jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shaobo Li | Yabo Dan | Li Xiang | Tiantian Hu | Rongzhi Dong | Zhuo Cao | Jianjun Hu
Source: Symmetry (article, 2020-02-08)
Field: Materials Science — Materials Chemistry

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can effectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc. Superconductivity ; Singular value decomposition ; Convolutional neural network ; Benchmark (surveying) ; Artificial neural network ; Representation (politics) ; Matrix (chemical analysis) ; Atom (system on chip) ; Deep learning ; Computer science ; Encoding (memory) ; Algorithm ; Materials science ; Artificial intelligence ; Physics ; Condensed matter physics ; Parallel computing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Vinit Sharma | Pankaj Kumar | Pratibha Dev | Ghanshyam Pilania
Source: Journal of Applied Physics (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Machine learning substitutional defect formation energies in ABO3 perovskites Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. We have developed a framework that itself is general and can be potentially applied to other material classes. Dopant ; Materials science ; Perovskite (structure) ; Ionic bonding ; Nanotechnology ; Stability (learning theory) ; Chemical physics ; Computer science ; Doping ; Chemistry ; Machine learning ; Ion ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Martin Deimel | Karsten Reuter | Mie Andersen
Source: ACS Catalysis (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts Computational screening based on first-principles microkinetic modeling has evolved into a widespread tool for catalyst discovery. Efficiently exploiting various scaling relations, this approach draws its predictive character from reliable adsorption energies, typically calculated with density-functional theory (DFT). In prevalent screening approaches, the concomitant computational costs are kept tractable through the use of reductionist microkinetic models that only resolve a minimalistic amount of active site motifs at the catalyst surface. Here, we scrutinize this common practice by systematically comparing the screening predictions for the CO methanation reaction when using microkinetic models that resolve an increasing amount of sites, up to the full consideration of all high-symmetry sites at stepped transition metal (TM) and binary TM alloy catalysts. Apart from generally overestimating the catalytic activity, the simplified models fail to identify a most promising class of layered bimetallic alloys as their insufficient representation of the catalyst surface does not allow them to correctly capture the rate-determining step. Only the full microkinetic model provides this proper mechanistic basis for the screening. The excessive amount of predictive-quality adsorption energetics required for this model is obtained from a compressed sensing descriptor that once trained readily provides these data for a new material from a single DFT calculation of the clean surface. With the current methodological advances in areas such as compressed sensing and machine learning, and the concurrent availability of cheap adsorption energetics for a wide range of possible catalyst materials, there is thus no reason to continue to use simplistic microkinetic models in computational catalyst screening. Methanation ; Density functional theory ; Catalysis ; Chemistry ; Bimetallic strip ; Biochemical engineering ; Computer science ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Hong‐Kyu Kim | Heon‐Young Ha | Jee‐Hwan Bae | Min Kyung Cho | Ju‐Young Kim | Jeongwoo Han | Jin‐Yoo Suh | Gyeung-Ho Kim | Tae‐Ho Lee | Jae Hoon Jang | Dong Won Chun
Source: Scientific Reports (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Nanoscale light element identification using machine learning aided STEM-EDS Abstract Light element identification is necessary in materials research to obtain detailed insight into various material properties. However, reported techniques, such as scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) have inadequate detection limits, which impairs identification. In this study, we achieved light element identification with nanoscale spatial resolution in a multi-component metal alloy through unsupervised machine learning algorithms of singular value decomposition (SVD) and independent component analysis (ICA). Improvement of the signal-to-noise ratio (SNR) in the STEM-EDS spectrum images was achieved by combining SVD and ICA, leading to the identification of a nanoscale N-depleted region that was not observed in as-measured STEM-EDS. Additionally, the formation of the nanoscale N-depleted region was validated using STEM–electron energy loss spectroscopy and multicomponent diffusional transformation simulation. The enhancement of SNR in STEM-EDS spectrum images by machine learning algorithms can provide an efficient, economical chemical analysis method to identify light elements at the nanoscale. Nanoscopic scale ; Scanning transmission electron microscopy ; Materials science ; Biological system ; Singular value decomposition ; Principal component analysis ; Artificial intelligence ; Computer science ; Nanotechnology ; Transmission electron microscopy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Electron and X-Ray Spectroscopy Techniques
Authors: Yue Huang | Jingtian Zhang | Edwin S. Jiang | Yutaka Oya | Akinori Saeki | Gota Kikugawa | Tomonaga Okabe | Fumio S. Ohuchi
Source: The Journal of Physical Chemistry C (article, 2020-04-24)
Field: Materials Science — Materials Chemistry

Structure–Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach A study workflow that utilizes several data science methods to apply on polymer materials databases is introduced to reveal correlations among their properties, structural information, and molecular descriptors. The data science methods used in this pipeline include the unsupervised machine learning (ML) method of self-organizing mapping (SOM) and the polymer molecular descriptor generator, both of which have been tailored to fit the polymer materials study. To demonstrate how this pipeline can be applied in this context, we used it on an organic photovoltaic (OPV) donor polymer database to investigate which properties or structural factors positively correlate with the power conversion efficiency (PCE) of OPV materials. This led us to discover that among the studied 8 properties and 11 molecular descriptors, only the photon energy loss (Eloss) and the number of fluorine atoms (nF) show strong positive correlations with PCE values, which is consistent with other verified studies. We also discovered that research trends can also be statistically visualized using our method. In our case study, we found that most of the studied OPV donor materials in the database have branched side chains and typically 7–12 non-hydrogen atoms, and high PCE materials usually have 6–9 aromatics rings as well. These results proved that the data science pipeline proposed in this study provides a fast and effective way to obtain research insights for polymer materials. Pipeline (software) ; Photovoltaic system ; Polymer ; Workflow ; Context (archaeology) ; Materials science ; Computer science ; Property (philosophy) ; Nanotechnology ; Database ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Bruno Cuevas-Zuviría | Luis F. Pacios
Source: Journal of Chemical Information and Modeling (article, 2020-08-04)
Field: Materials Science — Materials Chemistry

Analytical Model of Electron Density and Its Machine Learning Inference We present an analytical model representation of the electron density ρ(<b>r</b>) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(<b>r</b>) in atoms, we devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational cost scales linearly with the number of atoms. To obtain the parameters of the model, we first devised a fitting procedure to reference electron densities obtained in <i>ab initio</i> correlated quantum calculations. Second, in order to skip costly <i>ab initio</i> calculations, we also developed a machine learning (ML)-based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(<b>r</b>) suggest potential applications to obtain reliable electron densities and ρ(<b>r</b>)-based molecular properties in biomacromolecules. Electron density ; Statistical physics ; Ab initio ; Representation (politics) ; Basis (linear algebra) ; Electron ; Anisotropy ; Atom (system on chip) ; Gaussian ; Scaling ; Quantum ; Inference ; Physics ; Computer science ; Molecular physics ; Quantum mechanics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Farhad Gharagheizi | Dai Tang | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2020-01-22)
Field: Chemistry — Inorganic Chemistry

Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals Industrial separations of near-azeotropic chemicals, species with very similar boiling points, are energy- and capital-intensive. Adsorption-based processes can energy-efficiently separate near-azeotropic mixtures provided suitable adsorbent materials can be found. Among the full diversity of industry-relevant molecules, millions of these mixtures exist, meaning that discovery of mixture-specific adsorbents by direct experiment is infeasible. We show that vast numbers of adsorbents and adsorbing molecules can be explored in a powerful way by coupling atomistic simulations with machine learning. This concept is demonstrated by describing the adsorption of ∼54 000 industry-relevant chemicals in an experimentally derived set of thousands of metal–organic framework materials. Our results identify thousands of near-azeotropic mixtures that can be efficiently separated using adsorption and open possibilities for creating adsorption processes for complex mixtures with many components. Adsorption ; Process engineering ; Set (abstract data type) ; Boiling ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Zheng Wang | Wei Zhao | Ge‐Fei Hao | Baoan Song
Source: Organic Chemistry Frontiers (article, 2020-08-28)
Field: Materials Science — Materials Chemistry

Mapping the resources and approaches facilitating computer-aided synthesis planning Computer-aided synthesis planning could facilitate organic synthesis study and relieve chemists of manual tasks. Artificial intelligence and deep learning would be useful for the development of computer-aided synthesis planning. Computer-aided ; Computer-aided process planning ; Chemistry ; Artificial intelligence ; Computer science ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Bowen Zheng | Grace X. Gu
Source: Nano-Micro Letters (article, 2020-09-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Detection of Graphene Defects with Atomic Precision Defects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative approach is to observe the thermal vibration properties of the graphene sheet, which reflects defect information but in an implicit fashion. Machine learning, an emerging data-driven approach that offers solutions to learning hidden patterns from complex data, has been extensively applied in material design and discovery problems. In this paper, we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration features. Two prediction strategies are developed: an atom-based method which constructs data by atom indices, and a domain-based method which constructs data by domain discretization. Results show that while the atom-based method is capable of detecting a single-atom vacancy, the domain-based method can detect an unknown number of multiple vacancies up to atomic precision. Both methods can achieve approximately a 90% prediction accuracy on the reserved data for testing, indicating a promising extrapolation into unseen future graphene configurations. The proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries. Graphene ; Extrapolation ; Atom (system on chip) ; Computer science ; Artificial intelligence ; Domain (mathematical analysis) ; Machine learning ; Materials science ; Nanotechnology ; Algorithm ; Vacancy defect ; Physics ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Chiara Fend | Ali Moghiseh | Claudia Redenbach | Katja Schladitz
Source: Journal of Microscopy (article, 2020-07-18)
Field: Materials Science — Surfaces, Coatings and Films

Reconstruction of highly porous structures from FIB‐SEM using a deep neural network trained on synthetic images Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures - realizations of stochastic germ-grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images. Porosity ; Artificial neural network ; Artificial intelligence ; Materials science ; Nanotechnology ; Computer science ; Composite material Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques ; Advancements in Photolithography Techniques
Authors: Nicolae C. Iovanac | Brett M. Savoie
Source: The Journal of Physical Chemistry A (article, 2020-04-08)
Field: Materials Science — Materials Chemistry

Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders Transfer learning is a subfield of machine learning that leverages proficiency in one or more prediction tasks to improve proficiency in a related task. For chemical property prediction, transfer learning models represent a promising approach for addressing the data scarcity limitations of many properties by utilizing potentially abundant data from one or more adjacent applications. Transfer learning models typically utilize a latent variable that is common to several prediction tasks and provides a mechanism for information exchange between tasks. For chemical applications, it is still largely unknown how correlation between the prediction tasks affects performance, the limitations on the number of tasks that can be simultaneously trained in these models before incurring performance degradation, and if transfer learning positively or negatively affects ancillary model properties. Here we investigate these questions using an autoencoder latent space as a latent variable for transfer learning models for predicting properties from the QM9 data set that have been supplemented with semiempirical quantum chemistry calculations. We demonstrate that property prediction can be counterintuitively improved by utilizing a simpler linear predictor model, which has the effect of forcing the latent space to organize linearly with respect to each property. In data scarce prediction tasks, the transfer learning improvement is dramatic, whereas in data rich prediction tasks, there appears to be little adverse impact of transfer learning on prediction performance. The transfer learning approach demonstrated here thus represents a highly advantageous supplement to property prediction models with no downside in implementation. Transfer of learning ; Autoencoder ; Computer science ; Machine learning ; Multi-task learning ; Property (philosophy) ; Artificial intelligence ; Latent variable ; Chemical space ; Set (abstract data type) ; Data set ; Forcing (mathematics) ; Task (project management) ; Deep learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hongxiang Zong | Ping He | Xiangdong Ding | Graeme J. Ackland
Source: Physical review. B./Physical review. B (article, 2020-04-22)
Field: Materials Science — Materials Chemistry

Nucleation mechanism for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>hcp</mml:mi><mml:mo>→</mml:mo><mml:mi>bcc</mml:mi></mml:mrow></mml:math> phase transformation in shock-compressed Zr We present large-scale atomic simulations of shock-induced phase transition in Zr assisted by the machine learning method. The results indicate that there exists a critical piston velocity of ${U}_{\mathrm{p}}\ensuremath{\sim}0.85\phantom{\rule{0.16em}{0ex}}\mathrm{km}/\mathrm{s}$, above which the product phase has changed from \ensuremath{\omega} to bcc. Unlike the case in Fe, the shock-induced $\mathrm{hcp}\ensuremath{\rightarrow}\mathrm{bcc}$ nucleation mechanism in hcp-Zr single-crystal shows significant dependence on crystal orientation. For shock along the $[10\overline{1}0]$ direction, the hcp phase directly transforms into bcc as expected. However, for shock compression along [0001] and $[1\overline{2}10]$ directions, the $\mathrm{hcp}\ensuremath{\rightarrow}\mathrm{bcc}$ transformation occurs in quite a different manner, i.e., the Zr single crystal transforms into a disordered intermediate that subsequently exhibits ultrafast crystallization of the bcc phase within the timescales of subnanoseconds. We associate such presence of disordered intermediate structure with the sluggishness of shear stress relaxation, which leads to an elastic unstable condition of the crystal during the first few picoseconds of uniaxial compression, and suggests that the fewer possible shear planes (related to Burgers mechanism) for [0001] and $[1\overline{2}10]$ shock loading is an underlying factor for the orientation dependence. Nucleation ; Crystallography ; Crystal (programming language) ; Orientation (vector space) ; Materials science ; Physics ; Phase (matter) ; Condensed matter physics ; Relaxation (psychology) ; Thermodynamics ; Geometry ; Quantum mechanics ; Chemistry Nuclear Materials and Properties ; High-pressure geophysics and materials ; Crystallography and molecular interactions