jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James L. McDonagh | Ardita Shkurti | David J. Bray | R. L. Anderson | Edward O. Pyzer‐Knapp
Source: ChemRxiv (preprint, 2019-07-17)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields This work demonstrates the use of open literature data to force field paramterization via a novel approach applying Bayesian optimization. We have selected Dissipative Particle Dynamics (DPD) as the simulation method in this proof-of-concept work. Dissipative particle dynamics ; Force field (fiction) ; Computer science ; Bayesian probability ; Work (physics) ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Mathematics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Lucas K. Wagner
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Bridging simulations and theories of correlated electron materials using ideas from machine learning Bridging (networking) ; Electron ; Computer science ; Theoretical physics ; Physics ; Statistical physics ; Artificial intelligence ; Cognitive science ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2019-04-06)
Field: Materials Science — Materials Chemistry

Quantum layer boosts machine-learning predictions Computational chemistry lets chemists predict molecules’ properties without measuring them in the lab. Some of the most accurate computational chemistry tools use quantum chemistry, but these calculations can be time consuming. At the American Chemical Society national meeting in Orlando, Florida last week, David J. Yaron of Carnegie Mellon University described a way to speed up calculation of molecular properties by combining quantum chemistry with machine learning. By adding a quantum chemistry “layer” to a machine-learning algorithm to predict the dipole, atomic charge, and other properties of a molecule, Yaron said, his team was able to reduce calculation errors by as much as two-thirds relative to standard machine-learning algorithms. Yaron said he wants chemists to be able to model organic molecules quickly without sacrificing accuracy. When calculations have to run overnight or over a weekend it can slow down discovery, he said. Typically, machine-learning algorithms can predict an unknown molecule’s Layer (electronics) ; Quantum machine learning ; Quantum ; Computer science ; Artificial intelligence ; Physics ; Materials science ; Nanotechnology ; Quantum computer ; Quantum mechanics Machine Learning in Materials Science
Authors: Hideyuki Jippo | Ryota Kikuchi | Tatsuru Matsuo | Daisuke Fukuda | Azuma Matsuura | Mari Ohfuchi
Source: The Japan Society of Applied Physics (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

Machine learning of graph for chemical substances using atom/bond types in force fields Materials science ; Atom (system on chip) ; Chemical physics ; Graph ; Nanotechnology ; Computational chemistry ; Computer science ; Theoretical computer science ; Parallel computing ; Physics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Paul Arindam | Dipendra Jha | Reda Al-Bahrani | Liao Wei-keng | Choudhary Alok | Ankit Agrawal
Source: arXiv (Cornell University) (preprint, 2019-03-07)
Field: Materials Science — Materials Chemistry

Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell\n Screening Organic Solar Cells are a promising technology for solving the clean energy\ncrisis in the world. However, generating candidate chemical compounds for solar\ncells is a time-consuming process requiring thousands of hours of laboratory\nanalysis. For a solar cell, the most important property is the power conversion\nefficiency which is dependent on the highest occupied molecular orbitals (HOMO)\nvalues of the donor molecules. Recently, machine learning techniques have\nproved to be very useful in building predictive models for HOMO values of donor\nstructures of Organic Photovoltaic Cells (OPVs). Since experimental datasets\nare limited in size, current machine learning models are trained on data\nderived from calculations based on density functional theory (DFT). Molecular\nline notations such as SMILES or InChI are popular input representations for\ndescribing the molecular structure of donor molecules. The two types of line\nrepresentations encode different information, such as SMILES defines the bond\ntypes while InChi defines protonation. In this work, we present an ensemble\ndeep neural network architecture, called SINet, which harnesses both the SMILES\nand InChI molecular representations to predict HOMO values and leverage the\npotential of transfer learning from a sizeable DFT-computed dataset- Harvard\nCEP to build more robust predictive models for relatively smaller HOPV\ndatasets. Harvard CEP dataset contains molecular structures and properties for\n2.3 million candidate donor structures for OPV while HOPV contains DFT-computed\nand experimental values of 350 and 243 molecules respectively. Our results\ndemonstrate significant performance improvement from the use of transfer\nlearning and leveraging both molecular representations.\n Leverage (statistics) ; Organic solar cell ; Artificial neural network ; Artificial intelligence ; Computer science ; Deep learning ; Transfer of learning ; Molecular orbital ; Machine learning ; ENCODE ; Photovoltaic system ; Molecule ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yasuyoshi Kurokawa | Takeshi Aoki | Munehiro Kozuma | Hajime Kimura | Shunpei Yamazaki
Source: The Japan Society of Applied Physics (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

A Crystalline Oxide Semiconductor-FET/Si-FET Hybrid Structure-based Multiplier for Artificial Neural Network Applications Materials science ; Semiconductor ; Artificial neural network ; Oxide ; Multiplier (economics) ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Metallurgy Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; Semiconductor Quantum Structures and Devices
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Synthesis-planning program relies on human-machine partnership Computer-aided synthesis-planning (CASP) programs aim to replicate what synthetic chemists do when tackling a synthesis: start with a target molecule and then work backward to trace a synthetic route, including an efficient and achievable series of reactions and reagents. Work in this field goes back 50 years, but successful examples have emerged only in the past several years. These rely either on chemistry rules written by human chemists or on machine-learning algorithms that have assimilated synthesis knowledge from databases of reactions. Researchers now report that one CASP program that combines human knowledge and machine learning performs better than one that uses only artificial intelligence, particularly for synthetic routes involving rarely used reactions (Angew. Chem., Int. Ed. 2019, DOI: 10.1002/anie.201912083). The program is an update to Chematica, which was developed by Bartosz A. Grzybowski of the Ulsan National Institute of Science and Technology and the Polish Academy of Sciences and is CASP ; Computer science ; Artificial intelligence ; General partnership ; Program synthesis ; Data science ; Chemistry ; Programming language ; Protein structure prediction Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Julie M. Nicely | B. N. Duncan | T. F. Hanisco | Glenn M. Wolfe | R. J. Salawitch | Makoto Deushi | O. A. Søvde | Patrick Jöckel | Béatrice Josse | Douglas E. Kinnison | Andrew Klekociuk | Michael Manyin | Virginie Marécal | Olaf Morgenstern | Lee T. Murray | Gunnar Myhre | Luke D. Oman | Giovanni Pitari | Andrea Pozzer | Ilaria Quaglia | Laura E. Revell | Eugene Rozanov | Andrea Stenke | Kane A. Stone | S. E. Strahan | Simone Tilmes | Holger Tost | Daniel M. Westervelt | Guang Zeng
Source:  (preprint, 2019-09-05)
Field: Chemistry — Organic Chemistry

Supplementary material to "A Machine Learning Examination of Hydroxyl Radical DifferencesAmong Model Simulations for CCMI-1" Chemistry ; Hydroxyl radical ; Radical ; Computational chemistry ; Organic chemistry Free Radicals and Antioxidants ; Electron Spin Resonance Studies
Authors: Tadokoro Daiki
Source: The Japan Institute of Metals and Materials (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Relationship between Microstructure and Mechanical properties of Ti-17 alloy for Aerospace Application and Machine Learning Microstructure ; Aerospace ; Alloy ; Metallurgy ; Materials science ; Titanium alloy ; Engineering ; Aerospace engineering Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Surface Treatment and Coatings
Authors: Yefan Tian | Yuhao Wang | Joseph Ross | Raymundo Arróyave
Source:  (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization Computer science ; Stochastic optimization ; Materials science ; Mathematical optimization ; Mathematics Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Non-Destructive Testing Techniques
Authors: Can Jozef Saul | Ayse Zeynep Kamis | Egemen Sahin
Source: 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Carbon Nanotube Coordinate Prediction with Deep Learning The development of carbon nanotube technology has provided a great advantage in applications of many fields including nanotechnology and materials science due to the exquisite mechanical, chemical, thermal and electrical properties of carbon nanotubes. However, due to their size, the scale at which the physical phenomena of carbon nanotubes are apparent is too small to do physical experiments, there is a need for certain computational methods like molecular dynamics simulations. In this present study, we propose a deep learning methodology, alongside a custom data preprocessing method, for precisely determining carbon nanotubes' coordinates. We experimented with various topologies of neural networks and acquired a top result of 81.34%. Our findings and computation method surpasses the previous work on this field, in terms accuracy and computational time. Carbon nanotube ; Computer science ; Computation ; Nanotechnology ; Materials science ; Artificial neural network ; Artificial intelligence ; Computational science ; Algorithm Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Neural Networks and Applications
Authors: Naomie Salim
Source:  (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Deep Learning Approaches for Big Data Analysis Good representations of data eliminate irrelevant variability of the input data, while preserving the information that is useful for the ultimate task. Among the various ways for learning representation is using deep learning methods. Deep feature hierarchies are formed by stacking unsupervised modules on top of each other, forming multiple nonlinear transformations to produce better representations. In this talk, we will first show how deep learning is used for bioactivity prediction of chemical compounds. Molecules are represented as several convolutional neural networks to predict their bioactivity. In addition, a new concept of merging multiple convolutional neural networks and an automatic learning features representation for the chemical compounds was proposed using the values within neurons of the last layer of the CNN architecture. We will also show how the concepts of deep learning is adapted into a deep belief network (DBN) to enhance the molecular similarity searching. The DBN achieves feature abstraction by reconstruction weight for each feature and minimizing the reconstruction error over the whole feature set. The DBN is later enhanced using data fusion to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors. Secondly, we will show how we used deep learning for stock market prediction. Here, we developed a Deep Long Short Term Memory Network model that is able to forecast the crude palm oil price movement with combined factors such as other commodities prices, weather and news sentiments and price movement of crude palm oil. We also show how we combined stock markets price and financial news and deployed the Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), and Word 2 Vector (Word2Vec) to project the stock prices for the following seven days. Finally, we will show how we exploited deep learning method for the opinion mining and later used it to extract the product's aspects from the user textual review for recommendation systems. Specifically, we employ a multichannel convolutional neural network (MCNN) for two different input layers, namely, word embedding layer and Part-of-speech (POS) tag embedding layer. We show effectiveness of the proposed model in terms of both aspect extraction and rating prediction performance. Deep learning ; Deep belief network ; Artificial intelligence ; Computer science ; Convolutional neural network ; Feature learning ; Machine learning ; Sentiment analysis ; External Data Representation ; Feature (linguistics) ; Artificial neural network ; Pattern recognition (psychology) Machine Learning in Materials Science ; Market Dynamics and Volatility ; Stock Market Forecasting Methods
Authors: Paolo Valentini | Maninder S. Grover | Eswar Josyula
Source: AIAA Aviation 2019 Forum (article, 2019-06-15)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Potential Energy Surface for Direct Molecular Simulation of Nonequilibrium Gas Flows Artificial neural network ; Non-equilibrium thermodynamics ; Computer science ; Energy (signal processing) ; Surface (topology) ; Statistical physics ; Physics ; Artificial intelligence ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Phase Equilibria and Thermodynamics
Authors: Tomokazu Yamamoto | Kohei Aso | Syo Matsumura
Source: Microscopy (article, 2019-11-01)
Field: Materials Science — Surfaces, Coatings and Films

PM-11 Atom Location Analysis on Atomic-resolution STEM Images of Metal Nanoparticles by Convolutional Neural Network Approach Metallic nanoparticles have attracted much attention as catalysts for various chemical reactions. Recently, aberrationcorrected STEM imaging was often used for atomistic characterization of the metallic nanoparticles because their catalytic properties depends on their sizes, shapes, crystal structure and presence of lattice defects such as staking faults and twins. The manual analyses for many atomic-resolution STEM images of nanoparticles are sometimes necessary for statistics and it is very time-consuming. Automation of image analysis is desirable in this purpose. Recently, successful object recognition and classification for complex natural images have been achieved for using convolutional neural network (CNN) framework In this study, we tried automated detection of nanoparticles in STEM images using trained CNNs implemented in deep-learning toolbox of MATLAB software. The CNNs were trained using a simulated images set of a few-nm sized nanoparticles generated with the Gaussian atom model After nanoparticle detection process using CNNs, we also fitted Gaussian functions for atomic columns of each nanoparticle to identify the atom positions. The established CNNs were able to distinguish nanoparticle regions from background region in simulated and experimental STEM images with small number of errors and initial atom column positions were generated successfully for further analyses. Convolutional neural network ; Nanoparticle ; Atom (system on chip) ; Resolution (logic) ; Metal ; Materials science ; Computer science ; Chemistry ; Nanotechnology ; Artificial intelligence ; Metallurgy ; Embedded system Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Yuki K. Wakabayashi | Yoshiharu Krockenberger | Naoto Tsujimoto | Takuma Otsuka | Tommy Boykin | Hiroshi Sawada | Shinji Tsuneyuki | Yoshitaka Taniyasu | Hideki Yamamoto
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Molecular beam epitaxy and utilization of machine learning for 4 d and 5 d ferromagnetic oxides Materials science ; Molecular beam epitaxy ; Ferromagnetism ; Epitaxy ; Nanotechnology ; Condensed matter physics ; Physics Machine Learning in Materials Science
Authors: Richard G. Hennig
Source: Data Archiving and Networked Services (DANS) (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Li-Ge data for: Machine Learning of Ab-initio Energy Landscapes for Crystal Structure Predictions Evolutionary algorithm structure search for Li-Ge crystal structures. W. W. Tipton, C. A. Mathulis, and R. G. Hennig. Ab-initio prediction of the Li5Ge2 Zintl compound. Comp. Mater. Sci. 93, 133 (2014) Ab initio ; Crystal structure ; Crystal structure prediction ; Ab initio quantum chemistry methods ; Energy landscape ; Crystallography ; Artificial intelligence ; Materials science ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: ChemRxiv (preprint, 2019-12-23)
Field: Materials Science — Materials Chemistry

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ωB97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation---for example, by enabling the development of novel machine learning models for quantitative reaction prediction. Transition state ; Reaction mechanism ; Chemistry ; Branching (polymer chemistry) ; Chemical reaction ; Atom (system on chip) ; Elementary reaction ; Quantum chemistry ; Quantum ; Computational chemistry ; Quantum chemical ; Potential energy surface ; Reaction rate ; Computer science ; Molecule ; Catalysis ; Physics ; Organic chemistry ; Kinetics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Marc S. Lavine
Source: Science (article, 2019-09-05)
Field: Materials Science — Materials Chemistry

Efficient sampling of equilibrium states Machine Learning Molecular dynamics or Monte Carlo methods can be used to sample equilibrium states, but these methods become computationally expensive for complex systems, where the transition from one equilibrium state to another may only occur through rare events. Noe et al. used neural networks and deep learning to generate distributions of independent soft condensed-matter samples at equilibrium (see the Perspective by Tuckerman). Supervised training is used to construct invertible transformations between the coordinates of the complex system of interest and simple Gaussian coordinates of the same dimensionality. Thus, configurations can be sampled in this simpler coordinate system and then transformed back into the complex one using the correct statistical weighting. Science , this issue p. [eaaw1147][1]; see also p. [982][2] [1]: /lookup/doi/10.1126/science.aaw1147 [2]: /lookup/doi/10.1126/science.aay2568 Sampling (signal processing) ; Mathematical economics ; Economics ; Computer science ; Telecommunications Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Stefan Krämer | Denis Gebele | Atif Raza | OpenRiskNet
Source: Faculty of 1000 Research Ltd (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

OpenRiskNet Part IV: WEKA machine learning services for the prediction of half-lifes of chemicals and nanoparticle transport Open peer review ; Plant biology ; Artificial intelligence ; Physiology ; Machine learning ; Computer science ; Medicine ; Biomedical engineering ; Biology ; Botany History and advancements in chemistry
Authors: Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2019-04-18)
Field: Materials Science — Materials Chemistry

Data-driven Discovery of Photoactive Quaternary Oxides using First-principles Machine Learning We present a low-cost, virtual high-throughput materials design workflow and use it to identify earth-abundant materials for solar energy applications from the quaternary oxide chemical space. A statistical model that predicts bandgap from chemical composition is built using supervised machine learning. The trained model forms the first in a hierarchy of screening steps. An ionic substitution algorithm is used to assign crystal structures, and an oxidation state probability model is used to discard unlikely chemistries. We demonstrate the utility of this process for screening over 1 million oxide compositions. We find that, despite the difficulties inherent to identifying stable multi-component inorganic materials, several compounds produced by our workflow are calculated to be thermodynamically stable or metastable and have desirable optoelectronic properties according to first-principles calculations. The predicted oxides are Li2MnSiO5, MnAg(SeO3)2 and two polymorphs of MnCdGe2O6, all four of which are found to have direct electronic bandgaps in the visible range of the solar spectrum. Workflow ; Band gap ; Metastability ; Ionic bonding ; Oxide ; Computer science ; Characterization (materials science) ; Materials science ; Hierarchy ; Chemical space ; Chemistry ; Nanotechnology ; Optoelectronics ; Drug discovery Machine Learning in Materials Science
Authors: Zhilei Wang
Source: The Japan Institute of Metals and Materials (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Processing optimization and property predictions of hot-extruded Bi-Te-Se thermoelectric materials via machine learning Property (philosophy) ; Thermoelectric effect ; Materials science ; Mechanical engineering ; Computer science ; Artificial intelligence ; Engineering ; Physics ; Thermodynamics ; Philosophy ; Epistemology Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Advanced Thermoelectric Materials and Devices