jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juhwan Noh | Jaehoon Kim | Helge S. Stein | Benjamín Sánchez-Lengeling | John M. Gregoire | Alán Aspuru‐Guzik | Yousung Jung
Source: Matter (article, 2019-10-02)
Field: Materials Science — Materials Chemistry

Inverse Design of Solid-State Materials via a Continuous Representation Representation (politics) ; Generative grammar ; Computer science ; Generative Design ; Space (punctuation) ; Chemical space ; Inverse ; Artificial intelligence ; Materials science ; Mathematics ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Teng Zhou | Zhen Song | Kai Sundmacher
Source: Engineering (review, 2019-08-21)
Field: Materials Science — Materials Chemistry

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided. Big data ; Data science ; Computer science ; Scale (ratio) ; Population ; Risk analysis (engineering) ; Management science ; Nanotechnology ; Engineering ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Jun Zhou | Lei Shen | Miguel Dias Costa | Kristin A. Persson | Shyue Ping Ong | Patrick Huck | Yunhao Lu | Xiaoyang Ma | Yiming Chen | Hanmei Tang | Yuan Ping Feng
Source: Scientific Data (article, 2019-06-12)
Field: Materials Science — Materials Chemistry

2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm and theoretically exfoliated them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data ( http://www.2dmatpedia.org/ ). Computer science ; Top-down and bottom-up design ; Table (database) ; Electronic materials ; Focus (optics) ; Point (geometry) ; Dimension (graph theory) ; Information retrieval ; Data mining ; Materials science ; Nanotechnology ; Physics ; Mathematics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Alfred Ludwig
Source: npj Computational Materials (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods Abstract This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with high-throughput calculations and materials informatics. Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries. Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional, structural and (multi)functional properties of the materials contained in the libraries. The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials, using combinatorial processing. Furthermore, these datasets are the basis for multifunctional existence diagrams, comprising correlations between composition, processing, structure and properties, which can be used for the design of future materials. Characterization (materials science) ; Throughput ; Combinatorial synthesis ; Computer science ; Materials informatics ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Chemistry ; Health informatics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Lianlin Li | Ya Shuang | Qian Ma | Haoyang Li | Hanting Zhao | Menglin Wei | Che Liu | Chenglong Hao | Cheng‐Wei Qiu | Tie Jun Cui
Source: Light Science & Applications (article, 2019-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent metasurface imager and recognizer There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time, we propose the simultaneous use of a smart metasurface imager and recognizer, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy, transforming measured microwave data into images of the whole human body, classifying specifically designated spots (hand and chest) within the whole image, and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities, smart homes, human-device interaction interfaces, health monitoring, and safety screening free of visual privacy issues. Computer science ; Identification (biology) ; Wireless ; Artificial neural network ; Hierarchy ; Real-time computing ; Artificial intelligence ; Telecommunications Metamaterials and Metasurfaces Applications ; Indoor and Outdoor Localization Technologies ; Antenna Design and Analysis
Authors: Tino Gottschall | Konstantin Skokov | Maximilian Fries | Andreas Taubel | Iliya Radulov | Franziska Scheibel | Dimitri Benke | Stefan Riegg | Oliver Gutfleisch
Source: Advanced Energy Materials (article, 2019-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Making a Cool Choice: The Materials Library of Magnetic Refrigeration Abstract The phase‐down scenario of conventional refrigerants used in gas–vapor compressors and the demand for environmentally friendly and efficient cooling make the search for alternative technologies more important than ever. Magnetic refrigeration utilizing the magnetocaloric effect of magnetic materials could be that alternative. However, there are still several challenges to be overcome before having devices that are competitive with those based on the conventional gas–vapor technology. In this paper a rigorous assessment of the most relevant examples of 14 different magnetocaloric material families is presented and those are compared in terms of their adiabatic temperature and isothermal entropy change under cycling in magnetic‐field changes of 1 and 2 T, criticality aspects, and the amount of heat that they can transfer per cycle. The work is based on magnetic, direct thermometric, and calorimetric measurements made under similar conditions and in the same devices. Such a wide‐ranging study has not been carried out before. This data sets the basis for more advanced modeling and machine learning approaches in the near future. Magnetic refrigeration ; Refrigerant ; Adiabatic process ; Materials science ; Isothermal process ; Gas compressor ; Refrigeration ; Magnetic field ; Thermodynamics ; Process engineering ; Work (physics) ; Volatility (finance) ; Engineering physics ; Nuclear engineering ; Mechanical engineering ; Magnetization ; Engineering Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Shape Memory Alloy Transformations
Authors: Meicheng Wen | Guiying Li | Hongli Liu | Jiangyao Chen | Taicheng An | Hiromi Yamashita
Source: Environmental Science Nano (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Metal–organic framework-based nanomaterials for adsorption and photocatalytic degradation of gaseous pollutants: recent progress and challenges Design and engineering of metal–organic frameworks for the adsorption/degradation of inorganic toxic gases, organic vapors and particulate matter. Adsorption ; Pollutant ; Nanomaterials ; Degradation (telecommunications) ; Environmental chemistry ; Photocatalysis ; Metal-organic framework ; Particulates ; Organic matter ; Metal ; Materials science ; Environmental science ; Chemical engineering ; Chemistry ; Nanotechnology ; Catalysis ; Metallurgy ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Catalytic Processes in Materials Science
Authors: Claudia Draxl | Matthias Scheffler
Source: Journal of Physics Materials (article, 2019-03-27)
Field: Materials Science — Materials Chemistry

The NOMAD laboratory: from data sharing to artificial intelligence Abstract The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments. Computer science ; Metadata ; Identifier ; Upload ; Data sharing ; Interoperability ; World Wide Web ; Data science ; Field (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Philomena Schlexer | Kirsten T. Winther | José Antonio Garrido Torres | Verena Streibel | Meng Zhao | Michal Bajdich | Frank Abild‐Pedersen | Thomas Bligaard
Source: ChemCatChem (article, 2019-05-15)
Field: Materials Science — Materials Chemistry

Machine Learning for Computational Heterogeneous Catalysis Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis. Catalysis ; Heterogeneous catalysis ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Jolene P. Reid | Matthew S. Sigman
Source: Nature (article, 2019-07-17)
Field: Chemistry — Spectroscopy

Holistic prediction of enantioselectivity in asymmetric catalysis Enantioselective synthesis ; Chemical space ; Catalysis ; Chemistry ; Workflow ; Biochemical engineering ; Computer science ; Chemical reaction ; Combinatorial chemistry ; Nucleophile ; Computational chemistry ; Organic chemistry ; Database ; Drug discovery Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Wenbo Sun | Yujie Zheng | Ke Yang | Qi Zhang | A.A. Shah | Zhou Wu | Yuyang Sun | Liang Feng | Dongyang Chen | Zeyun Xiao | Shirong Lu | Yong Li | Kuan Sun
Source: Science Advances (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field. Photovoltaic system ; Computer science ; Consistency (knowledge bases) ; Organic solar cell ; Reliability (semiconductor) ; Process (computing) ; Artificial intelligence ; Photovoltaics ; Field (mathematics) ; Property (philosophy) ; Machine learning ; Power (physics) ; Mathematics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Albert Bruix | Johannes T. Margraf | Mie Andersen | Karsten Reuter
Source: Nature Catalysis (article, 2019-06-24)
Field: Materials Science — Materials Chemistry

First-principles-based multiscale modelling of heterogeneous catalysis Computer science ; Field (mathematics) ; Management science ; Function (biology) ; Biochemical engineering ; Data science ; Risk analysis (engineering) ; Nanotechnology ; Systems engineering ; Engineering ; Materials science ; Medicine ; Biology Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Efrat Resnick | A.R. Bradley | Jinrui Gan | A. Douangamath | T. Krojer | Ritika Sethi | Paul P. Geurink | A. Aimon | Gabriel Amitai | Dom Bellini | James M. Bennett | M. Fairhead | Oleg Fedorov | Ronen Gabizon | Gan Jin | Jingxu Guo | A.N. Plotnikov | Nava Reznik | G.F. Ruda | L. Diaz Saez | Verena M. Straub | Tamás Szommer | Srikannathasan Velupillai | Daniel Zaidman | Yanling Zhang | Alun R. Coker | Christopher G. Dowson | Haim Barr | Chu Wang | K. Huber | Paul E. Brennan | Huib Ovaa | F. von Delft | Nir London
Source: Journal of the American Chemical Society (article, 2019-05-07)
Field: Chemistry — Organic Chemistry

Rapid Covalent-Probe Discovery by Electrophile-Fragment Screening Covalent probes can display unmatched potency, selectivity, and duration of action; however, their discovery is challenging. In principle, fragments that can irreversibly bind their target can overcome the low affinity that limits reversible fragment screening, but such electrophilic fragments were considered nonselective and were rarely screened. We hypothesized that mild electrophiles might overcome the selectivity challenge and constructed a library of 993 mildly electrophilic fragments. We characterized this library by a new high-throughput thiol-reactivity assay and screened them against 10 cysteine-containing proteins. Highly reactive and promiscuous fragments were rare and could be easily eliminated. In contrast, we found hits for most targets. Combining our approach with high-throughput crystallography allowed rapid progression to potent and selective probes for two enzymes, the deubiquitinase OTUB2 and the pyrophosphatase NUDT7. No inhibitors were previously known for either. This study highlights the potential of electrophile-fragment screening as a practical and efficient tool for covalent-ligand discovery. Chemistry ; Electrophile ; Covalent bond ; Ligand efficiency ; Combinatorial chemistry ; Drug discovery ; Fragment (logic) ; Ligand (biochemistry) ; Cysteine ; Selectivity ; Deubiquitinating enzyme ; Stereochemistry ; High-throughput screening ; Enzyme ; Biochemistry ; Organic chemistry ; Receptor ; Gene ; Ubiquitin Click Chemistry and Applications ; Ubiquitin and proteasome pathways ; Biochemical and Molecular Research
Authors: Nathan C. Frey | Jin Wang | Gabriel Iván Vega Bellido | Babak Anasori | Yury Gogotsi | Vivek B. Shenoy
Source: ACS Nano (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning Growing interest in the potential applications of two-dimensional (2D) materials has fueled advancement in the identification of 2D systems with exotic properties. Increasingly, the bottleneck in this field is the synthesis of these materials. Although theoretical calculations have predicted a myriad of promising 2D materials, only a few dozen have been experimentally realized since the initial discovery of graphene. Here, we adapt the state-of-the-art positive and unlabeled (PU) machine learning framework to predict which theoretically proposed 2D materials have the highest likelihood of being successfully synthesized. Using elemental information and data from high-throughput density functional theory calculations, we apply the PU learning method to the MXene family of 2D transition metal carbides, carbonitrides, and nitrides, and their layered precursor MAX phases, and identify 18 MXene compounds that are highly promising candidates for synthesis. By considering both the MXenes and their precursors, we further propose 20 synthesizable MAX phases that can be chemically exfoliated to produce MXenes. MXenes ; Materials science ; Carbide ; Bottleneck ; MAX phases ; Graphene ; Nitride ; Nanotechnology ; Metal ; Computer science ; Layer (electronics) ; Metallurgy MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Anand Chandrasekaran | Deepak Kamal | Rohit Batra | Chiho Kim | Lihua Chen | Rampi Ramprasad
Source: npj Computational Materials (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

Solving the electronic structure problem with machine learning Abstract Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size. Density functional theory ; Computer science ; Grid ; Bottleneck ; Emulation ; Artificial neural network ; Kohn–Sham equations ; Electronic structure ; Linear scale ; Invariant (physics) ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Zheng Xiong | Yuxin Cui | Zhonghao Liu | Yong Zhao | Ming Hu | Jianjun Hu
Source: Computational Materials Science (article, 2019-08-27)
Field: Materials Science — Materials Chemistry

Evaluating explorative prediction power of machine learning algorithms for materials discovery using<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-fold forward cross-validation Machine learning ; Algorithm ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Property (philosophy) ; Predictive modelling ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods