jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: LAUREN K. WOLF
Source: C&EN Global Enterprise (article, 2018-12-17)
Field: Chemistry — Physical and Theoretical Chemistry

What to watch for in 2019 To know where you’re going, you have to know where you’ve been. C&EN adopted this motto in early December when it hosted a webinar in conjunction with the American Chemical Society, our parent organization. During the live broadcast, science editors Michael Torrice and Lauren Wolf picked the brains of chemistry experts in the scientific publishing community about the biggest research trends from 2018 and those on the way in 2019. Panelists included Marshall Brennan, publishing manager at preprint server ChemRxiv, which is co-owned by ACS; Claire Hansell, a senior editor and the chemistry and materials team leader at Nature; and Jake Yeston, editor of physical sciences research at Science. They weighed in on a variety of scientific advances, including those in flow chemistry and artificial intelligence. For her big trend of 2018, Hansell picked the use of machine learning to predict reactions and new materials. Yeston pointed to organic chemists Publishing ; Scientific publishing ; Variety (cybernetics) ; Preprint ; Library science ; Chemistry ; Media studies ; Sociology ; Computer science ; Political science ; World Wide Web ; Artificial intelligence ; Law History and advancements in chemistry
Authors: Yada
Source: The Chemical Society of Japan (article, 2018-03-02)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Prediction of Reaction Yield in Tungsten-catalyzed Epoxidation of Alkene with Hydrogen Peroxide Alkene ; Hydrogen peroxide ; Yield (engineering) ; Catalysis ; Tungsten ; Chemistry ; Hydrogen ; Photochemistry ; Organic chemistry ; Materials science ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: 이종수
Source: Open Access System for Information Sharing (Pohang University of Science and Technology) (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

Quantification of Phase Volume Fraction in Alpha-beta Ti Alloys by Sensitivity Analysis of Artificial Neural Networks BETA (programming language) ; Alpha (finance) ; Artificial neural network ; Sensitivity (control systems) ; Fraction (chemistry) ; Phase (matter) ; Volume fraction ; Volume (thermodynamics) ; Artificial intelligence ; Materials science ; Mathematics ; Chemistry ; Computer science ; Chromatography ; Statistics ; Physics ; Thermodynamics ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Ljubinka Sandjakoska
Source:  (article, 2018-10-05)
Field: Materials Science — Materials Chemistry

Dropout Regularization in Deep Neural Networks Used in Atomic Simulations Recently deep learning has received significant attention in the research community. That implies the fast development of improved deep learning concepts, which can be applied in variety of domains. In most of the domains the problem of regularization arise. This paper aims to give a breakthrough in understanding the importance of dropout, as an adaptive regularizer that can control the overfitting in deep neural networks. Several aspects of the dropout regularization are discussed. We try to solve the problem of correlation between the adjacent units and propose a new method for regularization of deep neural network designed for atomic simulations, actually for predicting of the energies of small molecules. Also, a theoretical framework of the proposed method is given, followed by the experiments on molecular dynamics dataset. The main contribution of this paper is improving on state-of-the-art results of energy prediction using the equilibrium energies as well as molecular dynamics trajectory on benchmark dataset. Overfitting ; Dropout (neural networks) ; Regularization (linguistics) ; Computer science ; Deep learning ; Artificial intelligence ; Benchmark (surveying) ; Artificial neural network ; Deep neural networks ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Mihaela MARIN
Source: International Multidisciplinary Scientific GeoConference SGEM ... (article, 2018-06-20)
Field: Chemistry — Inorganic Chemistry

EXPERIMENTAL AND PREDICTION OF POROSITY IN SOME SINTERED IRON-BASED POWDER METALLURGY MATERIALS BY USING ARTIFICIAL NEURAL NETWORKS Porosity ; Artificial neural network ; Materials science ; Powder metallurgy ; Metallurgy ; Artificial intelligence ; Computer science ; Composite material ; Sintering Pigment Synthesis and Properties ; Iron and Steelmaking Processes
Authors: Chi Chen | Z.G. Deng | Richard Tran | Hanmei Tang | Iek‐Heng Chu | Shyue Ping Ong
Source: Physical Review Materials (article, 2018-10-30)
Field: Materials Science — Materials Chemistry

Publisher's Note: Accurate force field for molybdenum by machine learning large materials data [Phys. Rev. Materials <b>1</b>, 043603 (2017)] Received 24 October 2018DOI:https://doi.org/10.1103/PhysRevMaterials.2.109902©2018 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElasticityTechniquesMachine learningInterdisciplinary PhysicsCondensed Matter, Materials & Applied Physics Molybdenum ; Materials science ; Field (mathematics) ; Force field (fiction) ; Engineering physics ; Nanotechnology ; Metallurgy ; Artificial intelligence ; Computer science ; Engineering ; Pure mathematics ; Mathematics Machine Learning in Materials Science
Authors: Qin Gao | Sanxi Yao | Jeff Schneider | Michael Widom
Source: Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (article, 2018-06-29)
Field: Materials Science — Materials Chemistry

Machine Learning methods for interatomic potentials: application to boron carbide Total energies of crystal structures can be calculated to high precision using quantum-based density functional theory (DFT) methods, but the calculations can be time consuming and scale badly with system size. Cluster expansions of total energy as a linear superposition of pair, triplet and higher interactions can efficiently approximate the total energies but are best suited to simple lattice structures. To model the total energy of boron carbide, with a complex crystal structure, we explore the utility of machine learning methods (L1-penalized regression, neural network, Gaussian process and support vector regression) that capture certain non-linear effects associated with manybody interactions despite requiring only pair frequencies as input. Our interaction models are combined with Monte Carlo simulations to evaluate the thermodynamics of chemical ordering. Superposition principle ; Statistical physics ; Gaussian ; Density functional theory ; Lattice (music) ; Monte Carlo method ; Artificial neural network ; Computer science ; Quantum Monte Carlo ; Physics ; Machine learning ; Quantum mechanics ; Mathematics ; Statistics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Mitchell Wood | Mary Alice Cusentino | Aidan Thompson
Source: Bulletin of the American Physical Society (article, 2018-11-07)
Field: Materials Science — Materials Chemistry

Scale-bridging from the Atoms Up; Employing Machine Learning to Improve the Accuracy and Scalability of Molecular Dynamics Bridging (networking) ; Scalability ; Scale (ratio) ; Molecular dynamics ; Computer science ; Dynamics (music) ; Chemical physics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Database Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Lukas Turcani | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2018-11-30)
Field: Materials Science — Materials Chemistry

Machine Learning for Organic Cage Property Prediction We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt. Computer science ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Supramolecular Chemistry and Complexes
Authors: Hiroya Nakata | Shandan Bai
Source: arXiv (Cornell University) (preprint, 2018-12-08)
Field: Materials Science — Materials Chemistry

Development of a New Parameter Optimization Scheme for a Reactive Force Field (ReaxFF) Based on a Machine Learning Approach Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the $k$-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets, thereby the optimized ReaxFF parameter can predict physical properties even in a high-temperature condition within a small effort of parameter refinement. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an $α$-Al$_2$O$_3$ crystal. The crystal structure of $α$-Al$_2$O$_3$ was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (11$\overline{2}$0) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process. ReaxFF ; Force field (fiction) ; Molecular dynamics ; Materials science ; Chemical vapor deposition ; Computer science ; Nanotechnology ; Chemistry ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Lukas Turcani | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2018-08-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Organic Cage Property Prediction &lt;p&gt;We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in&lt;/p&gt; &lt;p&gt;multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying&lt;/p&gt; &lt;p&gt;data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt.&lt;/p&gt; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Rendering (computer graphics) ; Open source software ; Porosity ; Chemistry ; Software ; Biological system ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Supramolecular Chemistry and Complexes
Authors: Khushmeen Sakloth | Wesley Beckner | Jim Pfaendtner | Garrett B. Goh
Source: arXiv (Cornell University) (preprint, 2018-09-13)
Field: Materials Science — Materials Chemistry

IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach however has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. However, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemicals entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately 20-35%, without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains. Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Feature engineering ; Domain (mathematical analysis) ; Hyperparameter ; Key (lock) ; Subject-matter expert ; Domain knowledge ; Class (philosophy) ; Feature (linguistics) ; Representation (politics) ; Deep learning ; Expert system Machine Learning in Materials Science ; Ionic liquids properties and applications ; Catalysis and Oxidation Reactions
Authors: Mykhaylo Krykunov | Tom K. Woo
Source: arXiv (Cornell University) (preprint, 2018-07-26)
Field: Materials Science — Materials Chemistry

Bond type restricted radial distribution functions for accurate machine learning prediction of atomization energies Understanding the performance of machine learning algorithms is essential for designing more accurate and efficient statistical models. It is not always possible to unravel the reasoning of neural networks. Here we propose a method for calculating machine learning kernels in closed and analytic form by combining atomic property weighted radial distribution function (AP-RDF) descriptor with a Gaussian kernel. This allowed us to analyse and improve the performance of the Bag-of-Bonds descriptor, when the bond type restriction is included in AP-RDF. The improvement is achieved for the prediction of molecular atomization energies and is due to the incorporation of a tensor product into the kernel which captures the multidimensional representation of the AP-RDF. On the other hand, the numerical version of the AP-RDF is a constant size descriptor, and it is more computationally efficient than Bag-of-Bonds. We have also discussed a connection between molecular quantum similarity and machine learning kernels with first principles kind of descriptors. Kernel (algebra) ; RDF ; Gaussian ; Computer science ; Similarity (geometry) ; Type (biology) ; Function (biology) ; Artificial neural network ; Gaussian function ; Artificial intelligence ; Representation (politics) ; Radial distribution function ; Algorithm ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Physics ; Quantum mechanics ; Pure mathematics ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Arturo Schiaffino | V. M. Krushnarao Kotteda | Arturo Bronson | Sanjay Shantha-Kumar | Vinod Kumar
Source:  (article, 2018-07-15)
Field: Materials Science — Materials Chemistry

Predicting the Depth of Penetration of Molten Metal Into a Pore Network Using TensorFlow Liquid metal infiltration consists of infusing liquid metal into a porous media or a packed bed of boron carbide powder to react and create ultimately a metal or ceramic matrix embedded with boride-carbide precipitates. The purpose of the study is to model the liquid flow into the capillaries of the packed bed by using machine learning algorithms from an open source available as TensorFlow library created by Google Brain. The library has a variety of algorithms including training and inference algorithms forming deep neural network models to predict the wetting dynamics, flow resistance, and the depth/rate of penetration into the capillaries of the packed bed. In the present work, the results from the machine-learning python code based on the TensorFlow library is compared against the experimental data obtained for molten Hf-Ti-Y-Zr alloys infiltrating into a packed bed of boron carbide at temperatures up to 2300°C. A summary of the techniques used to tweak the machine learning algorithms to predict the infusion behavior will be presented. Boron carbide ; Materials science ; Ceramic ; Liquid metal ; Artificial intelligence ; Packed bed ; Penetration (warfare) ; Carbide ; Wetting ; Molten metal ; Porous medium ; Silicon carbide ; Metal ; Porosity ; Composite material ; Mechanical engineering ; Computer science ; Metallurgy ; Chemical engineering ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Injection Molding Process and Properties
Authors: Yuan Dong | Chuhan Wu | Chi Zhang | Yingda Liu | Jianlin Cheng | Jian Lin
Source: arXiv (Cornell University) (preprint, 2018-09-28)
Field: Materials Science — Materials Chemistry

Deep Learning Bandgaps of Topologically Doped Graphene Manipulation of material properties via precise doping affords enormous tunable phenomena to explore. Recent advance shows that in the atomic and nano scales topological states of dopants play crucial roles in determining their properties. However, such determination is largely unknown due to the incredible size of topological states. Here, we present a case study of developing deep learning algorithms to predict bandgaps of boron-nitrogen pair doped graphene with arbitrary dopant topologies. A material descriptor system that enables to correlate structures with the bandgaps was developed for convolutional neuron networks (CNNs). Bandgaps calculated by the ab initio calculations and the corresponding structures were fed as input datasets to train VGG16 convolutional network, residual convolutional network, and concatenate convolutional network. Then these trained CNNs were used to predict bandgaps of doped graphene with various dopant topologies. All of them afford great prediction accuracy, showing square of the coefficient of correlation (R2) of &gt; 90% and root-mean-square errors of ~ 0.1 eV for the predicted bandgaps. They are much better than those predicted by a shallow machine learning method - support vector machine. The transfer learning was further performed by leveraging data generated from smaller systems to improve the prediction for large systems. Success of this work provides a cornerstone for future investigation of topologically doped graphene and other 2D materials. Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales. Dopant ; Graphene ; Network topology ; Materials science ; Doping ; Artificial intelligence ; Topology (electrical circuits) ; Deep learning ; Convolutional neural network ; Computer science ; Machine learning ; Nanotechnology ; Optoelectronics ; Engineering ; Electrical engineering Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Malcolm A. Ramsay
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2018-06-17)
Field: Materials Science — Materials Chemistry

malramsay64/MLCrystals-tutorial: Release version 1.0 of tutorial A tutorial demonstrating the use of machine learning for the classification of crystal structures in a molecular dynamics simulation. Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Paschalis Gkoupidenis | Dimitrios K. Koutsouras | George G. Malliaras
Source:  (article, 2018-09-18)
Field: Materials Science — Polymers and Plastics

Neuromorphic devices based on organic mixed conductors (Conference Presentation) Neuromorphic devices and architectures offer novel ways of data manipulation and processing, especially in data intensive applications. At a single device level, various forms of neuroplasticity have been emulated over the past years, mainly with inorganic devices. The implementation of neuroplasticity functions with these devices also enabled applications at a circuit level related to machine learning such as feature or pattern recognition. Although the field of organic-based neuromorphic devices and circuits is still at its infancy, organic materials may offer attractive features for neuromorphic engineering. Over the past years for example, a few simple neuromorphic functions have been demonstrated with biological substances and bioelectronic devices. In this work various neuromorphic devices will be presented that are based on organic mixed conductors, materials that are traditionally used in organic bioelectronics. A prominent example of a device in bioelectronics that exploits mixed conductivity phenomena is the organic electrochemical transistor (OECT). Devices based on OECTs show volatile and tunable dynamics suitable for the emulation of short-term synaptic plasticity functions. Chemical synthesis allows for the introduction of non-volatile phenomena suitable for long-term memory functions. The device operation in common electrolyte permits the definition of spatially distributed multiple inputs at a single device level. The presence of a global electrolyte in an array of devices also allows for the homeostatic or global control of the array. Global electrical oscillations can be used as global clocks that frequency-lock the local activity of individual devices in analogy to the global oscillations in the brain. Finally, “soft” interconnectivity through the electrolyte can be defined, a feature that paves the way for parallel interconnections between devices with minimal hard-wired connections. Neuromorphic engineering ; Presentation (obstetrics) ; Computer science ; Electrical conductor ; Computer architecture ; Electrical engineering ; Artificial intelligence ; Engineering ; Artificial neural network ; Medicine Conducting polymers and applications ; Advanced Memory and Neural Computing