jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anton Vladyka | Tim Albrecht
Source: arXiv (Cornell University) (preprint, 2020-04-02)
Field: Materials Science — Materials Chemistry

Unsupervised Classification of Single-Molecule Data with Autoencoders\n and Transfer Learning Datasets from single-molecule experiments often reflect a large variety of\nmolecular behaviour. The exploration of such datasets can be challenging,\nespecially if knowledge about the data is limited and a priori assumptions\nabout expected data characteristics are to be avoided. Indeed, searching for\npre-defined signal characteristics is sometimes useful, but it can also lead to\ninformation loss and the introduction of expectation bias. Here, we demonstrate\nhow Transfer Learning-enhanced dimensionality reduction can be employed to\nidentify and quantify hidden features in single-molecule charge transport data,\nin an unsupervised manner. Taking advantage of open-access neural networks\ntrained on millions of seemingly unrelated image data, our results also show\nhow Deep Learning methodologies can readily be employed, even if the amount of\nproblem-specific, 'own' data is limited.\n Computer science ; Transfer of learning ; Artificial intelligence ; Dimensionality reduction ; Machine learning ; Unsupervised learning ; A priori and a posteriori ; Curse of dimensionality ; Variety (cybernetics) ; Artificial neural network ; Pattern recognition (psychology) Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Alina Mreńca-Kolasińska | K. Kolasiński | B. Szafran
Source: Physical review. B./Physical review. B (article, 2020-08-11)
Field: Materials Science — Materials Chemistry

Fast evaluation of interaction integrals for confined systems with machine learning The calculation of interaction integrals is a bottleneck for the treatment of many-body quantum systems due to its high numerical cost. We conduct configuration interaction calculations of the few-electron states confined in III-V semiconductor two-dimensional structures using a shallow neural network to calculate the two-electron integrals, which can be used for general isotropic interaction potentials. This approach allows for a speedup of the evaluation of the energy levels and controllable accuracy. Bottleneck ; Isotropy ; Electron ; Quantum ; Artificial neural network ; Energy (signal processing) ; Computer science ; Semiconductor ; Statistical physics ; Physics ; Quantum mechanics ; Artificial intelligence ; Embedded system Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques
Authors: Tsubasa Hori | Kazuyoshi Ogasawara
Source: IOP Conference Series Materials Science and Engineering (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Prediction of emission energy of Cr<sup>3+</sup> in oxides based on first-principles calculations and machine learning Abstract The first-principles calculations of the energies of the emission level ( 2 E g ) of Cr 3+ in 10 oxide crystals were performed using the first-principles configuration-interaction calculations. In order to improve the accuracy of the prediction, a machine learning model was created by using the results of the first-principles calculations and the experimental 2 E g energies as the training data. The predicted values using this model showed good correlation with the experimental values, where the correlation coefficient is 0.92. The obtained predicted model indicated that the Cr 3d component of t 2g molecular orbital is the most important quantity for prediction of the 2 E g energy. Correlation coefficient ; Energy (signal processing) ; Experimental data ; Component (thermodynamics) ; Oxide ; Correlation ; Materials science ; Computational physics ; Computer science ; Physics ; Thermodynamics ; Machine learning ; Mathematics ; Statistics ; Quantum mechanics ; Geometry Luminescence Properties of Advanced Materials ; X-ray Diffraction in Crystallography ; Inorganic Fluorides and Related Compounds
Authors: Raluca Maria Aileni | Laura Chiriac | Lilioara Surdu
Source: IOP Conference Series Materials Science and Engineering (article, 2020-04-01)
Field: Materials Science — Polymers and Plastics

The predictive development of the electroconductive textile using artificial neural network Abstract This paper presents several aspects concerning the predictive development of the electroconductive textiles based on the artificial neural network. The electroconductive textiles have been obtained using thin film coating with metal micro/nanoparticles and printing. For these textiles, we have made a mathematical model and we optimized dependent parameters, such as pH, dispersion conductivity, mass, thickness, air permeability, and surface resistance, by neural networks based on optimization algorithm Levenberg-Marquardt back propagation. The methodology consists in developing three feed-forward neural networks that are capable of estimating the surface conductivity of the textile materials, based on six parameters obtained in the laboratory. Moreover, this work is significant in the development of surfaces with conductivity directed to obtain the antistatic, electroconductive textiles or insulators, starting from input matrix values. Antistatic agent ; Artificial neural network ; Textile ; Materials science ; Coating ; Conductivity ; Composite material ; Computer science ; Artificial intelligence Textile materials and evaluations ; Color perception and design ; Color Science and Applications
Source: IOP Conference Series Materials Science and Engineering (article, 2020-01-01)
Field: Materials Science — General Materials Science

Preface 2019 Workshop on Materials and Engineering in Aeronautics (MEA2019) was held 16-17 October 2019 in Moscow Aviation Institute (National Research University). This Workshop was held 15 times from 2001 to 2018. Each was attended by over 20 participants who submitted their reports for publishing. The participants represented mostly the leading technical universities of Russia. The Scientific Committee hopes that the international involvement would increase. The (MEA2019) Workshop Proceedings include 28 papers but the scope is actually huge. It covers materials &amp; structure development, aircraft and airships design, pilot interface, aircraft parameter identification, system theory, control, aircraft engines technical state monitoring, robots, statistical classification. This year we include more papers dealing with deep learning and recurrent neural networks applications. In the year 2019 the Scientific Committee’s confirms its traditional position not to limit the creativity of the participants. As a result all the papers are unlike one another and many of them contain original ideas and approaches. That is why our advice to a reader is not to judge the Proceedings by one or two papers but look through the most part of them. On behalf of the Workshop Scientific Committee the scientific editors of MEA2019 Proceedings: Evgeny V. Nikulchev, Dr., Professor of MIREA – Russian Technological University, nikulchev@mail.ru Oleg N. Korsun, Dr., Prof., Head of the laboratory, State Research Institute of Aviation Systems, marmottoduo@yandex.ru Library science ; Scope (computer science) ; Aviation ; Identification (biology) ; State (computer science) ; Engineering ; Engineering ethics ; Operations research ; Aeronautics ; Political science ; Computer science Material Properties and Applications
Authors: Fang Liu | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-07-27)
Field: Materials Science — Materials Chemistry

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3d transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. Character (mathematics) ; HOMO/LUMO ; Density functional theory ; Computer science ; Correlation ; Chemical space ; Artificial intelligence ; Chemical shift ; Machine learning ; Materials science ; Physics ; Statistical physics ; Chemistry ; Mathematics ; Computational chemistry ; Drug discovery ; Quantum mechanics ; Molecule ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Fabian L. Thiemann | Patrick J. Rowe | Erich A. Müller | Angelos Michaelides
Source: arXiv (Cornell University) (preprint, 2020-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Potential for Hexagonal Boron Nitride Applied to\n Thermally and Mechanically Induced Rippling We introduce an interatomic\npotential for hexagonal boron nitride\n(hBN) based on the Gaussian approximation potential (GAP) machine\nlearning methodology. The potential is based on a training set of\nconfigurations collected from density functional theory (DFT) simulations\nand is capable of treating bulk and multilayer hBN as well as nanotubes\nof arbitrary chirality. The developed force field faithfully reproduces\nthe potential energy surface predicted by DFT while improving the\nefficiency by several orders of magnitude. We test our potential by\ncomparing formation energies, geometrical properties, phonon dispersion\nspectra, and mechanical properties with respect to benchmark DFT calculations\nand experiments. In addition, we use our model and a recently developed\ngraphene-GAP to analyze and compare thermally and mechanically induced\nrippling in large scale two-dimensional (2D) hBN and graphene. Both\nmaterials show almost identical scaling behavior with an exponent\nof η ≈ 0.85 for the height fluctuations agreeing well\nwith the theory of flexible membranes. On the basis of its lower resistance\nto bending, however, hBN experiences slightly larger out-of-plane\ndeviations both at zero and finite applied external strain. Upon compression,\na phase transition from incoherent ripple motion to soliton-ripples\nis observed for both materials. Our potential is freely available\nonline at [http://www.libatoms.org]. Materials science ; Graphene ; Phonon ; Density functional theory ; Condensed matter physics ; Boron nitride ; Scaling ; Interatomic potential ; Nanotechnology ; Physics ; Molecular dynamics ; Quantum mechanics ; Mathematics ; Geometry Graphene research and applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Vivek Sinha | Jochem Jan Laan | Evgeny A. Pidko
Source: ChemRxiv (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

Accurate and Rapid Prediction of pKa of Transition Metal Complexes: Semiempirical Quantum Chemistry with a DataAugmented Approach Rapid and accurate prediction of reactivity descriptors of transition metal (TM) complexes is a major challenge for contemporary quantum chemistry. Recently developed GFN2-xTB method based on the density functional tight-binding theory (DFT-B) is suitable for high-throughput calculation of geometries and thermochemistry for TM complexes albeit with a moderate accuracy. Herein we present a data-augmented approach to improve substantially the accuracy of GFN2-xTB for the prediction of thermochemical properties using pKa values of TM hydrides as a representative model example. We constructed a comprehensive database for ca. 200 TM hydride complexes featuring the experimentally measured pKa’s as well as the GFN2-xTB optimized geometries and various computed electronic and energetic descriptors. The GFN2-xTB results were further refined and validated by DFT calculations with the hybrid PBE0 functional. Our results show that although the GFN2-xTB performs well in most cases, it fails to adequately desribe TM complexes featuring multicarbonyl and multihydride ligand environments. The dataset was analyzed with the partial least squares (OLS) fitting and was used to construct an automated machine learning (AutoML) approach for the rapid estimation of pKa of TM hydride complexes. The results obtained show a high predictive power of the very fast AutoML model (RMSE ~ 2.7) comparable to that of the much slower DFT calculations (RMSE ~ 3). The presented data-augmented quantum chemistry-based approach is promising for high-throughput computational screening workflows of homogeneous TM-based catalysts. Thermochemistry ; Hydride ; Density functional theory ; Chemistry ; Quantum ; Transition metal ; Quantum chemistry ; Homogeneous ; Workflow ; Quantum chemical ; Computational chemistry ; Mean squared error ; Computer science ; Metal ; Statistical physics ; Molecule ; Catalysis ; Physical chemistry ; Physics ; Electrochemistry ; Mathematics ; Quantum mechanics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Chia-Hao Liu
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Development of small-angle scattering pair distribution function analysis techniques and application to nanoparticles assemblies With the improvement in synthesis method, a variety of nanoparticles (NPs) with nearly uniform distribution in size and morphology are now available to scientists. This progress opens a new opportunity of assembling these high quality nanoparticles into metamaterial - nanoparticle assemblies (NPAs). The properties of NPA depend on the interactions between constituent NPs, therefore NPA offer a distinct advantage in designing material properties that are not available in the bulk phase (crystal) or discrete phase (nanoparticle). Novel application of NPA in modern devices, such as solar cells and field effect transistors, had also been demonstrated. The spatial arrangements of NPs is the key factor to their interactions, therefore, it is crucial to characterize the structure of NPA quantitatively. The technique of diffraction plays an unique role for characterizing NPA structure, as it not only offers the structural type, which may also be obtained from imagine technique, but also yields structural information in three-dimension, such inter-particle distance and the range of structural coherence of the packing order. Traditionally, the diffraction analysis is based on crystallography and is carried out in reciprocal space. However, it has been known local structure is overlooked in this kind of crystallographic analysis, which places a challenge for have a comprehensive understanding of the NPA structure. The pair distribution function (PDF) analysis, which is powerful in probing local structures for atomic systems, serves as a promising tool for characterizing NPA structure. How- ever, the approach of using PDF analysis for NPA structure characterization has barely been explored. In this thesis, I will present the methodological developments of the PDF technique. Starting from presenting a machine-learning-assisted approach for predicting the space group of its structure from the PDF, I will be focusing on the aspect of accelerating the structure modeling steps with PDF. Next, the development of pair distribution function analysis in small-angle scattering domain sasPDF will be introduced, including software package PDFgetX3 which is aiming to facilitate the extraction of PDF from small-angle scattering data quickly. The approach of sasPDF is validated against three representative structures across different levels of structural order. Finally, the example of applying sasPDF method to identify the jamming transition signature in polymer-ligated NPA is introduced, followed by another example of discovering multiply-twinned structure from the reprogramming of DNA-ligated NPA. Nanoparticle ; Scattering ; Function (biology) ; Materials science ; Small-angle scattering ; Pair distribution function ; Nanotechnology ; Physics ; Optics ; Mathematics ; Mathematical analysis ; Biology X-ray Diffraction in Crystallography ; Optical and Acousto-Optic Technologies ; Crystal Structures and Properties
Authors: Jan‐Joris Devogelaer | Hugo Meekes | Paul Tinnemans | Elias Vlieg | R. De Gelder
Source: ChemRxiv (preprint, 2020-06-24)
Field: Materials Science — Materials Chemistry

Cocrystal Prediction by Artificial Neural Networks A significant amount of attention has been given to the design and synthesis of cocrystals by both industry and academia because of its potential to change a molecule’s physicochemical properties. This paper reports on the application of a data-driven cocrystal prediction method, based on two types of artificial neural network models and cocrystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a cocrystal is likely to form. Cocrystal ; Artificial neural network ; Computer science ; Artificial intelligence ; Chemistry ; Molecule ; Organic chemistry Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Michael Darmawan
Source: DR-NTU (Nanyang Technological University) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Augmenting machine learning to thermoelectric measurements : using Bayesian inference to infer electronic transport parameters from device level power-load data Thermoelectric materials efficiency is characterized by Figure of Merit. Figure of Merit depends on several thermoelectric descriptors which is thermal conductivity and Power Factor, which is equal to the product of electrical conductivity and square of Seebeck coefficient. Hence, scientists have been looking for a rapid characterization of thermoelectric for highput materials synthesis. In this project, we use Bayesian inference algorithm on power-load experiment dat to infer the transport properties of thermoelectric materials. We focused on conventional Bi-Sb-Te thermoelectric materials,Bi0.4Sb1.6Te3, Bi0.5Sb1.5Te3, and Bi0.6Sb1.4Te3. These samples are prepared using ball miling followed by Spark Plasma Sintering (SPS) process. We obtained the power out of a single leg as function of load resistance at base temperature of 300 K in ultra-high vacuum in a closed-cycle He cryostat. &#13;\nThe experiment is repeated under different temperature gradients accros the samples as discriminative testing condition for the machine learning algorithm to infer the electrical transport properties. Seebeck coefficient (S), electrical conductivity (), contact resistance (Rc), and error in temperature difference are inferred and presented in probability distribution. Along with these parameters, Bayesian inference helps to estimate the intrinsic properties of thermoelectric materials, such as energy depedent scattering, doping and weighted average mobility, which are very tedious to measure experimentally. Inference ; Bayesian probability ; Artificial intelligence ; Computer science ; Bayesian inference ; Machine learning ; Power (physics) ; Seebeck coefficient ; Data mining ; Engineering ; Electrical engineering ; Electrical resistivity and conductivity Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Stefan Oehmcke | Thomas Teusch | Thorben Petersen | Thorsten Klüner | Oliver Krämer
Source:  (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Modeling H<sub>2</sub>O/Rutile-TiO<sub>2</sub>(110) Potential Energy Surfaces with Deep Networks Successful and cost-effective water splitting could be one of the most interesting energy sources of the future. The modification of the transition metal oxide rutile-TiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> as photocatalyst can lead to improved performance in the water splitting process. For that purpose, an accurate description of the interaction potential of a water molecule and the rutile-TiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (110) surface in the ground and electronically excited state after photoexcitation is crucial. The electronic Schrödinger equation for the states involved is solved pointwise for different nuclear configurations within the Born-Oppenheimer approximation, and accurate fits to these energy points are required to obtain an analytic expression for the potential energy surface. This is too computationally expensive for fine-grained surface calculations of quantum chemical models. In this paper, we propose to use state-of-the-art deep learning techniques to provide accurate fits for this problem. Namely, we employ a fully connected variant of ResNet and DenseNet with heavy regularization (L2, RReLU, Dropout, and BatchNormalization). Previous literature applied neural network approaches before, but with unsatisfactory accuracy. In an experimental evaluation we show that the root mean squared error (RMSE) can be 6.8 times lower for the exited state and 12.7 times lower for the ground state compared to former approaches. Pointwise ; Ground state ; Rutile ; Excited state ; Potential energy surface ; Mean squared error ; Algorithm ; Materials science ; Computer science ; Chemistry ; Mathematics ; Physics ; Molecule ; Atomic physics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Yutaka Oya | Gota Kikugawa
Source: Journal of The Adhesion Society of Japan (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Multi-Objective Material Optimization by Self-Organizing Map We introduced the Self-Organizing Map（ SOM） as a visualization tool utilized for a multi-objective material optimization. The SOM, one of un-supervised machine learning techniques, enables to locate and cluster materials on a low-dimensional space according to similarity of multi-objective functions. In this paper, we showed the effectiveness of the SOM through applications to structural materials such as composite and thermosetting resins. Self-organizing map ; Computer science ; Thermosetting polymer ; Visualization ; Artificial intelligence ; Similarity (geometry) ; Pattern recognition (psychology) ; Materials science ; Cluster analysis ; Composite material ; Image (mathematics) Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection