jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claudia Draxl | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2019-04-11)
Field: Materials Science — Materials Chemistry

Big-Data-Driven Materials Science and its FAIR Data Infrastructure This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges. Interoperability ; Data science ; Computer science ; USable ; Big data ; Field (mathematics) ; Data sharing ; Open research ; World Wide Web ; Data mining Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Research Data Management Practices
Authors: Alexandre Varnek
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2019-06-25)
Field: Materials Science — Materials Chemistry

Artificial Intelligence: The Future for Synthetic Chemistry? 2nd French-Israeli Workshop on Cheminformatics, University of Strasbourg, 13-14 February 2020 Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Dil K. Limbu | Stephen R. Elliott | Raymond Atta‐Fynn | Parthapratim Biswas
Source: arXiv (Cornell University) (preprint, 2019-12-04)
Field: Materials Science — Materials Chemistry

Disorder by design: A data-driven approach to amorphous semiconductors\n without total-energy functionals This paper addresses a difficult inverse problem that involves the\nreconstruction of a three-dimensional model of tetrahedral amorphous\nsemiconductors via inversion of diffraction data. By posing the\nmaterial-structure determination as a multi-objective optimization program, it\nhas been shown that the problem can be solved accurately using a few structural\nconstraints, but no total-energy functionals/forces, which describe the local\nchemistry of amorphous networks. The approach yields highly realistic models of\namorphous silicon, with no or only a few coordination defects ($\\le$ 1%), a\nnarrow bond-angle distribution of width 9-11.5 degree, and an electronic gap of\n0.8-1.4 eV. These data-driven information-based models have been found to\nproduce electronic and vibrational properties of amorphous silicon that match\naccurately with experimental data and rival that of the Wooten-Winer-Weaire\n(W3) models. The study confirms the effectiveness of a multi-objective\noptimization approach to the structural determination of complex materials, and\nresolves a long-standing dispute concerning the uniqueness of a model of\ntetrahedral amorphous semiconductors obtained via inversion of diffraction\ndata.\n Amorphous semiconductors ; Tetrahedron ; Amorphous silicon ; Amorphous solid ; Uniqueness ; Semiconductor ; Inversion (geology) ; Inverse ; Diffraction ; Materials science ; Inverse problem ; Silicon ; Statistical physics ; Physics ; Mathematics ; Crystallography ; Chemistry ; Mathematical analysis ; Optics ; Geometry ; Crystalline silicon ; Optoelectronics ; Geology Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies ; Nonlinear Optical Materials Studies
Authors: Philip Kocieński
Source: Synfacts (article, 2019-10-18)
Field: Chemistry — Organic Chemistry

Artificial Intelligence Guides the Synthesis of SCH 47949 Key words SCH 47949 - 4-arylazetidinones - electrocyclic ring closure - Buchwald amidation - copper(I) catalysis Chemistry Synthesis of β-Lactam Compounds ; Cancer Treatment and Pharmacology ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Haris Hasić | 貴士 石田 | Takashi Ishida
Source: Institutional Repositories DataBase (IRDB) (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Predicting Chemical Reaction Routes with Artificial Intelligence-based Retrosynthesis Retrosynthetic analysis ; Chemical control ; Chemical products ; Work (physics) ; Prospection Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: John A. Schlueter
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Designing Materials to Revolutionize and Engineer the Future of Clean Energy Technologies through Computationally-Led and Data-Driven Approaches The Materials Genome Initiative (MGI) is a multi-agency partnership that seeks to accelerate the pace of materials development across the Materials Development Continuum. Through use of a computationally-led and data-driven approach, MGI promotes the rapid discovery and progression to deployment of advanced materials that will ensure sustained American leadership in sectors including clean energy. The goals of the MGI include 1) leading a culture shift in materials research through use of an iterative feedback-loop approach, 2) integrating experiment, computation, and theory throughout the materials research community, 3) making digital data accessible and useful to the larger community, and 4) creating a world-class materials science and engineering workforce. The Designing Materials to Revolutionize and Engineer our Future (DMREF) program at the National Science Foundation (NSF) partners with a number of other federal agencies to promote these objectives. DMREF currently funds about one hundred projects that cover the full spectrum of materials research, including a number that focus on energy conversion technologies. This talk will provide an overview of the DMREF program and provide specific examples of ways that DMREF teams are benefitting from collaboration with MGI-related efforts at other federal agencies. Interactions with the U.S. Department of Energy's (DOE) Energy Materials Network (EMN) will be highlighted. Future priorities and funding opportunities for the DMREF program will be discussed. General partnership ; Software deployment ; Pace ; Agency (philosophy) ; Workforce ; Engineering management ; Engineering ; Computer science ; Business ; Political science Machine Learning in Materials Science
Authors: Aidan P. Thompson
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Predictive Atomistic Simulations of Materials using SNAP Data-Driven Potentials Abstract not provided. Snap ; Statistical physics ; Materials science ; Physics ; Computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Materials and Properties
Authors: He Zhao
Source: PhDT (article, 2019-01-01)
Field: Materials Science — General Materials Science

Modeling of Structure-Property Relationship in Polymer Nanocomposites with Finite Element and Data Driven Methods Finite element method ; Property (philosophy) ; Materials science ; Polymer ; Mathematics ; Composite material ; Structural engineering ; Engineering ; Epistemology Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Polymer Nanocomposites and Properties
Authors: Roel Moonen
Source: 12th Natural Gas Conversion Symposium (article, 2019-06-05)
Field: Materials Science — Materials Chemistry

Catalytic Options for Methane Valorization Catalyst Performance Evaluation Using Parallel Fixed Bed Reactor Systems and Data Driven Catalyst Development Catalysis ; Methane ; Process engineering ; Environmental science ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Tzyy-Shyang Lin | Connor W. Coley | Hidenobu Mochigase | Haley K. Beech | Wencong Wang | Zi Wang | Eliot Woods | Stephen L. Craig | Jeremiah A. Johnson | Julia A. Kalow | Klavs F. Jensen | Bradley D. Olsen
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2019-09-01)
Field: Materials Science — Surfaces, Coatings and Films

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular "simplified molecular-input line-entry system" (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community. Theory of computation ; Notation ; Macromolecule ; Line (geometry) ; Computer science ; Computational biology ; Mathematics ; Programming language ; Biology ; Genetics ; Arithmetic ; Geometry Electron and X-Ray Spectroscopy Techniques
Source: Science and Technology of Advanced Materials (article, 2019-06-05)
Field: Materials Science — Materials Chemistry

Notice of Duplicate Publication: Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials This article refers to:Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials Notice ; Thermoelectric effect ; Electron ; Materials science ; Thermoelectric materials ; Relaxation (psychology) ; Condensed matter physics ; Engineering physics ; Physics ; Thermodynamics ; Nuclear physics ; Political science ; Medicine Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Neural Networks and Applications
Authors: Mark Peplow﻿
Source: C&EN Global Enterprise (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Automation for the people High-tech synthesis equipment is relatively common in the pharmaceutical industry, where researchers use high-throughput robotic platforms to optimize reaction conditions or screen catalysts. But these facilities are still rare in academia, and many PhD students in synthetic chemistry have little or no experience with the technology. Academic programs are now aiming to close that skills gap and redefine what it means to be a synthetic chemist. One of these programs is based at the Centre for Rapid Online Analysis of Reactions (ROAR), an automated synthesis suite that opened this year at Imperial College London. ROAR’s launch is part of a broader drive to transform synthetic chemistry into a truly data-driven discipline and may herald a time when automated synthesis suites are as ubiquitous at universities as nuclear magnetic resonance facilities. A stint in process development at BASF, a role analyzing Australia’s natural gas supply chain, and a summer in private Automation ; Computer science ; Systems engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemistry and Chemical Engineering
Source: 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON) (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

UKRCON 2019 Welcome The following topics are dealt with: optimisation; image colour analysis; electromagnetic wave scattering; statistical analysis; learning (artificial intelligence); entropy; probability; permittivity; control system synthesis; dielectric materials. Computer science Machine Learning in Materials Science ; Microwave Imaging and Scattering Analysis ; Advanced Computational Techniques in Science and Engineering
Authors: J-M A Nabholtz | KhalidA AlSaleh | Sharif Kullab | Nashwa Abdel-Aziz | Ahmed Abdelwarith | Abdulrahman Al Diab | Muath Abu Hilal | Farida Dabouz | Vladimir B. Bajić | Roberto Incitti | M.R.K. Bahadoor | A.M. Azeer
Source: Annals of Oncology (article, 2019-10-01)
Field: Chemistry — Spectroscopy

Molecular fingerprinting in breast cancer (BC) screening using Quantum Optics (QO) technology combined with an artificial intelligence (AI) approach applying the concept of “molecular profiles at n variables (MPnV)”: A prospective pilot study Mammography ; Breast cancer ; Medicine ; Artificial intelligence ; Oncology ; Medical physics ; Cancer ; Computer science ; Optics ; Physics ; Internal medicine Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Source: Focus on Pigments (article, 2019-11-29)
Field: Chemistry — Organic Chemistry

Sumika acquires the Emas plastic compounding group in Turkey Compounding ; Stereoselectivity ; Yield (engineering) ; Ring (chemistry) ; Group (periodic table) ; Key (lock) ; Lactam ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Materials science ; Catalysis ; Composite material Synthesis of β-Lactam Compounds ; Synthetic Organic Chemistry Methods ; Asymmetric Synthesis and Catalysis
Authors: Benjamin List | Joyce A. A. Grimm
Source: Synfacts (article, 2019-03-19)
Field: Chemistry — Spectroscopy

Application of Chemoinformatics in Asymmetric Catalysis Key words chiral phosphoric acids - catalyst design - chemoinformatics - acetals - artificial intelligence Cheminformatics ; Chemistry ; Catalysis ; Key (lock) ; Organic chemistry ; Organocatalysis ; Combinatorial chemistry ; Phosphoric acid ; Computational chemistry ; Enantioselective synthesis ; Computer science Analytical Chemistry and Chromatography ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Alejandro Mejia | Jiaxin Zhu | Stephen S. Nonnenmann | David S. Mebane
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Developing Data Driven Models to Study Electrocatalytic CO<sub>2</sub> Reduction on Ceria As the rates of carbon dioxide emissions have continually increased on a global scale, mainly due to high demand of fossil fuel burning for transportation and generating electricity, an inevitable interest in managing and recycling the CO 2 byproducts has emerged. New solutions are focusing on sustainable reduction of CO 2 to CO, which can be conveniently converted to useful hydrocarbon liquid fuel products, such as methane, using existing technologies and infrastructure, as well as renewably generated electricity. Of particular interest in this field is the development and improvement of effective CO 2 reduction catalysts, which ceria-based materials have been shown to be suitable for intermediate temperature solid oxide fuel cells. However, understanding the physical mechanisms by which ceria-based catalysts reduce CO 2 has posed a challenging problem. In this work, recently-developed continuum approaches for the near-surface chemistry of high-temperature mixed conductors and data driven model-building routines have been combined to gain a quantitative understanding of the physical phenomenon of high-temperature CO 2 reduction on ceria catalysts. The main insights pertain to the surface and near-surface defect structure of these materials, where the presence of oxygen vacancies is linked to the enhanced catalytic activity. The experimental data is in-operando scanning surface potential probe measurements performed on specially-designed patterned and thin-film test cells at temperature and under polarization. Preliminary results of data-driven model building will be presented and discussed. Oxide ; Catalysis ; Methane ; Materials science ; Fossil fuel ; Hydrocarbon ; Electricity ; Nanotechnology ; Process engineering ; Fuel cells ; Chemical engineering ; Environmental science ; Waste management ; Chemistry ; Metallurgy ; Engineering ; Electrical engineering ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: James Vecchio
Source: Thermal spray (article, 2019-05-26)
Field: Materials Science — Materials Chemistry

The Development of Next Generation Wear Plate Overlays Using Big Data Abstract This study shows how data-driven modeling tools aid in the development of alloys that meet specific processing, property, and performance requirements. By leveraging a “big data” approach, two new alloys were designed that outperform commonly used materials in hardfacing and wear plate overlay applications. Over one million alloy compositions were analyzed to find two with the right combination of matrix and hard phases to provide the desired level of impact and abrasion resistance. The difference between the two alloys is in their matrix phase; one being austenitic, which has higher toughness, the other being martensitic, which has higher resistance to abrasive wear. Hardfacing ; Austenite ; Abrasive ; Materials science ; Abrasion (mechanical) ; Wear resistance ; Martensite ; Overlay ; Metallurgy ; Alloy ; Toughness ; Big data ; Shape-memory alloy ; Computer science ; Composite material ; Microstructure Metal Alloys Wear and Properties ; Advanced materials and composites ; Additive Manufacturing Materials and Processes
Authors: Priyanka Thakral | Vimal Rarh | Ashok Kumar Bakhshi
Source: Current Physical Chemistry (article, 2019-02-26)
Field: Materials Science — Polymers and Plastics

<i>In-silico</i> Molecular Modeling of Low Band Gap Intrinsically Conducting Copolymers Background: Over the last four decades, tremendous growth has been witnessed in the field of electrically conducting polymers. A great deal of theoretical and empirical efforts have been made to achieve multifunctional conducting polymeric structures. However, the major challenge in this field is achieving the minimum band gap value which governs various electronic and optoelectronic properties of the structure. Objective: Artificial optimization viz., metaheuristic algorithms have been clubbed with the polymer problem to investigate the electronic properties of copolymers. Method: Band structures of different homopolymers obtained from ab-initio Hartree-Fock crystal orbital method have been used as input to obtain the electronic properties of copolymers using genetic algorithm, ant colony optimization and particle swarm optimization. Result: Nature-based computing methods employed for tailoring intrinsically conducting copolymers correspond to optimal electronic properties. Conclusion: This computational cloning approach provides a cost-effective and potent passage for taking forward optimized theoretical solutions to synthetic environment. Keywords: Ant algorithm, artificial intelligence, conducting polymers, copolymerization, electronic properties, genetic algorithm, in-silico optimization, particle swarm optimization. Particle swarm optimization ; Metaheuristic ; Copolymer ; Electronic structure ; Materials science ; Ab initio ; Band gap ; Computer science ; Field (mathematics) ; Ant colony optimization algorithms ; Polymer ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Optoelectronics Conducting polymers and applications
Authors: Runhai Ouyang | Emre Ahmetcik | Christian Carbogno | Matthias Scheffler | Luca M. Ghiringhelli
Source: arXiv (Cornell University) (preprint, 2019-01-04)
Field: Materials Science — Materials Chemistry

Simultaneous Learning of Several Materials Properties from Incomplete Databases with Multi-Task SISSO The identification of descriptors of materials properties and functions that capture the underlying physical mechanisms is a critical goal in data-driven materials science. Only such descriptors will enable a trustful and efficient scanning of materials spaces and possibly the discovery of new materials. Recently, the sure-independence screening and sparsifying operator (SISSO) has been introduced and was successfully applied to a number of materials-science problems. SISSO is a compressed-sensing based methodology yielding predictive models that are expressed in form of analytical formulas, built from simple physical properties. These formulas are systematically selected from an immense number (billions or more) of candidates. In this work, we describe a powerful extension of the methodology to a 'multi-task learning' approach, which identifies a single descriptor capturing multiple target materials properties at the same time. This approach is specifically suited for a heterogeneous materials database with scarce or partial data, e.g., in which not all properties are reported for all materials in the training set. As showcase examples, we address the construction of materials-properties maps for the relative stability of octet-binary compounds, considering several crystal phases simultaneously, and the metal/insulator classification of binary materials distributed over many crystal-prototypes. Computer science ; Binary number ; Set (abstract data type) ; Task (project management) ; Operator (biology) ; Database ; Binary classification ; Stability (learning theory) ; Machine learning ; Mathematics ; Support vector machine ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques