jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuangjia Zheng | Jiahua Rao | Zhongyue Zhang | Jun Xu | Yuedong Yang
Source: Journal of Chemical Information and Modeling (article, 2019-12-11)
Field: Materials Science — Materials Chemistry

Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot provide satisfactory results. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis using transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of the reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark data set, which outperformed other deep learning methods by >21% and template-based methods by >6%. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set. Retrosynthetic analysis ; Transformer ; Artificial neural network ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Set (abstract data type) ; Machine translation ; Process (computing) ; Machine learning ; Training set ; Engineering ; Programming language ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Kirsten T. Winther | Max J. Hoffmann | Jacob R. Boes | Osman Mamun | Michal Bajdich | Thomas Bligaard
Source: Scientific Data (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Catalysis-Hub.org, an open electronic structure database for surface reactions We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation. Chemisorption ; Bimetallic strip ; Catalysis ; Database ; Computer science ; Materials science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Fahime Bigdeli | Christina Lollar | Ali Morsali | Hong‐Cai Zhou
Source: Angewandte Chemie International Edition (review, 2019-05-28)
Field: Chemistry — Inorganic Chemistry

Switching in Metal–Organic Frameworks In recent years, metal-organic frameworks (MOFs) have become an area of intense research interest because of their adjustable pores and nearly limitless structural diversity deriving from the design of different organic linkers and metal structural building units (SBUs). Among the recent great challenges for scientists include switchable MOFs and their corresponding applications. Switchable MOFs are a type of smart material that undergo distinct, reversible, chemical changes in their structure upon exposure to external stimuli, yielding interesting technological applicability. Although the process of switching shares similarities with flexibility, very limited studies have been devoted specifically to switching, while a fairly large amount of research and a number of Reviews have covered flexibility in MOFs. This Review focuses on the properties and general design of switchable MOFs. The switching activity has been delineated based on the cause of the switching: light, spin crossover (SCO), redox, temperature, and wettability. Metal-organic framework ; Materials science ; Business ; Chemistry ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Nanoplatforms for cancer theranostics
Authors: Andreas Singraber | Jörg Behler | Christoph Dellago
Source: Journal of Chemical Theory and Computation (article, 2019-01-24)
Field: Materials Science — Materials Chemistry

Library-Based <i>LAMMPS</i> Implementation of High-Dimensional Neural Network Potentials Neural networks and other machine learning approaches have been successfully used to accurately represent atomic interaction potentials derived from computationally demanding electronic structure calculations. Due to their low computational cost, such representations open the possibility for large scale reactive molecular dynamics simulations of processes with bonding situations that cannot be described accurately with traditional empirical force fields. Here, we present a library of functions developed for the implementation of neural network potentials. Written in C++, this library incorporates several strategies resulting in a very high efficiency of neural network potential-energy and force evaluations. Based on this library, we have developed an implementation of the neural network potential within the molecular dynamics package LAMMPS and demonstrate its performance using liquid water as a test system. Artificial neural network ; Computer science ; Molecular dynamics ; Artificial intelligence ; Energy (signal processing) ; Computational science ; Machine learning ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Robin Taylor | P.A. Wood
Source: Chemical Reviews (review, 2019-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts The founding in 1965 of what is now called the Cambridge Structural Database (CSD) has reaped dividends in numerous and diverse areas of chemical research. Each of the million or so crystal structures in the database was solved for its own particular reason, but collected together, the structures can be reused to address a multitude of new problems. In this Review, which is focused mainly on the last 10 years, we chronicle the contribution of the CSD to research into molecular geometries, molecular interactions, and molecular assemblies and demonstrate its value in the design of biologically active molecules and the solid forms in which they are delivered. Its potential in other commercially relevant areas is described, including gas storage and delivery, thin films, and (opto)electronics. The CSD also aids the solution of new crystal structures. Because no scientific instrument is without shortcomings, the limitations of CSD research are assessed. We emphasize the importance of maintaining database quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ignore the principle of garbage in, garbage out. Finally, we explain why the CSD must evolve with the world around it to ensure it remains fit for purpose in the years ahead. Garbage ; Quality (philosophy) ; Chemistry ; Nanotechnology ; Multitude ; Data science ; Value (mathematics) ; Database ; Computer science ; Epistemology Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yaolong Zhang | Ce Hu | Bin Jiang
Source: The Journal of Physical Chemistry Letters (article, 2019-08-09)
Field: Materials Science — Materials Chemistry

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation We propose a simple, but efficient and accurate, machine learning (ML) model for developing a high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom method (EAM) model used in the condensed phase. It simply replaces the scalar embedded atom density in EAM with a Gaussian-type orbital based density vector and represents the complex relationship between the embedded density vector and atomic energy by neural networks. We demonstrate that the EANN approach is equally accurate as several established ML models in representing both big molecular and extended periodic systems, yet with much fewer parameters and configurations. It is highly efficient as it implicitly contains the three-body information without an explicit sum of the conventional costly angular descriptors. With high accuracy and efficiency, EANN potentials can vastly accelerate molecular dynamics and spectroscopic simulations in complex systems at ab initio level. Artificial neural network ; Computer science ; Atom (system on chip) ; Gaussian ; Representation (politics) ; Energy (signal processing) ; Scalar (mathematics) ; Artificial intelligence ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Ankit Agrawal | Alok Choudhary
Source: MRS Communications (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Deep materials informatics: Applications of deep learning in materials science Abstract The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials. Big data ; Deep learning ; Data science ; Informatics ; Software deployment ; Materials informatics ; Analytics ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Health informatics ; Engineering ; Engineering informatics ; Data mining ; Software engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Mostafa M. Abo Elsoud | Ebtsam M. El-Kady
Source: Bulletin of the National Research Centre/Bulletin of the National Research Center (article, 2019-04-05)
Field: Materials Science — Biomaterials

Current trends in fungal biosynthesis of chitin and chitosan Chitin and chitosan are natural biopolymers found in shell of crustaceans, exoskeletons of insects and mollusks, as well as in the cell walls of fungi. These biopolymers have versatile applications in various fields such as biomedical, food industry, and agriculture. These applications are back to their biocompatibility, biodegradability, strong antibacterial effect, and non-toxicity. The fungal biopolymers have many features that made them more advantageous than those biopolymers from seafood waste origin. Chitin and chitosan are not components of cell wall in all fungal species. The fungal classes of Basidiomycetes, Ascomycetes, Zygomycetes, and Deuteromycetes are known to contain chitin and chitosan in their cell walls. The amounts and characters of fungal biopolymers are affected by many factors so they should be optimized before increasing the scale of production. The statistical design of experiments is the most recent and advanced approach for optimization of various factors with more reliable results. Although extensively studied, further studies concerning fungal chitin and chitosan should be conducted in order to be sure of safety for human use. Chitin ; Chitosan ; Cell wall ; Biocompatibility ; Biodegradation ; Biopolymer ; Biology ; Chemistry ; Biotechnology ; Biochemistry ; Organic chemistry ; Polymer Nanocomposite Films for Food Packaging ; Studies on Chitinases and Chitosanases
Authors: Nico Verbeeck | Richard M. Caprioli | Raf Van de Plas
Source: Mass Spectrometry Reviews (review, 2019-10-11)
Field: Chemistry — Spectroscopy

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019. Mass spectrometry imaging ; Chemistry ; Mass spectrometry ; Cluster analysis ; Preprocessor ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Isotope Analysis in Ecology
Authors: Zach Jensen | Edward Kim | Soonhyoung Kwon | Terry Z. H. Gani | Yuriy Román‐Leshkov | Manuel Moliner | Avelino Corma | Elsa Olivetti
Source: ACS Central Science (article, 2019-04-19)
Field: Chemistry — Inorganic Chemistry

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction Zeolites are porous, aluminosilicate materials with many industrial and "green" applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite's framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 Å<sup>3</sup>, and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies. Zeolite ; Heuristics ; Aluminosilicate ; Computer science ; Parsing ; Germanium ; Artificial intelligence ; Algorithm ; Machine learning ; Data mining ; Materials science ; Chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Khadijeh Nekoueian | Mandana Amiri | Mika Sillanpää | Frank Marken | Rabah Boukherroub | Sabine Szunerits
Source: Chemical Society Reviews (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Carbon-based quantum particles: an electroanalytical and biomedical perspective Carbon-based quantum particles, especially spherical carbon quantum dots (CQDs) and nanosheets like graphene quantum dots (GQDs), are an emerging class of quantum dots with unique properties owing to their quantum confinement effect. Many reviews appeared recently in the literature highlighting their optical properties, structures, and applications. These papers cover a broad spectrum of carbon-based nanoparticles, excluding a more detailed discussion about some important aspects related to the definition of carbon-based particles and the correlation of optical and electrochemical aspects in relation to sensing and biomedical applications. A large part of this review is devoted to these aspects. It aims, in particular, to act as a bridge between optical and electrochemical aspects of carbon-based quantum particles, both of which are associated with the electronic nature of carbon-based quantum particles. A special focus will be on their use in electroanalysis, notably their benefits in redox, and in electrochemical analysis with emphasis on their application as sensors. Electroanalysis is an easy and cost-effective means of providing qualitative and quantitative information of a specific analyte in solution in a time scale of some minutes. The integration of carbon-based quantum particles into these detection schemes as well as their incorporation into composite nanomaterials have largely improved detection limits with possibilities for their integration in aspects ranging from point-of-care devices to personalized medicine. This review will focus on some of these aspects while also covering the nanomedical aspects of carbon-based quantum particles, ultimately correlated for such developments. Quantum dot ; Carbon quantum dots ; Graphene ; Carbon fibers ; Quantum ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics ; Composite number ; Composite material Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Peyman Z. Moghadam | Sven M. J. Rogge | Aurelia Li | Chun-Man Chow | Jelle Wieme | Noushin Moharrami | Marta Aragones‐Anglada | G. J. Conduit | Diego A. Gómez‐Gualdrón | Véronique Van Speybroeck | David Fairen‐Jiménez
Source: Matter (article, 2019-05-15)
Field: Chemistry — Inorganic Chemistry

Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning Stability (learning theory) ; Metal-organic framework ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Machine learning ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Leigh Weston | Vahe Tshitoyan | John Dagdelen | Olga Kononova | Amalie Trewartha | Kristin A. Persson | Gerbrand Ceder | Anubhav Jain
Source: Journal of Chemical Information and Modeling (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (<i>f</i><sub>1</sub>) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex "meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data and functionality has been made freely available on our Github ( https://github.com/materialsintelligence/matscholar ) and website ( http://matscholar.com ), and we expect these results to accelerate the pace of future materials science discovery. Computer science ; Bottleneck ; Information retrieval ; Named-entity recognition ; Normalization (sociology) ; Pipeline (software) ; Information extraction ; Classifier (UML) ; Pace ; Data science ; Data mining ; Artificial intelligence ; Task (project management) Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Jonathan Hwang | Zhenxing Feng | Nenian Charles | Xiao Renshaw Wang | Dongkyu Lee | Kelsey A. Stoerzinger | Sokseiha Muy | Reshma R. Rao | Dongwook Lee | Ryan Jacobs | Dane Morgan | Yang Shao‐Horn
Source: Materials Today (article, 2019-04-17)
Field: Materials Science — Materials Chemistry

Tuning perovskite oxides by strain: Electronic structure, properties, and functions in (electro)catalysis and ferroelectricity Materials science ; Ferroelectricity ; Electrocatalyst ; Ionic bonding ; Electronic structure ; Chemical physics ; Oxide ; Perovskite (structure) ; Nanotechnology ; Electrochemistry ; Physical chemistry ; Electrode ; Dielectric ; Chemistry ; Computational chemistry ; Crystallography ; Optoelectronics ; Ion Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Pengfei Gao | M.W. Fu | Mei Zhan | Zhenni Lei | Yanxi Li
Source: Journal of Material Science and Technology (review, 2019-09-26)
Field: Materials Science — Materials Chemistry

Deformation behavior and microstructure evolution of titanium alloys with lamellar microstructure in hot working process: A review Microstructure ; Lamellar structure ; Materials science ; Deformation (meteorology) ; Titanium alloy ; Metallurgy ; Process (computing) ; Hot working ; Composite material ; Alloy ; Computer science Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Advanced Welding Techniques Analysis