jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniela E. Blanco | Bryan Lee | Miguel A. Modestino
Source: ChemRxiv (preprint, 2019-05-08)
Field: Chemistry — Electrochemistry

Optimizing Organic Electrosynthesis through Controlled Voltage Dosing and Artificial Intelligence Organic electrocatalysis can transform the chemical industry by introducing new, electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density, due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile, the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5-150 ms, led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process. Moreover, combining this systematic experimental investigation with artificial intelligence (AI) tools allowed us to rapidly discover drastically improved electrosynthetic conditions, reaching improvements of 30% and 325% in ADN production rates and selectivity, respectively. This powerful AI-enhanced experimental approach represents a new paradigm in electrocatalysis research that can accelerate the deployment of electrochemical manufacturing processes. Electrosynthesis ; Context (archaeology) ; Process engineering ; Electrocatalyst ; Computer science ; Electrochemistry ; Software deployment ; Renewable energy ; Nanotechnology ; Materials science ; Chemistry ; Electrode ; Electrical engineering ; Engineering Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Britt E. Erickson
Source: C&EN Global Enterprise (article, 2019-04-06)
Field: Chemistry — Physical and Theoretical Chemistry

C&amp;EN talks with Brian Berridge, toxicology research innovator After more than 40 years of evaluating the hazards of chemicals by testing animals to extrapolate human health effects, the US National Toxicology Program is shifting gears. Under the direction of Associate Director Brian Berridge, the program is embracing innovative technologies such as artificial intelligence and in vitro human cell systems in hopes of making chemical hazard assessments more relevant to people. The NTP is also flipping its process for chemical hazard assessment upside down. Rather than starting with individual chemicals or classes of chemicals and looking for their health hazards, the program is looking into whether particular diseases that have a big impact on public health have chemical origins. Berridge joined the NTP in January 2018, after more than a decade working for the pharmaceutical company GlaxoSmithKline. There, he was the first to lead a group examining the role of animals in drug development. The group was started “with Innovator ; Toxicology ; Political science ; Biology ; Law History and advancements in chemistry
Authors: Andrea Bertolino | Magnus Fürst | Alessandro Parente
Source: Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (article, 2019-05-06)
Field: Chemistry — Spectroscopy

Optimisation of chemical mechanisms for MILD conditions Moderate or Intense Low-oxygen Dilution (MILD) combustion is among the best new technologies for clean and efficient combustion in industrial applications. While large Damköhler numbers indicate mixing controlled flames, flameless combustion is characterized by unitary Damköhler numbers, and the presence of a relevant amount of diluents. Under these conditions, an overlapping between mixing and chemical time scales occurs. For this reason, modelling chemistry with a detailed mechanism is needed. Unfortunately, existing kinetic mechanisms accomplish a non-accurate estimation for this particular regime, even for fuels with a simple and well-known chemistry, such as hydrogen and syngas. Experimental data-driven optimisation for kinetic mechanisms is nowadays a promising technique to solve this problem.This work addresses the following questions: how would the choice of the kinetic mechanism impact its optimisation, if the latter is carried out using common experimental targets? Would the parameters to be improved similar? Would their values converge to similar optimal combinations? The most impactful reactions will be identified using local sensitivity analysis. Then, a local brute force sensitivity analysis on their Arrhenius parameters will help discarding automatically those parameters that are of minor relevance. Finally, an optimization based on evolutionary algorithm will be performed, coupling Dakota and OpenSMOKE++, which minimizes the mean squared error on the overall dataset to find the optimal parameter combination. Computer science Analytical Chemistry and Chromatography
Authors: Guanshen Fang | Massoud Amini | Hao Chen | 信幸 福田 | 治夫 細矢 | 雅弘 河合 | Joseph E. LeBlanc | Paul G. Mezey | 勇夫 成木 | 正 岡田 | Eric Rambo | Mark Spivakovsky | 茂 竹内 | Keith F. Taylor | Hongyi Wong | 聡 山中 | 正明 横谷 | Peter Zizler | 茂 有本
Source: Institutional Repositories DataBase (IRDB) (article, 2019-03-30)
Field: Materials Science — Materials Chemistry

Mathematics and chemistry interdisciplinary joint research and the Fukui Project XXVII This is the 27th part of the series of articles that records and further develops essentials of the Mathematics and Chemistry Interdisciplinary Symposium 2013 Tsuyama, whose main themes were symmetry, periodicity, and repetition. The symposium was held on April 5th and 6th in Tsuyama city, Okayama, Japan, in conjunction with the Fukui Project and was devoted to the memory of the late Professor Kenichi Fukui (1981 Nobel Prize) who initiated the project. The present series also provides challenging cross-disciplinary problems which are directly related to the Fukui conjecture, the Global Pattern Identification (GPI) in the Repeat Space Theory (RST), and Artificial Intelligence (AI). Some of these problems are formulated using mathematical language not well known among chemists despite the importance of these notions in elucidating additivity and high-speed asymptotic phenomena in molecules having many repeating identical moieties. The cross-disciplinary interaction between the Repeat Space Theory and the Spatial Anthropology has been discussed in connection with the Science-Art Multi-angle Network (SAM Network) Project, which seeks to bridge Science and Art (visual, audial, and conceptual) for a creative collaboration, and is an important part of the Fukui Project. Series (stratigraphy) ; Space (punctuation) ; Identification (biology) ; Fukui function ; Connection (principal bundle) ; Conjunction (astronomy) Machine Learning in Materials Science ; Graph theory and applications ; History and advancements in chemistry
Authors: Guanshen Fang | Massoud Amini | Hao Chen | 信幸 福田 | 治夫 細矢 | 雅弘 河合 | Joseph E. LeBlanc | Paul G. Mezey | 勇夫 成木 | 正 岡田 | Eric Rambo | Mark Spivakovsky | 茂 竹内 | Keith F. Taylor | Hongyi Wong | 聡 山中 | 正明 横谷 | Peter Zizler | 茂 有本
Source: Institutional Repositories DataBase (IRDB) (article, 2019-03-30)
Field: Materials Science — Materials Chemistry

Mathematics and chemistry interdisciplinary joint research and the Fukui Project XXVI This is the 26th part of the series of articles that records and further develops essentials of the Mathematics and Chemistry Interdisciplinary Symposium 2013 Tsuyama, whose main themes were symmetry, periodicity, and repetition. The symposium was held on April 5th and 6th in Tsuyama city, Okayama, Japan, in conjunction with the Fukui Project and was devoted to the memory of the late Professor Kenichi Fukui (1981 Nobel Prize) who initiated the project. The present series also provides challenging cross-disciplinary problems which are directly related to the Fukui conjecture, the Global Pattern Identification (GPI) in the Repeat Space Theory (RST), and Artificial Intelligence (AI). Some of these problems are formulated using mathematical language not well known among chemists despite the importance of these notions in elucidating additivity and high-speed asymptotic phenomena in molecules having many repeating identical moieties. The cross-disciplinary interaction between the Repeat Space Theory and the Spatial Anthropology has been discussed in connection with the Science-Art Multi-angle Network (SAM Network) Project, which seeks to bridge Science and Art (visual, audial, and conceptual) for a creative collaboration, and is an important part of the Fukui Project. Series (stratigraphy) ; Space (punctuation) ; Identification (biology) ; Fukui function ; Connection (principal bundle) Machine Learning in Materials Science ; Graph theory and applications ; Computational Drug Discovery Methods
Authors: Guanshen Fang | Massoud Amini | Hao Chen | 信幸 福田 | 治夫 細矢 | 雅弘 河合 | Joseph E. LeBlanc | Paul G. Mezey | 勇夫 成木 | 正 岡田 | Eric Rambo | Mark Spivakovsky | 茂 竹内 | Keith F. Taylor | Hongyi Wong | 聡 山中 | 正明 横谷 | Peter Zizler | 茂 有本
Source: Institutional Repositories DataBase (IRDB) (article, 2019-03-30)
Field: Materials Science — Materials Chemistry

Mathematics and chemistry interdisciplinary joint research and the Fukui Project XXVIII This is the 28th part of the series of articles that records and further develops essentials of the Mathematics and Chemistry Interdisciplinary Symposium 2013 Tsuyama, whose main themes were symmetry, periodicity, and repetition. The symposium was held on April 5th and 6th in Tsuyama city, Okayama, Japan, in conjunction with the Fukui Project and was devoted to the memory of the late Professor Kenichi Fukui (1981 Nobel Prize) who initiated the project. The present series also provides challenging cross-disciplinary problems which are directly related to the Fukui conjecture, the Global Pattern Identification (GPI) in the Repeat Space Theory (RST), and Artificial Intelligence (AI). Some of these problems are formulated using mathematical language not well known among chemists despite the importance of these notions in elucidating additivity and high-speed asymptotic phenomena in molecules having many repeating identical moieties. The cross-disciplinary interaction between the Repeat Space Theory and the Spatial Anthropology has been discussed in connection with the Science-Art Multi-angle Network (SAM Network) Project, which seeks to bridge Science and Art (visual, audial, and conceptual) for a creative collaboration, and is an important part of the Fukui Project. Series (stratigraphy) ; Space (punctuation) ; Identification (biology) ; Fukui function ; Connection (principal bundle) ; Conjunction (astronomy) Machine Learning in Materials Science ; Graph theory and applications ; History and advancements in chemistry
Authors: Sergey Dianov | В.М. Новичков
Source: Civil Aviation High TECHNOLOGIES (article, 2019-08-22)
Field: Materials Science — Materials Chemistry

Formation of the measuring channel for quantum diagnostic computer Modem and perspective tasks of robotics with control from artificial intelligence systems require the use of small-sized measuring devices. In this case, the intensively developed quantum sensors and quantum computers have good prospects. Their main advantage is the ability to successfully process the data of random processes with decomposition of complex functions into simple multipliers, as well as their small size and the ability to transmit data over long distances without wires. Data transmitted over quantum communication lines cannot be copied or intercepted, which is very useful for remote control of complex technical systems. Based on the results of the probabilistic noisy data analysis quantum computer can quickly develop an assessment of the complex system technical condition. At the same time, there is no need to go through all the possible solutions to the evaluation problem with a huge amount of input data, some of which can sometimes be undefined. The main problem in the research of quantum processes is that researchers study the processes occurring in materials, but they do not indicate the ways in which quantum sensors and quantum computers are used in practical applications. This article explains how to form a measurement channel that will be compatible with a quantum computer, how to organize the input of information about the measured physical quantity into a quantum computer using a quantum sensor. The main objective was to bring the results of basic research in the field of quantum computing closer to their application in applied tasks. It is shown how the quantum processes can be shifted to the field of physical quantities technical measurements used in the automatic diagnosis of the technical state of complex systems. In the process of obtaining the results of the study, the deduction method of conclusions within the framework of a systematic approach to the development of technical systems elements was used. The result is a description of the processes in the quantum measuring channel, which is used to enter diagnostic data in a quantum computer. The study main conclusions breed the idea that quantum sensors implemented in the form of a modified diamond crystal are well described by the theory of measuring transducers with frequency output and can be used in diagnostic computers. Computer science ; Quantum computer ; Quantum ; Process (computing) ; Field (mathematics) ; Probabilistic logic ; Computer engineering ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques ; Carbon Nanotubes in Composites
Authors: Edward J. Beard | Ganesh Sivaraman | Álvaro Vázquez‐Mayagoitia | Venkatram Vishwanath | Jacqueline M. Cole
Source: Apollo (University of Cambridge) (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Comparative dataset of experimental and computational attributes of UV/vis absorption spectra. The ability to auto-generate databases of optical properties holds great prospects in data-driven materials discovery for optoelectronic applications. We present a cognate set of experimental and computational data that describes key features of optical absorption spectra. This includes an auto-generated database of 18,309 records of experimentally determined UV/vis absorption maxima, λmax, and associated extinction coefficients, ϵ, where present. This database was produced using the text-mining toolkit, ChemDataExtractor, on 402,034 scientific documents. High-throughput electronic-structure calculations using fast (simplified Tamm-Dancoff approach) and traditional (time-dependent) density functional theory were executed to predict λmax and oscillation strengths, f (related to ϵ) for a subset of validated compounds. Paired quantities of these computational and experimental data show strong correlations in λmax, f and ϵ, laying the path for reliable in silico calculations of additional optical properties. The total dataset of 8,488 unique compounds and a subset of 5,380 compounds with experimental and computational data, are available in MongoDB, CSV and JSON formats. These can be queried using Python, R, Java, and MATLAB, for data-driven optoelectronic materials discovery. Python (programming language) ; Computer science ; JSON ; MATLAB ; Density functional theory ; Computational science ; Data mining ; Database ; Chemistry ; Computational chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Zachary Trautt | Martin L. Green
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Motivating the Collaboratory for High-Throughput Experimental Materials Science and Beyond... A review of roadmapping reports and related documents published over the past decade in materials science and engineering has revealed a set of systemic needs centered around data management as well as emerging needs centered around application of artificial intelligence. In this talk, we will present a thorough of review of past roadmapping reports and related documents. We will then show that our proposed model for the Collaboratory would be an effective model for addressing both the systemic and emerging needs. We will show that the Collaboratory concept is comparable to other entities, which have been successful in other domains. Figure 1 Collaboratory ; Computer science ; Set (abstract data type) ; Data science ; Throughput ; Science and engineering ; World Wide Web ; Engineering ethics ; Engineering Machine Learning in Materials Science
Authors: Jong‐Young Park | Byeong-Jae Choi | Youngjae Kim | Hideo HONMA | Joo-Hyong Noh
Source: Journal of Materials Science and Engineering B (article, 2019-06-28)
Field: Materials Science — Surfaces, Coatings and Films

Flash Lamp Annealing Method for Improving Adhesion Strength on the Dielectric Material and Reducing Substrate Warpage Today's electronic devices have required higher performance properties for 5G and artificial intelligence (AI). High-performance system on chip (SOC), graphic processing unit (GPU), and central processing unit (CPU) requires advanced packages to meet demands for performance, size, and high-speed transmission. To respond to these demands, integration approaches such as 3D IC chip stacking, package on package (PoP), 2.5D interposer integration, system-in-package (SiP), and fan-out packaging technologies have emerged Therefore, the package substrate for high-performance device will require low transmission loss and the small package warpage. Low loss materials have limitation of seed layer formation with electroless Cu plating. Also, heat treatment has major impact on substrate warpage in order for growth of plated Cu metal and curing for epoxy mold compound (EMC) process. In this paper, we believe it is possible to create a seed layer on the low-loss material by combining electroless Cu plating and flash lamp annealing (FLA) method instead of sputtering process. In terms of warpage control and metal growth, the flash lamp treatment, not the conventional convection or hot plate type heat treatment, could improve electro-migration between metal line and line through improving large number of (111) slip directions. In addition, flash lamp not only provides alternative to conventional heat treatment process but also significantly reduces substrate warpage. Through result of this study, by using FLA method for advanced package, it is possible to provide solutions in improving adhesion strength between dielectric materials and deposited metal film, and to reduce the warpage of the substrate. Materials science ; Annealing (glass) ; Dielectric ; Optoelectronics ; Flash-lamp ; Composite material ; Substrate (aquarium) ; Flash (photography) ; Adhesion ; Optics Optical Coatings and Gratings
Authors: Dil K. Limbu | Raymond Atta‐Fynn | Parthapratim Biswas
Source: MRS Advances (preprint, 2019-01-01)
Field: Materials Science — Materials Chemistry

Atomistic simulation of nearly defect-free models of amorphous silicon: An information-based approach Reverse Monte Carlo ; Statistical physics ; Monte Carlo method ; Amorphous solid ; Molecular dynamics ; Amorphous silicon ; Silicon ; Tetrahedron ; Materials science ; Diffraction ; Computer science ; Physics ; Crystalline silicon ; Crystallography ; Chemistry ; Mathematics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies
Authors: George Helman
Source: ALTEX (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard_suppl Generalized Read-Across (GenRA) is a data driven approach that makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA’s CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting the presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues, and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predic­tions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects). GenRA offers a new capability of making reproducible read-across predictions in an easy-to-use interface. Workflow ; Dashboard ; Computer science ; Consistency (knowledge bases) ; Data mining ; Similarity (geometry) ; Information retrieval ; Database ; Artificial intelligence History and advancements in chemistry ; Water Quality Monitoring and Analysis ; Chemistry and Chemical Engineering
Authors: Maja-Olivia Lenz | Thomas A. R. Purcell | David Hicks | Stefano Curtarolo | Matthias Scheffler | Christian Carbogno
Source: npj Computational Materials (preprint, 2019-12-17)
Field: Materials Science — Materials Chemistry

Parametrically constrained geometry relaxations for high-throughput materials science Abstract Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g., Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method’s ability to relax metastable structures, we highlight its adaptability and performance over a test set of 359 materials, across 13 lattice prototypes. Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude. The flexibility of these constraints enables a significant acceleration of high-throughput searches for novel materials for numerous applications. Parametric statistics ; Computer science ; Lattice (music) ; Adaptability ; Metastability ; Perturbation (astronomy) ; Homogeneous space ; Flexibility (engineering) ; Throughput ; Algorithm ; Statistical physics ; Mathematical optimization ; Computational science ; Topology (electrical circuits) ; Physics ; Mathematics ; Geometry ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials