jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyunwook Jung | Sina Stocker | Christian Künkel | Harald Oberhofer | Byungchan Han | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2019-10-22)
Field: Materials Science — Materials Chemistry

Size-Extensive Molecular Machine Learning with Global Descriptors Machine learning (ML) models are increasingly used to predict molecular prop- erties in a high-throughput setting at a much lower computational cost than con- ventional electronic structure calculations. Such ML models require descriptors that encode the molecular structure in a vector. These descriptors are generally designed to respect the symmetries and invariances of the target property. However, size- extensivity is usually not guaranteed for so-called global descriptors. In this contri- bution, we show how extensivity can be build into ML models with global descriptors such as the Many-Body Tensor Representation. Properties of extensive and non- extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our result shows that the non-extensive model is only useful in the size-range of its training set, whereas the extensive models provide reasonable predictions across large size differences. Remaining sources of error for the extensive models are discussed. Computer science ; Range (aeronautics) ; Set (abstract data type) ; Representation (politics) ; Property (philosophy) ; Machine learning ; Tensor (intrinsic definition) ; Training set ; Theoretical computer science ; Artificial intelligence ; Data mining ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Philippe Schwaller | Alain C. Vaucher | Vishnu H Nair | Teodoro Laino
Source: ChemRxiv (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Data-Driven Chemical Reaction Classification with Attention-Based Neural Networks Organic reactions are usually clustered in classes that collect entities undergoing similar structural rearrangement. The classification process is a tedious task, requiring first an accurate mapping of the rearrangement (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present a transformer-based model that infers reaction classes from the SMILES representation of chemical reactions. The model reaches an accuracy of 93.8 % for a multi-class classification task involving several hundred different classes. The attention weights provided by the model give an insight into what parts of the SMILES strings are taken into account for classification, based solely on data. We study the incorrect predictions of our model and show that it uncovers different biases and mistakes in the underlying data set. Computer science ; Task (project management) ; Artificial intelligence ; Class (philosophy) ; Transformer ; Set (abstract data type) ; Representation (politics) ; Data set ; Smiles rearrangement ; Data mining ; Machine learning ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Logan Ward | Ben Blaiszik | Ian Foster | Rajeev S. Assary | Badri Narayanan | Larry A. Curtiss
Source: MRS Communications (preprint, 2019-08-27)
Field: Materials Science — Materials Chemistry

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations Fidelity ; Molecule ; Set (abstract data type) ; Quantum chemical ; Mean absolute error ; High fidelity ; Training set ; Energy (signal processing) ; Quantum ; Computer science ; Organic molecules ; Simple (philosophy) ; Core (optical fiber) ; Chemistry ; Algorithm ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Mean squared error ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Alain Tchagang | Julio J. Valdés
Source:  (preprint, 2019-05-05)
Field: Materials Science — Materials Chemistry

Discrete Fourier Transform Improves the Prediction of the Electronic Properties of Molecules in Quantum Machine Learning High-throughput approximations of quantum mechanics calculations and combinatorial experiments have been traditionally used to reduce the search space of possible molecules, drugs and materials. However, the interplay of structural and chemical degrees of freedom introduces enormous complexity, which the current state-of-the-art tools are not yet designed to handle. The availability of large molecular databases generated by quantum mechanics (QM) computations using first principles open new venues for data science to accelerate the discovery of new compounds. In recent years, models that combine QM with machine learning (ML) known as QM/ML models have been successful at delivering the accuracy of QM at the speed of ML. The goals are to develop a framework that will accelerate the extraction of knowledge and to get insights from quantitative process-structure-property-performance relationships hidden in materials data via a better search of the chemical compound space, and to infer new materials with targeted properties. In this study, we show that by integrating well-known signal processing techniques such as discrete Fourier transform in the QM/ML pipeline, the outcomes can be significantly improved in some cases. We also show that the spectrogram of a molecule may represent an interesting molecular visualization tool. Computer science ; Chemical space ; Visualization ; Pipeline (software) ; Computation ; Fourier transform ; Property (philosophy) ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Computational science ; Machine learning ; Drug discovery ; Chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: А. В. Блинов | A. A. Gvozdenko | M. A. Yasnaya | Alexey Golik | А. А. Blinova | I.M. Shevchenko | V.N. Kramarenko
Source: Physical and Chemical Aspects of the Study of Clusters Nanostructures and Nanomaterials (article, 2019-12-16)
Field: Materials Science — Biomaterials

EFFECT OF SYNTHESIS PARAMETERS ON DIMENSIONAL CHARACTERISTICS OF Fe<sub>3</sub>O<sub>4</sub> NANOPARTICLES: NEURAL-NETWORK RESEARCH Our research shows the possibility of using the neural-network processing of experimental data to study the influence of various factors on the process of synthesis of nanoscale Iron (II, III) oxide. A mathematical model was obtained which adequately describes the effect of temperature, stabilizer mass and precipitant quantity on the size of nanoparticles of Iron (II, III) oxide. The optimal synthesis conditions were determined, which provide a high content of Fe3O4 particles with an average hydrodynamic radius less than 100 nm. Artificial neural network ; Nanoparticle ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Martin Jacob | Loubna El Gueddari | G. Navarro | M. C. Cyrille | Pascale Bayle‐Guillemaud | Philippe Ciuciu | Zineb Saghi
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Statistical Machine Learning and Compressed Sensing Approaches for Analytical Electron Tomography - Application to Phase Change Materials Journal Article Statistical Machine Learning and Compressed Sensing Approaches for Analytical Electron Tomography - Application to Phase Change Materials Get access Martin Jacob, Martin Jacob Univ. Grenoble Alpes, CEA-LETI, Grenoble, France Corresponding author: martin.jacob@cea.fr Search for other works by this author on: Oxford Academic Google Scholar Loubna El Gueddari, Loubna El Gueddari CEA-NeuroSpin, Bât 145 Gif-sur Yvette, France Search for other works by this author on: Oxford Academic Google Scholar Gabriele Navarro, Gabriele Navarro Univ. Grenoble Alpes, CEA-LETI, Grenoble, France Search for other works by this author on: Oxford Academic Google Scholar Marie-Claire Cyrille, Marie-Claire Cyrille Univ. Grenoble Alpes, CEA-LETI, Grenoble, France Search for other works by this author on: Oxford Academic Google Scholar Pascale Bayle-Guillemaud, Pascale Bayle-Guillemaud Univ. Grenoble Alpes, CEA-INAC, MEM, Grenoble, France Search for other works by this author on: Oxford Academic Google Scholar Philippe Ciuciu, Philippe Ciuciu CEA-NeuroSpin, Bât 145 Gif-sur Yvette, France Search for other works by this author on: Oxford Academic Google Scholar Zineb Saghi Zineb Saghi Univ. Grenoble Alpes, CEA-LETI, Grenoble, France Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 156–157, https://doi.org/10.1017/S143192761900151X Published: 01 August 2019 Library science ; Art ; Art history ; Humanities ; Computer science Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Jordan Venderley | Michael Matty | Eun-Ah Kim
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning of single crystal x-ray diffraction data Diffraction ; Unsupervised learning ; Materials science ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Crystallography ; Optics ; Physics ; Chemistry ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Shenghong Ju | Ryo Yoshida | Chang Liu | Kenta Hongo | Terumasa Tadano | Junichiro Shiomi
Source: ChemRxiv (preprint, 2019-09-20)
Field: Materials Science — Materials Chemistry

Exploring Diamond-Like Lattice Thermal Conductivity Crystals via Feature-Based Transfer Learning Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond. Even though most materials in the top list are superhard materials, we reveal that superhard property do not necessarily lead to high lattice thermal conductivity. Large hardness means high elastic constants and group velocity of phonons in the linear dispersion regime, but the lattice thermal conductivity is determined also by other important factor such as the phonon relaxation time. What’s more, the average or maximum dipole polarizability and the van der Waals radius are revealed to be the leading descriptors among those that can also be qualitatively related to anharmonicity.&lt;br&gt; Thermal conductivity ; Anharmonicity ; Phonon ; Diamond ; Polarizability ; Materials science ; Lattice (music) ; Condensed matter physics ; Heat transfer ; Transfer of learning ; Machine learning ; Artificial intelligence ; Statistical physics ; Computer science ; Thermodynamics ; Physics ; Quantum mechanics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Di An | Zhuang Ma | C Y Li | Chen Ma | W Z Li
Source: IOP Conference Series Materials Science and Engineering (article, 2019-11-01)
Field: Materials Science — Ceramics and Composites

Machine-Learning-Assisted the Design of Resin Matrix Composites Coating with Ablation Resistance Abstract Traditional experimental methods always cost a lot but produce little when designing and developing new kinds of materials, especially for coating materials. However, with the assistance of machine learning, it is possible to predict the performance of a specific coating without preparation or simulation, which makes the design of material more efficient. In this study, machine learning was introduced to assist the design of resin matrix composites coating with ablation resistance. A structured method for engineering data in the material field was approved. Based on this method, the data from laboratory records and Lange’s Chemistry Handbook were collated into one operational database. All the 190 sets of data were used to train the artificial neural network (ANN) regression model to predict the back-surface temperature of the substrate for specific coating under given ablation condition. The mean absolutely percentage error (MAPE) of the model is 7%. Concerning the characteristics of the material database, a feature engineering method, which combines the Pearson correlation coefficient and random forest (RF) algorithm was performed to identify the main controlling factors of the service performance of the coatings. Coating ; Artificial neural network ; Ablation ; Computer science ; Matrix (chemical analysis) ; Materials science ; Mean absolute percentage error ; Random forest ; Composite material ; Machine learning ; Artificial intelligence ; Engineering Advanced ceramic materials synthesis ; Metal and Thin Film Mechanics ; Additive Manufacturing and 3D Printing Technologies
Authors: Tetiana Zubatyuk | Ben Nebgen | Nicholas Lubbers | Justin S. Smith | R.I. Zubatyuk | Guoqing Zhou | Christopher Koh | Kipton Barros | Olexandr Isayev | Sergei Tretiak
Source: arXiv (Cornell University) (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks The Hückel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these traditionally static parameters with dynamically predicted values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability while the deep neural network parameterization is smooth, accurate, and reproduces insightful features of the original static parameterization. Finally, we demonstrate that the Hückel model, and not the deep neural network, is responsible for capturing intricate orbital interactions in two molecular case studies. Overall, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models. Artificial neural network ; Interfacing ; Interpretability ; Statistical physics ; Simple (philosophy) ; Parameterized complexity ; Physics ; Hamiltonian (control theory) ; Computer science ; Deep learning ; Artificial intelligence ; Hückel method ; Theoretical physics ; Quantum mechanics ; Algorithm ; Molecular orbital ; Mathematics ; Molecule Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Thantip Krasienapibal | Yasuhiro Shirasaki | Momoyo Enyama | Akiko Kagatsume | Min‐Seok Park | Sayaka Tanimoto
Source: MRS Advances (article, 2019-01-02)
Field: Materials Science — Materials Chemistry

Fast Evaluation of Microstructure-Property Relation in Duplex Alloys Using SEM Images Microstructure ; Materials science ; Scanning electron microscope ; Duplex (building) ; Segmentation ; Phase (matter) ; Crystal (programming language) ; Artificial intelligence ; Composite material ; Computer science Microstructure and mechanical properties ; Non-Destructive Testing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Alex Foden | Alessandro Previero | T. Ben Britton
Source: arXiv (Cornell University) (preprint, 2019-08-12)
Field: Materials Science — Materials Chemistry

Advances in electron backscatter diffraction We present a few recent developments in the field of electron backscatter diffraction (EBSD). We highlight how open source algorithms and open data formats can be used to rapidly to develop microstructural insight of materials. We include use of AstroEBSD for single pixel based EBSD mapping and conventional orientation mapping; followed by an unsupervised machine learning approach using principal component analysis and multivariate statistics combined with a refined template matching method to rapidly index orientation data with high precision. Next, we compare a diffraction pattern captured using direct electron detector with a dynamical simulation and project this to create a high quality experimental "reference diffraction sphere". Finally, we classify phases using supervised machine learning with transfer learning and a convolutional neural network. Electron backscatter diffraction ; Computer science ; Orientation (vector space) ; Diffraction ; Artificial intelligence ; Principal component analysis ; Pixel ; Pattern recognition (psychology) ; Optics ; Physics ; Mathematics ; Geometry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Geological and Geochemical Analysis
Authors: Sanae Zaoui | Adel Belayadi | Mokhtar Zabat | Ahmed Mougari | F. Mekideche-Chafa
Source: Physica B Condensed Matter (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

An implementation of impedance spectroscopy approach to predict thermoelectronic properties of elements using a connectionist model of artificial neural network Thermoelectric effect ; Artificial neural network ; Thermal diffusivity ; Materials science ; Seebeck coefficient ; Thermal conductivity ; Dielectric spectroscopy ; Electrical impedance ; Thermoelectric materials ; Electronic engineering ; Computer science ; Biological system ; Thermodynamics ; Artificial intelligence ; Physics ; Engineering ; Chemistry ; Electrical engineering ; Physical chemistry ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermography and Photoacoustic Techniques
Authors: Hiroyuki Teramae | Meiyan Xuan | Tsukasa YAMASHITA | Jun Takayama | Mari Okazaki | Takeshi Sakamoto
Source: Journal of Computer Chemistry Japan (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Theoretical Study on Antioxidant Properties of Ferulic Acid Ferulic acid is known to have strong antioxidant properties. In the present study, we investigate the electronic structures of ferulic acid and its radical species extracting the hydrogen atom from its phenolic hydroxyl group. The relation of the results by several machine learning models using R/caret package, such as partial least squares, random forest, radial basis function kernel regularized least squares, and baysian regularized neural network, with the radical scavenging activity with the DPPH reagent, IC50, measured by Sakamoto et al. is discussed. We found all four methods gave reasonable correlation coefficients which means the possible prediction of the IC50 values with the results of the molecular orbital calculations only. Ferulic acid ; DPPH ; Chemistry ; Antioxidant ; Partial least squares regression ; Kernel (algebra) ; Hydrogen atom ; Computational chemistry ; Organic chemistry ; Mathematics ; Biochemistry ; Group (periodic table) ; Pure mathematics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemical and Physical Properties in Aqueous Solutions
Authors: Hui Lü | Xiaojun Hu | Bin Cao | Liang Ma | Wanqiu Chai | Yunchuan Yang
Source: Chemometrics and Intelligent Laboratory Systems (article, 2019-11-11)
Field: Materials Science — Materials Chemistry

Data mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems Liquidus ; Electrolyte ; Crystallization ; Support vector machine ; Chemistry ; Thermodynamics ; Computer science ; Phase (matter) ; Machine learning ; Electrode ; Physical chemistry ; Physics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes
Authors: Ruifang Wang | Dandan Ma | Zhiwei Li | Haisheng Ren | Jianyi Ma
Source: Chemical Physics Letters (article, 2019-04-12)
Field: Chemistry — Organic Chemistry

Activation of the electronic excitation state of reactions in the complex region using microwave irradiation Excitation ; Microwave irradiation ; Irradiation ; Microwave ; Atomic physics ; State (computer science) ; Chemistry ; Materials science ; Photochemistry ; Physics ; Nuclear physics ; Computer science ; Quantum mechanics Microwave-Assisted Synthesis and Applications ; Inorganic and Organometallic Chemistry ; Molecular Spectroscopy and Structure
Authors: Dmitriy A. Golovatov | Aslan A. Tatarkanov | Azamat A. Shavaev | С. А. Гусев
Source:  (article, 2019-09-01)
Field: Materials Science — General Materials Science

The Use of Modern Information Technology in Predicting the Process of Impregnating Composite Preforms with Polymer Resins The paper describes the development of automated system for modeling resin frontal spread during the impregnation of fibrous reinforcing agent with regard to resin viscosity and impregnation time. The topicality of the paper is due to the necessity to provide the quality of composite materials by managing a technological process. The method of modeling boundaries of resin spread during reinforcing agent impregnation on the basis of an artificial neural network is offered. A number of experimental studies on a resin spread process in the volume of reinforcing agent, allowing to make a data base for training neural network model, is made. The developed neural network model allows to forecast the specific features of a composite material product impregnation process with regards to rheokinetics of the process. Artificial neural network ; Process (computing) ; Composite number ; Process engineering ; Materials science ; Composite material ; Computer science ; Artificial intelligence ; Engineering Material Properties and Applications ; Polymer Science and Applications ; Material Properties and Failure Mechanisms