jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nils Zimmermann | Anubhav Jain
Source: ChemRxiv (preprint, 2019-05-03)
Field: Materials Science — Materials Chemistry

Local Structure Order Parameters and Site Fingerprints for Quantification of Coordination Environment and Crystal Structure Similarity Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure. Therefore, reliable and robust procedures to find coordinated neighbors and to evaluate the resulting coordination pattern (e.g., tetrahedral, square planar) are critically important for both traditional and machine learning approaches that aim to exploit site or structure information for predicting materials properties. Here, we introduce new local structure order parameters (LoStOPs) that are specifically designed to rapidly detect rotationally symmetric (e.g., tetrahedron) as well as asymmetric local coordination environments (e.g., square pyramid). Furthermore, we introduce a Monte Carlo optimization approach to ensure that the different LoStOPs are comparable with each other. We then apply the new local environment descriptors to define site and structure fingerprints and to measure similarity between 61 known coordination environments and 40 commonly studied crystal structures, respectively. After extensive testing and optimization, we determine the most accurate structure similarity assessment procedure to compute all 2.45 billion structure similarities between each pair of the ≈ 70,000 materials that are currently present in the Materials Project database. Computer science ; Similarity (geometry) ; Local structure ; Crystal structure ; Data mining ; Monte Carlo method ; Planar ; Tetrahedron ; Pyramid (geometry) ; Algorithm ; Artificial intelligence ; Mathematics ; Chemistry ; Crystallography ; Statistics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Yanan Chen | Chunyong Han | Guochang Shi | Liming Yuan | Xiaojun Ying | Yi Liao
Source:  (article, 2019-06-01)
Field: Materials Science — General Materials Science

A Computational Model for Electromagnetic Characteristics of Anisotropic Composites Based on Machine Learning Carbon fiber reinforced composites applied to aerospace vehicles in a large amount are always provided with inhomogeneity and anisotropy. The existing theoretical calculation model, namely dynamic homogenization model, can only solve the problems of slow calculation and large memory consumption caused by inhomogeneity and anisotropy when the direction of electromagnetic wave electric field is perpendicular to carbon fiber. In order to meet the requirement of high precision extraction of electromagnetic parameters of aerospace composite materials, it is necessary to have a further study on the calculation model of electromagnetic properties of composite materials at arbitrary incident angles of electromagnetic waves. Our work mainly researches the high-precision calculation model of equivalent permittivity of anisotropic composites based on machine learning. Emphasis is laid on the study of the influence of multi-incident angle of electromagnetic wave on electromagnetic properties of anisotropic composites, the high-precision prediction model of electromagnetic parameters based on machine learning, model verification and optimization, etc.. First, the model training database is obtained through data processing based on the Helmholtz equation method; then, the machine learning high-precision calculation model is established by neutral network; finally, the remaining data is utilized into the machine learning model to verify the accuracy of the electromagnetic parameter model of the composite material. The calculation model of composite electromagnetic characteristics based on machine learning has the high-precision prediction ability of electromagnetic parameters of composite materials under arbitrary incident angle of electromagnetic wave. The extracted equivalent permittivity can be applied to electromagnetic properties of airframe structure in the aerospace vehicle modeling, which can be utilized to analyze shielding effectiveness and electromagnetic environment effects of aerospace vehicles. The purpose is to shorten the research and development cycle of aerospace vehicles, reduce the number of experiments and save the research and development funds. Anisotropy ; Electromagnetic radiation ; Electromagnetic field ; Composite number ; Permittivity ; Computer science ; Materials science ; Composite material ; Physics ; Dielectric ; Optics Material Properties and Applications ; Smart Materials for Construction ; Electromagnetic wave absorption materials
Authors: Zheng Chen | Chi Chen | Yiming Chen | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2019-11-04)
Field: Materials Science — Materials Chemistry

Random Forest Models for Accurate Identification of Coordination Environments from X-ray Absorption Near-Edge Structure Analyzing coordination environments using X-ray absorption spectroscopy has broad applications ranging from solid-state physics to material chemistry. Here, we show that random forest models can identify the main coordination environment from K-edge X-ray absorption near edge structure (XANES) with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine learning (ML) models. In a departure from prior works, we used a robust description of the coordination environment as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1-12. The random forest models were trained on the world's largest database of ~190,000 computed K-edge XANES spectra. Furthermore, the random forest models can be applied to predict the coordination environment from experimental K-edge XANES with minimal loss in accuracy (82.1%) due to the use of data augmentation. A drop-out feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification, with the post-peak region becoming increasingly important at higher coordination numbers. This work provides a general strategy to identify the coordination environment from K-edge XANES across broad chemistries, paving the way for future advancements in the application of ML to spectroscopy. XANES ; Coordination number ; Random forest ; Spectroscopy ; Computer science ; Coordination complex ; Absorption (acoustics) ; Enhanced Data Rates for GSM Evolution ; Chemistry ; Artificial intelligence ; Physics ; Optics ; Ion ; Metal Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Ayana Ghosh | F. Ronning | Serge Nakhmanson | Jian‐Xin Zhu
Source: arXiv (Cornell University) (preprint, 2019-07-24)
Field: Materials Science — Materials Chemistry

Understanding Magnetic Properties of Actinide-Based Compounds from Machine Learning Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f-electrons and include unconventional superconductivity and magnetism, hidden order; and heavy fermion behavior. Due to the strongly correlated nature of the 5f electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput DFT simulations and experimental measurements for the families of uranium and neptunium based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation f-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the Random Forest Regression algorithm and can predict magnetic moment sizes and ordering forms in actinide-based systems with 10-20% of root mean square error. Magnetism ; Actinide ; Magnetic moment ; Neptunium ; Electron ; Condensed matter physics ; Superconductivity ; Materials science ; Chemistry ; Physics ; Nuclear physics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Inorganic Chemistry and Materials
Authors: Sheng Gong | Wei Wu | Fancy Qian Wang | Jie Liu | Yu Zhao | Yiheng Shen | Shuo Wang | Qiang Sun | Qian Wang
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Classifying superheavy elements by machine learning Among the 118 elements listed in the periodic table, there are nine superheavy elements (Mt, Ds, Mc, Rg, Nh, Fl, Lv, Ts, and Og) that have not yet been well studied experimentally because of their limited half-lives and production rates. How to classify these elements for further study remains an open question. For superheavy elements, although relativistic quantum-mechanical calculations for the single atoms are more accurate and reliable than those for their molecules and crystals, there is no study reported to classify elements solely based on atomic properties. By using cutting-edge machine learning techniques, we find the relationship between atomic data and classification of elements, and further identify that Mt, Ds, Mc, Rg, Lv, Ts, and Og should be metals, while Nh and Fl should be metalloids. These findings not only highlight the significance of machine learning for superheavy atoms but also challenge the conventional belief that one can determine the characteristics of an element only by looking at its position in the table. Superheavy Elements ; Computer science ; Physics ; Nuclear physics X-ray Diffraction in Crystallography ; Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications
Authors: P. Radha | N. Selvakumar | Ronald S. Harichandran
Source: Archives of Metallurgy and Materials (article, 2019-09-03)
Field: Materials Science — General Materials Science

Computational Intelligence for Analysing the Mechanical Properties of AA 2219 – (B4C + h-BN) Hybrid Nano Composites Processed by Ultrasound Assisted Casting The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro / nano composites with the inclusion of B4C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model. Materials science ; Nano- ; Casting ; Composite material ; Industrial chemistry ; Materials processing ; Ultrasound ; Acoustics ; Manufacturing engineering ; Engineering Material Properties and Applications ; Surface Treatment and Coatings ; Additive Manufacturing and 3D Printing Technologies
Authors: Alexander W. Kohn | Zhou Lin | Troy Van Voorhis
Source: ChemRxiv (preprint, 2019-02-08)
Field: Chemistry — Physical and Theoretical Chemistry

Towards Prediction of Non-Radiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields Many emerging technologies depend on human’s ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescentlabeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from non-radiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from first principles because the description of a slow non-radiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semiempirical method of calculating the fluorescence quantum yield (Φfl) for molecular chromophores, which, based on machine learning, converts simple electronic energies computed using time-dependent density functional theory (TDDFT) into an estimate of Φfl. As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φfl’s are known, so as to provide a black-box method which can later predict Φfl’s for chemically similar chromophores that have not been studied experimentally. As a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC) – the electronic properties of at least one higher-lying electronic state (Sn or Tn) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum a distorted local minimum near an S0/S1 conical intersection (which governs IC) and the magnitude of the spin–orbit coupling (which governs ISC). The resulting Φfl’s are predicted with reasonable accuracy (±22%), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing non-radiative relaxations. Intersystem crossing ; Excited state ; Chromophore ; Quantum yield ; Internal conversion ; Quantum ; Time-dependent density functional theory ; Density functional theory ; Radiative transfer ; Electroluminescence ; Chemistry ; Physics ; Chemical physics ; Computer science ; Materials science ; Fluorescence ; Atomic physics ; Quantum mechanics ; Singlet state ; Nanotechnology ; Electron Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Luminescence and Fluorescent Materials
Authors: Shaomin Yan | Guang Wu
Source: Journal of Biomedical Science and Engineering (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Correlation of Combined Characters of Amino Acid and Whole Protein with Success Rate of Crystallization of &amp;lt;i&amp;gt;Lactobacillus&amp;lt;/i&amp;gt; Proteins Crystallization of proteins is a very delicate process, which is influenced by many known and unknown factors. Of tested factors, many factors are exclusively related to individual amino-acid characters such as molecular weight or protein characters such as protein length. It is considered necessary to test factors that combine both individual amino-acid characters and protein characters with respect to success rate of crystallization. In this study, two combined characters characterizing individual amino-acid character and protein character, amino acid distribution probability and future composition, were used to correlate the success rate of crystallization of proteins from Lactobacillus via modeling. The results obtained from logistic regression and neural network were compared against the results obtained from each of 533 individual amino-acid characters. This study demonstrated that the combined characters are involved in crystallization process and should be taken into account for predicting the success rate of crystallization process. Crystallization ; Amino acid ; Character (mathematics) ; Protein crystallization ; Biology ; Lactobacillus ; Chemistry ; Biochemistry ; Bacteria ; Mathematics ; Genetics Enzyme Structure and Function ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics
Authors: Yoolhee Kim | Edward Kim | Erin Antono | Bryce Meredig | Julia Ling
Source: arXiv (Cornell University) (preprint, 2019-11-25)
Field: Materials Science — Materials Chemistry

Machine-learned metrics for predicting the likelihood of success in materials discovery Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a critical question: Are we searching in the right haystack? We refer to the haystack as the design space for a particular materials discovery problem (i.e. the set of possible candidate materials to synthesize), and thus frame this question as one of design space selection. In this paper, we introduce two metrics, the Predicted Fraction of Improved Candidates (PFIC), and the Cumulative Maximum Likelihood of Improvement (CMLI), which we demonstrate can identify discovery-rich and discovery-poor design spaces, respectively. Using CMLI and PFIC together to identify optimal design spaces can significantly accelerate ML-driven materials discovery. Haystack ; Computer science ; Set (abstract data type) ; Selection (genetic algorithm) ; Space (punctuation) ; Machine learning ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Roman A. Eremin | Pavel N. Zolotarev | Andrey A. Golov | Nadezhda A. Nekrasova | Tilmann Leisegang
Source: The Journal of Physical Chemistry (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Ionic Transport in Doped Solid Electrolytes by Means of DFT Modeling and ML Approaches: A Case Study of Ti-Doped KFeO₂ We present a comprehensive study on the influence of Ti doping on K⁺ migration in the K₁–ₓFe₁–ₓTiₓO₂ solid electrolyte. A novel approach is proposed which is based on modeling of configurational spaces (CSs) and full sets of inequivalent migration pathways by means of density functional theory (DFT) calculations and machine learning (ML) techniques. A 2 × 1 × 1 supercell (32 formula units) of a low-temperature polymorph of the KFeO₂ compound with space group symmetry Pbca was used. For the three lowest Ti contents (x = 0.03, 0.06, and 0.09), all symmetrically inequivalent configurations of atomic arrangements (CSs) and K⁺ migration pathways (total numbers: 128, 59520, and 8630400) were generated. With the DFT-derived energetics of K⁺ migration at the lowest doping level (x = 0.03), the ML models were trained to predict ionic transport properties by using geometrical descriptors for the pathway-dopant arrangement. The trained ML models were then used to evaluate the K⁺ migration properties for pathways at higher doping levels. The computational results obtained are in good agreement with the results of a previous experimental study of the title compound. This demonstrates the applicability of the proposed approach for modeling and predicting effects of doping in crystalline solids, such as solid electrolytes and intercalation cathodes. Brief recommendations are given on the application of the proposed combined approach. Chemistry ; Density functional theory ; Doping ; Dopant ; Ionic bonding ; Supercell ; Electrolyte ; Intercalation (chemistry) ; Computational chemistry ; Crystallography ; Thermodynamics ; Ion ; Chemical physics ; Physical chemistry ; Inorganic chemistry ; Condensed matter physics ; Physics ; Organic chemistry ; Electrode Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Battery Materials and Technologies
Authors: Dror Fixler | Chen Tzur | Zeev Zalevsky
Source: Materials (article, 2019-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Genetic Algorithm-Based Design for Metal-Enhanced Fluorescent Nanostructures In this paper, we present our optimization tool for fluorophore-conjugated metal nanostructures for the purpose of designing novel contrast agents for multimodal bioimaging. Contrast agents are of great importance to biological imaging. They usually include nanoelements causing a reduction in the need for harmful materials and improvement in the quality of the captured images. Thus, smart design tools that are based on evolutionary algorithms and machine learning definitely provide a technological leap in the fluorescence bioimaging world. This article proposes the usage of properly designed metallic structures that change their fluorescence properties when the dye molecules and the plasmonic nanoparticles interact. The nanostructures design and evaluation processes are based upon genetic algorithms, and they result in an optimal separation distance, orientation angles, and aspect ratio of the metal nanostructure. Nanostructure ; Nanotechnology ; Fluorescence ; Fluorophore ; Materials science ; Computer science ; Plasmon ; Conjugated system ; Genetic algorithm ; Algorithm ; Machine learning ; Polymer Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics