jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eleanor K Miller
Source: OakTrust (Texas A&M University Libraries) (article, 2018-05-23)
Field: Chemistry — Physical and Theoretical Chemistry

Recognizing Elementary Elements in Chemical Diagram Sketches Organic Chemistry is a challenging subject that requires dedicated practice to learn the meticulous rules composing the subject, otherwise a student risks failure. Current software to teach chemical structures contains drag-and-drop components and fails to provide students with true understanding of Organic Chemistry concepts. My solution is to integrate a sketch recognition interface that can learn to recognize components of various, user-sketched chemical structures with a back-propagation neural network that can be trained to translate the components of the chemical structure to determine correctness. The accuracy of the program will be rigorously tested to determine correctness in interpreting chemical structures. Diagram ; Computer science ; Epistemology ; Mathematics education ; Mathematics ; Philosophy History and advancements in chemistry ; Various Chemistry Research Topics
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2018-12-17)
Field: Materials Science — Materials Chemistry

Machine learning marched forward Intelligent robots may yet take our jobs; they definitely grabbed a lot of C&EN’s headlines this year. Researchers continued to explore applications of machine learning, a type of artificial intelligence used to make predictions or decisions through algorithms that can learn from large data sets. The technology powers self-driving cars and image-recognition software. Scientists demonstrated numerous ways in which machine learning can help explore chemical space. For example, Heather Kulik of the Massachusetts Institute of Technology and colleagues identified inorganic molecules called spin-crossover complexes that could be useful as sensors or electronic switches (J. Phys. Chem. Lett. 2018, DOI: 10.1021/acs.jpclett.8b00170). Apurva Mehta of the SLAC National Accelerator Laboratory, along with collaborators, used machine learning to identify new alloys that are metallic glasses (Sci. Adv. 2018, DOI: 10.1126/sciadv.aaq1566). And the chemical company Symrise teamed up with IBM to search for new fragrances using machine learning. Meanwhile, Thomas F. Miller and coworkers Computer science ; Artificial intelligence Machine Learning in Materials Science ; Big Data and Digital Economy ; Economic and Technological Innovation
Authors: Murat Beken | Fatma Çavuş Kosovali | Y. Lenger Özcanlı
Source: DergiPark (Istanbul University) (article, 2018-06-30)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Modelling of PP/PET Blends In this study, comparison between the dynamic mechanical properties of polymer blends and the results of artificial neural networks (ANN) modeling has been conducted. The glass transition temperature and storage modulus values of PP(polypropylene)/PET(polypropylene terephthalate) polymer blends was used for ANN modelling. The observations on ANN results and the experimental results has shown sufficient accuracy mutually. At the same time, these results were supported by scanning analyzes. The artificial intelligence modeling studies for this article proves the applicability of dynamic mechanical properties of PP/PET blends. These results shows that artificial neural networks can be a helpful tool for experimental work of dynamic mechanical properties of polymer materials. Polypropylene ; Artificial neural network ; Materials science ; Dynamic mechanical analysis ; Glass transition ; Polymer ; Dynamic modulus ; Modulus ; Polymer blend ; Biological system ; Composite material ; Polymer science ; Artificial intelligence ; Computer science ; Copolymer Polymer crystallization and properties
Authors: Toru Hara
Source: Microscopy (article, 2018-10-26)
Field: Materials Science — Surfaces, Coatings and Films

2aA_SS1-2Current situation and future prospects of FIB-SEM serial-sectioning technique in materials science Two types of material genome integration system (MIPHA and rMIPHA) were developed to accelerate material development much more efficiently. The MIPHA/rMIPHA allow users to minute-base image processing, plenty of quantitative 2D/3D analysis, direct and inverse analysis to correlate microstructure and properties. A variety of machine learning techniques are implemented in MIPHA/rMIPHA such as artificial neural network, support vector regression, random forest regression, image recognition by convolutional neural network, machine-leaning based image processing, AIC/BIC, principal component analysis, autoencoder, maximum likelihood analysis, posterior-MCMC analysis etc. This machine-learning based approach features ultimate approximation, complex system, noisy data, low calculation cost, estimation, input-output correlation, and big data. These systems also suggest most reasonable parameters in theoretical formulas by maximum likelihood parametric models. In 2D/3D analysis modules, the following material genomes are calculated just by "one click"; 2D count, 2D area, 2D circ., 2D solidity, 2D ferret's diameter/angle, 2D Morishita's index, 3D count, 3D volume, 3D ferret's diameter, 3D sphericity, 3D surface area, 3D genus, 3D Euler-Poincare, 3D piercing particle, 3D isolated inner particle, 3D isolated inner particle with isolated inner matric particle, 3D branching points, 3D mean/gaussian curvature. Materials science ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
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\n learning prediction of atomization energies Understanding the performance of machine learning algorithms is essential for\ndesigning more accurate and efficient statistical models. It is not always\npossible to unravel the reasoning of neural networks. Here we propose a method\nfor calculating machine learning kernels in closed and analytic form by\ncombining atomic property weighted radial distribution function (AP-RDF)\ndescriptor with a Gaussian kernel. This allowed us to analyse and improve the\nperformance of the Bag-of-Bonds descriptor, when the bond type restriction is\nincluded in AP-RDF. The improvement is achieved for the prediction of molecular\natomization energies and is due to the incorporation of a tensor product into\nthe kernel which captures the multidimensional representation of the AP-RDF. On\nthe other hand, the numerical version of the AP-RDF is a constant size\ndescriptor, and it is more computationally efficient than Bag-of-Bonds. We have\nalso discussed a connection between molecular quantum similarity and machine\nlearning kernels with first principles kind of descriptors.\n Kernel (algebra) ; RDF ; Gaussian ; Computer science ; Similarity (geometry) ; Artificial intelligence ; Function (biology) ; Artificial neural network ; Type (biology) ; Representation (politics) ; Gaussian function ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Physics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Nicholas Marcella | Janis Timosenko | Anatoly I. Frenkel
Source: Bulletin of the American Physical Society (article, 2018-03-05)
Field: Materials Science — Materials Chemistry

Determination of Local Structure Descriptors from X-ray Absorption Near Edge Structure in Bimetallic Nano-Alloys Using an Ab-Inito-Trained Neural Network Bimetallic strip ; Nano- ; Artificial neural network ; Network structure ; Absorption (acoustics) ; Extended X-ray absorption fine structure ; Materials science ; X-ray ; Local structure ; Crystallography ; Chemistry ; Computer science ; Optics ; Physics ; Artificial intelligence ; Absorption spectroscopy ; Machine learning ; Composite material ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Yuma Iwasaki | Ichiro Takeuchi | Masahiko Ishida
Source: APS (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Predicting Material Properties by Integrating Combinatorial Experiments, Ab-initio Calculations, and Machine Learning in the Fe x Co y Ni 1-x-y Ternary Ternary operation ; Materials science ; Ab initio ; Ab initio quantum chemistry methods ; Thermodynamics ; Physical chemistry ; Computer science ; Physics ; Molecule ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Ruobing Wang
Source: arXiv (Cornell University) (preprint, 2018-05-24)
Field: Materials Science — Materials Chemistry

Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve complicated quantum-chemical equations and realizing efficient computing of molecular electronic properties. Generalization ; Quantum chemical ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Quantum ; Machine learning ; Theoretical computer science ; Molecule ; Mathematics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Humera Parveen | Vikash Yadav | Amit Gupta | Yogesh Kumar Sharma
Source: 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

Retraction Notice: A Particle Swarm Optimization Strategy using QSAR modeling on the Second generation Neural Network QSAR (Quantitative Structure-Activity Relationship) demonstrating is one of the all-around created zones in sedate improvement through computational chemistry and computational biology. Diverse properties or practices of compound particles have been explored in the field of QSAR, This sort of connection between sub-atomic structure and change in organic movement is focal point of center for QSAR displaying. Artificial neural network based calculations are essential instruments for QSAR investigation, subsequently; they are coordinated into the medication generation process. In this paper we will attempt to gesso through the issue of taking in the Second generation neural networks (2DNN/CVNNs) utilizing Particle Swarm Optimization (PSO); which is one of the open themes in the machine learning computing environment where the two dimensional/Complex valued neural network (2DNN/CVNN) is a more entangled for complex-esteemed information handling in two dimensional space(also called the 2DNN) because of a considerable measure of imperatives, for example, enactment work must be limited and differentiable at the total complex environment. In the proposed study, a 2DNN show for genuine esteemed relapse issues s exhibited. We tried such prepared CVNN on two QSAR datasets as a true benchmark issue. The outcomes demonstrate that the forecast and speculation capacities of CVNNs are better in correlation than the traditional real valued neural systems (1DNN/RVNNs). Also, merging of CVNNs is substantially speedier than that of RVNNs in a large portion of the case Quantitative structure–activity relationship ; Artificial neural network ; Particle swarm optimization ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Elizabeth J. Kautz | Alexander Hagen | Jesse Johns | Douglas E. Burkes
Source: arXiv (Cornell University) (preprint, 2018-11-30)
Field: Materials Science — Materials Chemistry

A machine learning approach to thermal conductivity modeling: A case\n study on irradiated uranium-molybdenum nuclear fuels A deep neural network was developed for the purpose of predicting thermal\nconductivity with a case study performed on neutron irradiated nuclear fuel.\nTraditional thermal conductivity modeling approaches rely on existing\ntheoretical frameworks that describe known, relevant phenomena that govern the\nmicrostructural evolution processes during neutron irradiation (such as\nrecrystallization, and pore size, distribution and morphology). Current\nempirical modeling approaches, however, do not represent all irradiation test\ndata well. Here, we develop a machine learning approach to thermal conductivity\nmodeling that does not require a priori knowledge of a specific material\nmicrostructure and system of interest. Our approach allows researchers to probe\ndependency of thermal conductivity on a variety of reactor operating and\nmaterial conditions. The purpose of building such a model is to allow for\nimproved predictive capabilities linking\nstructure-property-processing-performance relationships in the system of\ninterest (here, irradiated nuclear fuel), which could lead to improved\nexperimental test planning and characterization. The uranium-molybdenum system\nis the fuel system studied in this work, and historic irradiation test data is\nleveraged for model development. Our model achieved a mean absolute percent\nerror of approximately 4% for the validation data set (when a leave-one-out\ncross validation approach was applied). Results indicate our model generalizes\nwell to never before seen data, and thus use of deep learning methods for\nmaterial property predictions from limited, historic irradiation test data is a\nviable approach.\n Thermal conductivity ; Nuclear fuel ; Nuclear engineering ; Materials science ; Irradiation ; Artificial neural network ; Artificial intelligence ; Uranium ; Test data ; Computer science ; Machine learning ; Process engineering ; Engineering ; Nuclear physics ; Composite material ; Metallurgy ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Yu Matsuda | Itsuo Hanasaki | Ryo Iwao | Hiroki Yamaguchi | Tomohide NIIMI
Source: The Proceedings of Mechanical Engineering Congress Japan (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Study on Application of Statistical Machine Learning to Classify Single-Molecule Tracking Data Statistical learning ; Tracking (education) ; Artificial intelligence ; Computer science ; Machine learning ; Statistical analysis ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Psychology Various Chemistry Research Topics ; Advanced Biosensing Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Bethany Halford﻿
Source: C&EN Global Enterprise (article, 2018-02-19)
Field: Materials Science — Materials Chemistry

… and organic reaction performance When chemists develop new types of reactions, they generate a lot of data on what works and how well, along with what doesn’t work at all. Much of the data are never used, says Abigail G. Doyle, a chemistry professor at Princeton University. “We publish only a small fraction and usually only the best results,” she says. Doyle thinks that by using machine learning—in which computer algorithms find patterns in data—it might be possible to use all the data chemists generate to predict the best conditions for a reaction even when the substrate has never been used in that transformation before. Doyle and Princeton’s Derek T. Ahneman and Jesús G. Estrada, along with Merck & Co.’s Spencer D. Dreher and Shishi Lin, take a step in this direction by using machine learning to predict the yield of a Buchwald-Hartwig amination (example shown). Their algorithm allowed for variation in the aryl Publication ; Amination ; Computer science ; Yield (engineering) ; Transformation (genetics) ; Artificial intelligence ; Chemistry ; Organic chemistry ; Law ; Political science ; Thermodynamics ; Physics Machine Learning in Materials Science
Authors: Bethany Halford﻿
Source: C&EN Global Enterprise (article, 2018-02-19)
Field: Materials Science — Materials Chemistry

… and organic reaction performance When chemists develop new types of reactions, they generate a lot of data on what works and how well, along with what doesn’t work at all. Much of the data are never used, says Abigail G. Doyle, a chemistry professor at Princeton University. “We publish only a small fraction and usually only the best results,” she says. Doyle thinks that by using machine learning—in which computer algorithms find patterns in data—it might be possible to use all the data chemists generate to predict the best conditions for a reaction even when the substrate has never been used in that transformation before. Doyle and Princeton’s Derek T. Ahneman and Jesús G. Estrada, along with Merck & Co.’s Spencer D. Dreher and Shishi Lin, take a step in this direction by using machine learning to predict the yield of a Buchwald-Hartwig amination (example shown). Their algorithm allowed for variation in the aryl Publication ; Yield (engineering) ; Amination ; Computer science ; Transformation (genetics) ; Artificial intelligence ; Chemistry ; Algorithm ; Organic chemistry ; Law ; Thermodynamics ; Political science ; Physics Machine Learning in Materials Science
Authors: Peter Zaspel | Bing Huang | Helmut Harbrecht | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2018-08-08)
Field: Materials Science — Materials Chemistry

Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approximations made when calculating molecular energies within quantum chemistry. When combined with quantum machine learning (QML) models, the resulting CQML model is a generalized unified recursive kernel ridge regression which exploits correlations implicitly encoded in training data comprised of multiple levels in multiple dimensions. Here, we have investigated up to three dimensions: Chemical space, basis set, and electron correlation treatment. Numerical results have been obtained for atomization energies of a set of $\sim$7'000 organic molecules with up to 7 atoms (not counting hydrogens) containing CHONFClS, as well as for $\sim$6'000 constitutional isomers of C$_7$H$_{10}$O$_2$. CQML learning curves for atomization energies suggest a dramatic reduction in necessary training samples calculated with the most accurate and costly method. In order to generate milli-second estimates of CCSD(T)/cc-pvdz atomization energies with prediction errors reaching chemical accuracy ($\sim$1 kcal/mol), the CQML model requires only $\sim$100 training instances at CCSD(T)/cc-pvdz level, rather than thousands within conventional QML, while more training molecules are required at lower levels. Our results suggest a possibly favourable trade-off between various hierarchical approximations whose computational cost scales differently with electron number. Quantum machine learning ; Quantum chemistry ; Quantum ; Kernel (algebra) ; Boosting (machine learning) ; Basis set ; Basis (linear algebra) ; Algorithm ; Molecule ; Computer science ; Statistical physics ; Mathematics ; Machine learning ; Quantum algorithm ; Physics ; Quantum mechanics ; Discrete mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Alfonso R.S.
Source: Data Archiving and Networked Services (DANS) (article, 2018-07-17)
Field: Materials Science — Materials Chemistry

Database, weights and biases Database of stacking fault energies in austenitic stainless steels alloys and neural network weights and biases. Database ; Computer science ; Information retrieval Machine Learning in Materials Science ; Surface Treatment and Coatings ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiayue Li | Farren Curtis | Timothy Rose | Christoph Schober | Álvaro Vázquez‐Mayagoitia | Reuter, Karsten | Harald Oberhofer | Noa Marom
Source: arXiv (Cornell University) (article, 2018-03-06)
Field: Chemistry — Spectroscopy

Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases. Statistical physics ; Physics ; Computer science Molecular spectroscopy and chirality ; Crystallization and Solubility Studies ; Crystallography and molecular interactions
Authors: Libor Strakoš | Ondřej Machek | T. Vystavěl | Andreas Schulze | Han Han | Matty Caymax | Richard Young
Source: Proceedings - International Symposium for Testing and Failure Analysis (article, 2018-11-01)
Field: Materials Science — Surfaces, Coatings and Films

Electron Channeling Contrast Imaging for Beyond Silicon Materials Characterization Abstract As semiconductor devices continue to shrink, novel materials (e.g. (Si)Ge, III/V) are being tested and incorporated to boost device performance. Such materials are difficult to grow on Si wafers without forming crystalline defects due to lattice mismatch. Such defects can decrease or compromise device performance. For this reason, non-destructive, high throughput and reliable analytical techniques are required. In this paper Electron Channeling Contrast Imaging (ECCI), large area mapping and defect detection using deep learning are combined in an analytical workflow for the characterization of the defectivity of “beyond Silicon” materials. Such a workflow addresses the requirements for large areas 10-4 cm2 with defect density down to 104 cm-2. Characterization (materials science) ; Wafer ; Silicon ; Materials science ; Workflow ; Semiconductor ; Optoelectronics ; Throughput ; Nanotechnology ; Electron ; Engineering physics ; Computer science ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques