jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Steven Kauwe | Jake Graser | Ryan Murdock | Taylor D. Sparks
Source: ChemRxiv (preprint, 2019-08-09)
Field: Materials Science — Materials Chemistry

Can Machine Learning Find Extraordinary Materials? &lt;p&gt;One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3/4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach. Finally, we discuss cautions and potential limitations in implementing such an approach to discover new record-breaking materials.&lt;/p&gt; Machine learning ; Artificial intelligence ; Debye model ; Set (abstract data type) ; Shear modulus ; Computer science ; False positive paradox ; Regression ; Meaning (existential) ; Support vector machine ; Mathematics ; Physics ; Statistics ; Thermodynamics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: John E. Herr | Kevin J. Koh | Kun Yao | John Parkhill
Source: The Journal of Chemical Physics (article, 2019-08-22)
Field: Materials Science — Materials Chemistry

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences We define a vector quantity which corresponds to atomic species identity by compressing a set of physical properties with an autoencoder. This vector, referred to here as the elemental modes, provides many advantages in downstream machine learning tasks. Using the elemental modes directly as the feature vector, we trained a neural network to predict formation energies of elpasolites with improved accuracy over previous works on the same task. Combining the elemental modes with geometric features used in high-dimensional neural network potentials (HD-NNPs) solves many problems of scaling and efficiency in the development of such neural network potentials. Whereas similar models in the past have been limited to typically four atomic species (H, C, N, and O), our implementation does not scale in cost by adding more atomic species and allows us to train an HD-NNP model which treats molecules containing H, C, N, O, F, P, S, Cl, Se, Br, and I. Finally, we establish that our implementation allows us to define feature vectors for alchemical intermediate states in the HD-NNP model, which opens up new possibilities for performing alchemical free energy calculations on systems where bond breaking/forming is important. Representation (politics) ; Chemical space ; Autoencoder ; Computer science ; Space (punctuation) ; Artificial intelligence ; Table (database) ; Feature (linguistics) ; Machine learning ; Popularity ; Theoretical computer science ; Chemistry ; Data mining ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Keisuke Suzuki | Takashi Toyao | Zen Maeno | Satoru Takakusagi | Ken‐ichi Shimizu | Ichigaku Takigawa
Source: ChemCatChem (article, 2019-09-12)
Field: Materials Science — Materials Chemistry

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data Abstract The front cover artwork for Issue 18/2019 is provided by the groups Ken‐ichi Shimizu group at Hokkaido University (Japan) and Ichigaku Takigawa at RIKEN (Japan). The image shows future catalysis research using artificial intelligence (AI). See the Full Paper itself at https://doi.org/10.1002/cctc.201900971 . Statistical analysis ; Front cover ; Cover (algebra) ; Statistical learning ; Data science ; Computer science ; Artificial intelligence ; Engineering ; Statistics ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Expert finding and Q&A systems
Authors: Hassan A. Tahini | Xin Tan | Sean C. Smith
Source: Advanced Theory and Simulations (article, 2019-01-25)
Field: Chemistry — Organic Chemistry

Unraveling the Factors Behind the Efficiency of Hydrogen Evolution in Endohedrally Doped C<sub>60</sub> Structures via Ab Initio Calculations and Insights from Machine Learning Models Abstract Understanding the origins of catalytic activity (or inactivity) in nanostructures allows for the rational design of cheap and durable catalysts. Here, consistent and comprehensive ab initio screening of endohedrally doped fullerenes as potential catalysts for hydrogen evolution reactions is performed. By examining variations in the electronic structure of the carbon atoms in the presence of the dopant, and by relying on machine learning algorithms, the origin of enhanced activity in fullerenes can be underpinned. The effect is attributed to the formation of free radicals by weakening the C─C double bonds. A number of electronic descriptors are discussed which can be fed into machine learning models to efficiently and reliably predict catalytic activities. This allows for a generalization of trends and a predictive ability that could be applied to other fullerene structures. Fullerene ; Ab initio ; Dopant ; Computational chemistry ; Catalysis ; Electronic structure ; Chemical physics ; Materials science ; Ab initio quantum chemistry methods ; Doping ; Machine learning ; Nanotechnology ; Chemistry ; Computer science ; Molecule ; Organic chemistry Fullerene Chemistry and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Claudia Merola | Hsiu‐Wei Cheng | Dominik Dworschak | Ching‐Shun Ku | Ching‐Yu Chiang | Frank Uwe Renner | Markus Valtiner
Source: Advanced Materials Interfaces (article, 2019-04-25)
Field: Materials Science — Materials Chemistry

Nanometer Resolved Real Time Visualization of Acidification and Material Breakdown in Confinement Abstract Localized surface reactions in confinement are inherently difficult to visualize in real‐time. Herein multiple‐beam‐interferometry (MBI) is extended as a real‐time monitoring tool for corrosion of nanometer confined bulk metallic surfaces. The capabilities of MBI are demonstrated, and the initial crevice corrosion mechanism on confined nickel and a Ni 75 Cr 16 Fe 9 model material is compared. The initiation of crevice corrosion is visualized in real time during linear sweep polarization in a 1 × 10 −3 m NaCl solution. Pre‐ and post‐experiment analysis is performed to complementarily characterize the degraded area with atomic force microscopy (AFM), optical microscopy, nano‐Laue diffraction (nano‐LD), scanning electron microscopy (SEM)/electron backscatter diffraction (EBSD), and X‐ray photoelectron spectroscopy (XPS). Overall, Ni 75 Cr 16 Fe 9 displays a better corrosion resistance; however, MBI imaging reveals 200 nm deep severe localized corrosion of the alloy in the crevice opening. Chromium rich passive films formed on the alloy contribute to accelerated corrosion of the confined alloy by a strongly acidifying dissolution of the passive film in the crevice opening. Nano‐LD further reveals preferential crystallographic defect and corrosion migration planes during corrosion. MBI provides nanometer accurate characterization of topologies and degradation in confined spaces. The technique enables the understanding of the initial crevice corrosion mechanism and testing modeling approaches and machine‐learning algorithms. Materials science ; Corrosion ; Crevice corrosion ; X-ray photoelectron spectroscopy ; Electron backscatter diffraction ; Scanning electron microscope ; Alloy ; Diffraction ; Nanometre ; Nanoscopic scale ; Metallurgy ; Composite material ; Chemical engineering ; Optics ; Nanotechnology ; Microstructure Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Force Microscopy Techniques and Applications
Authors: Hanine Hadni | Mohamed Mazigh | Menana El Hallaoui
Source: Mediterranean Journal of Chemistry (article, 2019-04-04)
Field: Chemistry — Organic Chemistry

QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors A quantitative structure-activity relationship (QSAR) investigation was performed towards 41 hybrids of 4-anilinoquinoline-triazines as potential antimalarial agents. The study was carried out using descendant multiple linear regression analyses (MLR), and artificial neural networks (ANN). Quantum chemical descriptors were calculated using DFT-B3LYP method, with the basis set 6-31G. The values obtained for the correlation coefficient of 0.87 and 0.92 by MLR and ANN, respectively, show a good predictive quality of the established model. In addition, the predicted model has been confirmed by several validation methods such as leave-one-out (LOO) cross-validation, Y-randomization, and external validation. The observed activity and the structural features of the studied molecules were further highlighted by molecular docking study on both wild and quadruple mutant type of pf-DHFR protein. Furthermore, the present work deals to study the binding modes and the key protein-ligand interactions. This methodology will be used to design new antimalarial drugs. Chemistry ; Loo ; Quantitative structure–activity relationship ; Triazine ; Docking (animal) ; Cross-validation ; Molecular descriptor ; Quantum chemical ; Linear regression ; Computational chemistry ; Stereochemistry ; Molecule ; Computational biology ; Artificial intelligence ; Machine learning ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications ; Synthesis and Reactivity of Heterocycles
Authors: Timur Gimadiev | Olga Klimchuk | Ramil Nugmanov | Timur Madzhidov | Alexandre Varnek
Source: Journal of Molecular Structure (article, 2019-08-06)
Field: Chemistry — Organic Chemistry

Sydnone-alkyne cycloaddition: Which factors are responsible for reaction rate ? Sydnone ; Cycloaddition ; Alkyne ; Chemistry ; Computational chemistry ; Reaction mechanism ; Reaction rate ; Organic chemistry ; Catalysis Click Chemistry and Applications ; Molecular Junctions and Nanostructures ; Catalytic Alkyne Reactions
Authors: Jiteng Wan | Chunji Jin | Banghai Liu | Zonglian She | Mengchun Gao | Zhengyang Wang
Source: Journal of the Serbian Chemical Society (article, 2019-01-01)
Field: Chemistry — Electrochemistry

Electrochemical oxidation of sulfamethoxazole using Ti/SnO2-Sb/Co-PbO2 electrode through ANN-PSO Even in a trace amounts, the presence of antibiotics in aqueous solution is getting more and more attention. Accordingly, appropriate technologies are needed to efficiently remove these compounds from aqueous environments. In this study, we have examined the electrochemical oxidation (EO) of sulfamethoxazole (SMX) on a Co modified PbO2 electrode. The process of EO of SMX in aqueous solution followed the pseudo-first-order kinetics, and the removal efficiency of SMX reached the maximum value of 95.1 % within 60 min. The effects of major factors on SMX oxidation kinetics were studied in detail by single-factor experiments, namely current density (1?20 mA cm-2), solution pH value (2?10), initial concentration of SMX (10?500 mg L-1) and concentration of electrolytes (0.05?0.4 mol L-1). An artificial neural network (ANN) model was used to simulate this EO process. Based on the obtained model, particle swarm optimization (PSO) was used to optimize the operating parameters. The maximum removal efficiency of SMX was obtained at the optimized conditions (e.g., current density of 12.37 mA cm-2, initial pH value of 4.78, initial SMX concentration of 74.45 mg L-1, electrolyte concentration of 0.24 mol L-1 and electrolysis time of 51.49 min). The validation results indicated that this method can ideally be used to optimize the related parameters and predict the anticipated results with acceptable accuracy. Electrolysis ; Aqueous solution ; Electrochemistry ; Electrolyte ; Chemistry ; Electrode ; Kinetics ; Supporting electrolyte ; Current density ; Nuclear chemistry ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Physical chemistry Electrochemical Analysis and Applications ; Advanced oxidation water treatment ; Advanced Photocatalysis Techniques
Authors: Félix Musil | Michele Ceriotti
Source: CHIMIA International Journal for Chemistry (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning at the Atomic Scale Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condensed-phase systems. This short review summarizes recent progress in the field, focusing in particular on the problem of representing an atomic configuration in a mathematically robust and computationally efficient way. We also discuss some of the regression algorithms that have been used to construct surrogate models of atomic-scale properties. We then show examples of how the optimization of the machine-learning models can both incorporate and reveal insights onto the physical phenomena that underlie structure-property relations. Atomic units ; Computer science ; Construct (python library) ; Scale (ratio) ; Field (mathematics) ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Eugene J. Ragasa | Chris John O'Brien | Richard G. Hennig | Stephen M. Foiles | Simon R. Phillpot
Source: Modelling and Simulation in Materials Science and Engineering (article, 2019-06-11)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of interatomic potentials with application to MgO The parameterization of a functional form for an interatomic potential is treated as a problem in multi-objective optimization. An autonomous, machine-learning approach based on the identification of the Pareto hypersurface of errors in predicted properties allows the development of an ensemble of parameterizations with high materials fidelity and robustness. The efficacy of this approach is illustrated for the simple example of a Buckingham potential for MgO. This approach also provides a strong foundation for uncertainty quantification of potential parameterizations. Materials science ; Interatomic potential ; Computational chemistry ; Molecular dynamics X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; Nuclear Materials and Properties
Authors: Fadoua Khmaissia | Hichem Frigui | Antonis N. Andriotis | Madhu Menon
Source: Journal of Physics Condensed Matter (article, 2019-07-13)
Field: Materials Science — Materials Chemistry

Data driven modeling of magnetism in dilute magnetic semiconductors: correlation between the magnetic features of diluted magnetic semiconductors and electronic properties of the constituent atoms We propose an efficient machine learning based approach in modeling the magnetism of diluted magnetic semiconductors (DMSs) leading to the prediction of new compounds with enhanced magnetic properties. The approach combines accurate ab initio methods with statistical tools to uncover the correlation between the magnetic features of DMSs and electronic properties of the constituent atoms to determine the underlying factors responsible for the DMS-magnetism. Taking the electronic properties of different DMS systems as descriptors to train different regression models allows us to achieve a speed up of several orders of magnitude in the search for an optimum combination of the host semiconductor and the dopants with enhanced magnetic properties. We demonstrate this by analyzing a large set of descriptors for a wide range of systems and show that only 30% of these features are more likely to contribute to this property. We also show that training regression models with the reduced set of features to predict the total magnetic moment of new candidate DMSs has reduced the mean square error by about 20% compared to the models trained using the whole set of features. Furthermore, our results indicate that the predictive power of our method can be improved even more by extending our descriptor set. Magnetism ; Magnetic semiconductor ; Condensed matter physics ; Semiconductor ; Materials science ; Physics ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yu‐Xuan Wang | Hsin‐An Chen | Chun‐Wei Pao | Chien-Cheng Chang
Source: Multiscale Science and Engineering (article, 2019-02-11)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Model for Atomistic Simulations of $${\rm {Sb/MoS}_{2}}$$ Sb / MoS 2 van der Waals Heterostructures van der Waals force ; Density functional theory ; Heterojunction ; Molecular dynamics ; Artificial neural network ; Work (physics) ; Materials science ; Statistical physics ; Electronic structure ; Computer science ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Molecule ; Quantum mechanics ; Optoelectronics 2D Materials and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Can Eyüpoğlu | Şeyda Eyüpoğlu | Nigar Merdan
Source: Journal of Testing and Evaluation (article, 2019-01-29)
Field: Materials Science — Polymers and Plastics

Improvement of Thermal Insulation Properties of Polyester Nonwoven and Estimation of Thermal Conductivity Coefficients Using Artificial Neural Network Abstract In this study, polyester, i.e., Poly(ethylene terephthalate) (PET) nonwoven fabric, was coated with white tuff, perlite, and volcanic stone powder at rates of 10, 20, 30 and 40 % so as to increase the thermal insulation properties of PET nonwoven fabric. In order to apply white tuff, perlite, and volcanic stone powder to PET nonwoven fabric, polyurethane-based coating material was used as a cross-linking agent. The porosity and thermal conductivity coefficients of samples were then investigated as regards the type and concentration of stone powder. Furthermore, three-layer feed-forward artificial neural network (3FFNN) was used to estimate and verify the accuracy of the thermal conductivity coefficient of PET nonwovens coated with white tuff, perlite, and volcanic stone powder. The results showed that perlite stone powder provided higher thermal insulation compared to white tuff and volcanic stone powder. Moreover, thermal insulation coefficient of samples increased with the rise in concentration of white tuff, perlite, and volcanic stone powder. Besides, the accuracy of 3FFNN was 99 %. Artificial neural network (ANN)-based results showed that the thermal conductivity coefficients of samples with four different concentrations obtained from white tuff, perlite, and volcanic stone powder were almost the same for experimental and ANN-trained models. According to the results, it was seen that 3FFNN was correctly modeled, and the prediction of the thermal conductivity coefficients was successfully realized. Perlite ; Materials science ; Thermal conductivity ; Composite material ; Polyester ; Porosity ; Thermal insulation ; Mineralogy ; Layer (electronics) ; Geology Textile materials and evaluations ; Building Energy and Comfort Optimization ; Phase Change Materials Research
Authors: Ahmed Abdul Baseer | D.V. Ravi Shankar | M. Manzoor Hussain
Source: Surface Review and Letters (article, 2019-04-04)
Field: Materials Science — Polymers and Plastics

INTERFACIAL AND TENSILE PROPERTIES OF HYBRID FRP COMPOSITES USING DNN STRUCTURE WITH OPTIMIZATION MODEL Fiber reinforced polymer (FRP) composites are appealing for use in structural building applications because of their high strength-to-weight and stiffness-to-weight proportions, corrosion resistance, lightweight, possibly high durability, along with free design characteristics. The aim of this research work was to develop high strength natural fiber-based composite plates for the possible application in the shear strengthening of the reinforced concrete structure. In the experimental modeling, the composites were fabricated using glass, flax and kenaf fibers in treated and untreated conditions. This paper studied and analyzed the interfacial and tensile properties of fiber reinforced hybrid composites such as flax/glass and kenaf/glass by using the simulation approach, i.e. Deep Neural Network (DNN) with weight optimization. For optimizing the weights in DNN, Oppositional based FireFly Optimization (OFFO) is proposed. All the optimal results exhibit in the way that the accomplished error values between the output of the experimental values and the predicted qualities are firmly equivalent to zero in the designed system. Kenaf ; Materials science ; Composite material ; Ultimate tensile strength ; Fibre-reinforced plastic ; Composite number ; Glass fiber ; Specific strength ; Stiffness ; Fiber ; Natural fiber ; Durability ; Structural engineering Natural Fiber Reinforced Composites ; Structural Behavior of Reinforced Concrete ; Innovative concrete reinforcement materials
Authors: Diego Guedes‐Sobrinho | Weiqi Wang | Ian Hamilton | Juarez L. F. Da Silva | Luca M. Ghiringhelli
Source: The Journal of Physical Chemistry Letters (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

(Meta-)stability and Core–Shell Dynamics of Gold Nanoclusters at Finite Temperature Gold nanoclusters have been the focus of numerous computational studies, but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core-shell morphology is observed. We analyze their structure and dynamics using state-of-the-art techniques, including unsupervised machine-learning nonlinear dimensionality reduction (sketch-map) for describing the similarities and differences among the range of sampled configurations. In the examined temperature range between 300 and 600 K, we find that whereas the gold nanoclusters exhibit continuous structural rearrangement, they are not amorphous. Instead, they clearly show persistent motifs: a cationic core of 1-5 atoms is loosely bound to a shell which typically displays a substructure resulting from the competition between locally spherical versus planar fragments. Besides illuminating the properties of core-shell gold nanoclusters, the present study proposes a set of useful tools for understanding their nature in operando. Nanoclusters ; Shell (structure) ; Stability (learning theory) ; Core (optical fiber) ; Materials science ; Nanotechnology ; Composite material ; Computer science Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Theoretical and Computational Physics