in situ ATR-FT-IR to examine solid/liquid catalytic interfaces, including different experimental aspects concerned with the internal reflection element, catalyst deposition, cell design, and advanced experimental methods and spectrum analysis. Some of these aspects are illustrated using recent examples from our research. Finally, the potential and some limitations of ATR will be elucidated.     

A New Expert System for Structural Interpretation of IR Spectra

A new expert system has been developed which can be used to aid chemists in structural interpretation of infrared spectra. The system consists of five essential portions: knowledge-base , inference engine, database, knowledge-acquisition module and explanatory interface. The system is implemented in Turbo PROLOG artificial intelligence language. Compared with other spectral interpretation systems this system is of the following advantages, i. e. the system has a friendly user interface, two kinds of methods for managing toowledge-base, several useful explanatory facilities such as why and how should explanation be made, etc. ; in addition, it can be run on the IBM PC/XT and its compatible microcomputers. Another important feature of the system is that it can simulate the reasoning procedure by which experienced chemists may interpret spectra as well as deduce complete molecular structures. So the system can also be used as a helpful learning tool for training chemistry students in IR spectrum interpreta     

Semiconductor,molecular and hybrid systems for photoelectrochemical solar fuel production

The paper shortly reviews the basic direct approaches applied in searching for viable solutions to solar fuel production. These are generally distinguished in molecular and semiconductor(non-molecular)systems, however, hybrid strategies, proposed recently, have also been included. The most promising efforts are considered, highlighting key aspects and emerging critical issues. Special attention is paid to aspects such as electrode architecture, device design, and main differences in the scientific vision and challenges to directly produce solar fuels. This overview could be useful to orientate the readers in the wide panorama of research activities concerning water splitting, natural and artificial photosynthesis, and solar fuel production through the identification of common aspects, specialties and potentialities of the many initiatives and approaches that are developing worldwide in this field with the final aim to meet world energy demand.