jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuangjia Zheng | Jiahua Rao | Zhongyue Zhang | Jun Xu | Yuedong Yang
Source: ChemRxiv (preprint, 2019-07-03)
Field: Materials Science — Materials Chemistry

Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks &lt;p&gt;&lt;a&gt;Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases &gt;21% over other deep learning methods, and &gt;6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.&lt;/a&gt;&lt;/p&gt; Retrosynthetic analysis ; Transformer ; Artificial neural network ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Machine translation ; Machine learning ; Engineering ; Chemistry ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Christopher Sutton | Mario Boley | Luca M. Ghiringhelli | Matthias Rupp | Jilles Vreeken | Matthias Scheffler
Source: ChemRxiv (preprint, 2019-12-23)
Field: Materials Science — Materials Chemistry

Identifying Domains of Applicability of Machine Learning Models for Materials Science We present an extension to the usual machine learning process that allows for the identification of the domain of applicability of a fitted model, i.e., the region in its domain where it performs most accurately. This approach is applied to several vastly different but commonly used materials representations (namely the n-gram approach, SOAP, and the many body tenor representation), which are practically indistinguishable based on performance using a single error statistic. Moreover, these models appear unsatisfactory for screening applications as they fail to reliably identify the ground state polymorphs. When applying our newly developed analysis for each of the models, we can identify the domain of applicability for each model according to a simple set of interpretable conditions. We show that identification of the domain of applicability in the prediction of the formation energy enables a more accurate ground-state search - a crucial step for the discovery of novel materials. Computer science ; Statistic ; Process (computing) ; Identification (biology) ; Domain (mathematical analysis) ; Machine learning ; Simple (philosophy) ; Representation (politics) ; Artificial intelligence ; Set (abstract data type) ; Extension (predicate logic) ; Data mining ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: WooSeok Jeong | Samuel J. Stoneburner | Daniel King | Ruye Li | Andrew Walker | Roland Lindh | Laura Gagliardi
Source: ChemRxiv (preprint, 2019-12-20)
Field: Materials Science — Materials Chemistry

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied. Complete active space ; Chemical space ; Computer science ; Atomic orbital ; Space (punctuation) ; Dissociation (chemistry) ; Density functional theory ; Chemical bond ; Bond order ; Active learning (machine learning) ; Configuration space ; Bond length ; Chemistry ; Artificial intelligence ; Computational chemistry ; Quantum mechanics ; Physics ; Electron ; Molecule ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Benjamin Provencher | Nicolas Piché | Mike Marsh
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Simplifying and Streamlining Large-Scale Materials Image Processing with Wizard-Driven and Scalable Deep Learning Journal Article Simplifying and Streamlining Large-Scale Materials Image Processing with Wizard-Driven and Scalable Deep Learning Get access Benjamin Provencher, Benjamin Provencher Object Research Systems. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Nicolas Piché, Nicolas Piché Object Research Systems. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Mike Marsh Mike Marsh Object Research Systems. Denver, USA Corresponding author: mmarsh@theobjects.com Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 402–403, https://doi.org/10.1017/S1431927619002745 Published: 01 August 2019 Wizard ; Scalability ; Object (grammar) ; Scale (ratio) ; Computer science ; Library science ; Data science ; World Wide Web ; Art history ; Artificial intelligence ; History ; Cartography ; Database ; Geography Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Eldhose Iype | Siddhaling Urolagin
Source: The Journal of Chemical Physics (article, 2019-01-10)
Field: Materials Science — Materials Chemistry

Machine learning model for non-equilibrium structures and energies of simple molecules Predicting molecular properties using a Machine Learning (ML) method is gaining interest among research as it offers quantum chemical accuracy at molecular mechanics speed. This prediction is performed by training an ML model using a set of reference data [mostly Density Functional Theory (DFT)] and then using it to predict properties. In this work, kernel based ML models are trained (using Bag of Bonds as well as many body tensor representation) against datasets containing non-equilibrium structures of six molecules (water, methane, ethane, propane, butane, and pentane) to predict their atomization energies and to perform a Metropolis Monte Carlo (MMC) run with simulated annealing to optimize molecular structures. The optimized structures and energies of the molecules are found to be comparable with DFT optimized structures, energies, and forces. Thus, this method offers the possibility to use a trained ML model to perform a classical simulation such as MMC without using any force field, thereby improving the accuracy of the simulation at low computational cost. Density functional theory ; Force field (fiction) ; Simulated annealing ; Computer science ; Statistical physics ; Monte Carlo method ; Molecular dynamics ; Molecule ; Simple (philosophy) ; Representation (politics) ; Work (physics) ; Butane ; Kernel (algebra) ; Algorithm ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence ; Thermodynamics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mengyao Chen | Ting Wu | Kaixia Xiao | Tanfeng Zhao | Yanmei Zhou | Qingyou Zhang | João Aires‐de‐Sousa
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2019-06-18)
Field: Chemistry — Spectroscopy

Machine learning to predict the specific optical rotations of chiral fluorinated molecules Multilinear map ; Mean squared error ; Quantitative structure–activity relationship ; Root mean square ; Random forest ; Molecular descriptor ; Artificial neural network ; Perceptron ; Enantiomer ; Chemistry ; Artificial intelligence ; Molecule ; Optical rotation ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Statistics ; Stereochemistry ; Physics ; Organic chemistry Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Di Li | Yingguang Li | Jing Zhou | Zhiwei Zhao
Source: Applied Composite Materials (article, 2019-11-13)
Field: Chemistry — Organic Chemistry

A Novel Method to Improve Temperature Uniformity in Polymer Composites Microwave Curing Process through Deep Learning with Historical Data Materials science ; Microwave ; Composite number ; Composite material ; Curing (chemistry) ; Convolutional neural network ; Process engineering ; Computer science ; Artificial intelligence Microwave-Assisted Synthesis and Applications ; Epoxy Resin Curing Processes ; Synthesis and properties of polymers
Authors: Di Chen | Yiwei Bai | Wenting Zhao | Sebastian Ament | John M. Gregoire | Carla P. Gomes
Source: arXiv (Cornell University) (preprint, 2019-06-03)
Field: Materials Science — Materials Chemistry

Deep Reasoning Networks: Thinking Fast and Slow We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving complex tasks, typically in an unsupervised or weakly-supervised setting. DRNets exploit problem structure and prior knowledge by tightly combining logic and constraint reasoning with stochastic-gradient-based neural network optimization. We illustrate the power of DRNets on de-mixing overlapping hand-written Sudokus (Multi-MNIST-Sudoku) and on a substantially more complex task in scientific discovery that concerns inferring crystal structures of materials from X-ray diffraction data under thermodynamic rules (Crystal-Structure-Phase-Mapping). At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module. The structured latent encoding is used by a generative decoder to generate the targeted output. Finally, an overall objective combines responses from the generative decoder (thinking fast) and the reasoning module (thinking slow), which is optimized using constraint-aware stochastic gradient descent. We show how to encode different tasks as DRNets and demonstrate DRNets' effectiveness with detailed experiments: DRNets significantly outperform the state of the art and experts' capabilities on Crystal-Structure-Phase-Mapping, recovering more precise and physically meaningful crystal structures. On Multi-MNIST-Sudoku, DRNets perfectly recovered the mixed Sudokus' digits, with 100% digit accuracy, outperforming the supervised state-of-the-art MNIST de-mixing models. Finally, as a proof of concept, we also show how DRNets can solve standard combinatorial problems -- 9-by-9 Sudoku puzzles and Boolean satisfiability problems (SAT), outperforming other specialized deep learning models. DRNets are general and can be adapted and expanded to tackle other tasks. MNIST database ; Computer science ; Artificial intelligence ; Constraint (computer-aided design) ; ENCODE ; Stochastic gradient descent ; Theoretical computer science ; Gradient descent ; Deep learning ; Machine learning ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Image Retrieval and Classification Techniques
Authors: Vijay L. Barai | S.J. Dhoble
Source: Journal of Luminescence (article, 2019-01-09)
Field: Materials Science — Materials Chemistry

Prediction of excitation wavelength of phosphors by using machine learning model Phosphor ; Excitation ; Lasso (programming language) ; Artificial neural network ; Computer science ; Luminescence ; Wavelength ; Artificial intelligence ; Selection (genetic algorithm) ; Operator (biology) ; Algorithm ; Machine learning ; Materials science ; Optoelectronics ; Chemistry ; Engineering ; Electrical engineering Luminescence Properties of Advanced Materials ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Zhong Yu | Wei Shi | Jun Wei Zhang | Bo Wang | Yu Jiang Wang | Yi Liang | Hao Tian
Source: Computational Materials Science (article, 2019-09-06)
Field: Materials Science — Surfaces, Coatings and Films

High-throughput, algorithmic determination of pore parameters from electron microscopy Throughput ; Porosity ; Repeatability ; Computer science ; Materials science ; Process (computing) ; Microscopy ; Scanning electron microscope ; Characterization (materials science) ; Electron microscope ; Image (mathematics) ; Biological system ; Computational science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Composite material ; Optics ; Physics ; Chromatography Electron and X-Ray Spectroscopy Techniques ; Aluminum Alloy Microstructure Properties ; Anodic Oxide Films and Nanostructures
Authors: TongYi ZHANG | YanLin HE | JiaHao WANG | Sheng Sun
Source: Scientia Sinica Technologica (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the hardness of tool and mold steels <p indent="0mm">Hardness is a major indicator of the quality of tool and mold steels (TMS). Analytic formulas of hardness versus compositions were proposed here by using hierarchical clustering (HC) and LASSO regressions, based on data of 79 brands of TMS. HC presents two large groups of TMS, which contain high and low concentrations of chromium. Then, LASSO regressions were applied on each group and the regression result with the lowest root mean square error in leave-one-out cross-validation was taken out as the theoretical prediction formula of hardness versus chemical composition feature. Furthermore, atomic features were selected from electronegativity, atomic radius, valence electron number, electron affinity, first ionization energy, etc. LASSO regressions of the data with the atomic features give another prediction formula. These results demonstrate the powerful ability of machine learning in the design of TMS. Mold ; Metallurgy ; Materials science ; Computer science ; Composite material Metal Alloys Wear and Properties ; Metallurgical Processes and Thermodynamics ; Microstructure and Mechanical Properties of Steels
Authors: Kazutoshi Miwa | Ryoji Asahi
Source: International Journal of Hydrogen Energy (article, 2019-08-02)
Field: Materials Science — Materials Chemistry

Path integral study on C15-type Laves TiCr2 hydride Path integral formulation ; Hydride ; Type (biology) ; Diffusion ; Path (computing) ; Diffusion Monte Carlo ; Monte Carlo method ; Alloy ; Hydrogen ; Laves phase ; Chemistry ; Quantum ; Atomic physics ; Condensed matter physics ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics ; Hybrid Monte Carlo ; Computer science Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Ryo Tamura | Jianbo Lin | Tsuyoshi Miyazaki
Source: Journal of the Physical Society of Japan (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure We study a generalization performance of the machine learning (ML) model to predict the atomic forces within the density functional theory (DFT). The targets are the Si and Ge single component systems in the liquid state. To train the machine learning model, Gaussian process regression is performed with the atomic fingerprints which express the local structure around the target atom. The training and test data are generated by the molecular dynamics (MD) based on DFT. We first report the accuracy of ML forces when both test and training data are generated from the DFT-MD simulations at a same temperature. By comparing the accuracy of ML forces at various temperatures, it is found that the accuracy becomes the lowest around the phase boundary between the solid and the liquid states. Furthermore, we investigate the transferability of ML models trained in the liquid state to temperature and pressure. We demonstrate that, if the training is performed at a high temperature and if the volume change is not so large, the transferability of ML forces in the liquid state is high enough, while its transferability to the solid state is very low. Transferability ; Generalization ; Gaussian process ; Gaussian ; Process (computing) ; Test data ; Training set ; Kriging ; Regression Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Li Wu | Yi Zhang | Fengxia Wang | Weiquan Ma | Tian Xie | Kama Huang
Source: Materials (article, 2019-02-22)
Field: Chemistry — Organic Chemistry

An On-Line System for High Temperature Dielectric Property Measurement of Microwave-Assisted Sintering Materials Microwave-assisted sintering materials have been proven to deliver improvements in the mechanical and physicochemical properties of the materials, compared with conventional sintering methods. Accurate values of dielectric properties of materials under high temperatures are essential for microwave-assisted sintering. In view of this, this paper, proposes an on-line system to measure the high temperature dielectric properties of materials under microwave processing at a frequency of 2450 MHz. A custom-designed ridge waveguide is utilized, where samples are heated and measured simultaneously. An artificial neural network (ANN) trained with the corresponding simulation data is integrated into this system to reverse the permittivity of the measured materials. This whole system is tested at room temperature with different materials. Accuracies of measuring dielectric property with an error lower than 9% with respect to theoretical data have been achieved even for high loss media. The functionality of the dielectric measurement system has also been demonstrated by heating and measuring Macor and Duran ceramic glass samples up to 800 °C. All the preliminary experiments prove the feasibility of this system. It provides another method for dielectric property measurement and improves the understanding of the mechanism between microwave and media under high temperatures, which is helpful for optimizing the microwave-assisted sintering of materials. Materials science ; Microwave ; Sintering ; Dielectric ; Ceramic ; Permittivity ; Composite material ; Dielectric loss ; System of measurement ; Temperature measurement ; Optoelectronics ; Computer science ; Telecommunications Microwave-Assisted Synthesis and Applications ; Microwave and Dielectric Measurement Techniques ; Microwave Dielectric Ceramics Synthesis
Authors: Wenhao Zhu | Yonglin Xu | Jianyue Ni | Guannan Hu | Xiangmeng Wang | Wu Zhang
Source: Materials Science and Engineering B (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

SEHC: A high-throughput materials computing framework with automatic self-evaluation filtering Throughput ; Pipeline (software) ; Computer science ; Computation ; Supercomputer ; Key (lock) ; Process (computing) ; Distributed computing ; Computer architecture ; Parallel computing ; Operating system ; Algorithm ; Wireless Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Osama M. Irfan | Hanafy M. Omar
Source: Metallurgical and Materials Transactions A (article, 2019-07-02)
Field: Materials Science — Materials Chemistry

Influence of Grain Refinement by ECAP on Mechanical and Erosion Corrosion Properties of AISI 4130 Steel: Experimental and Prediction Approach Materials science ; Metallurgy ; Microstructure ; Grain size ; Ultimate tensile strength ; Severe plastic deformation ; Pressing ; Deformation (meteorology) ; Die (integrated circuit) ; Slurry ; Composite material Metal Alloys Wear and Properties ; Advanced materials and composites ; Erosion and Abrasive Machining
Authors: MN Uddin | Sohan Ahmed | Swapan Kumer Ray | Muhammad Saiful Islam | Ariful Hai Quadery | M. Sarwar Jahan
Source: Nordic Pulp & Paper Research Journal (article, 2019-01-30)
Field: Chemistry — Analytical Chemistry

Method for predicting lignocellulose components in jute by transformed FT-NIR spectroscopic data and chemometrics Abstract In this investigation, a nondestructive technique has been developed for determining chemical composition of jute fiber by chemometric modeling with pretreated FT-NIR spectroscopic data. The chemical composition of jute fibers in wet chemical method were, 58 to 61.80 % α-cellulose, 13.0 to 21.90 % lignin, 9.89 to 16.8 % pentosan and 79.02 to 88.33 % holocellulose. FT-NIR spectral data from range 9000–4000 cm −1 of all jute samples were collected from the instrument. Spectral data of jute samples were pretreated with second order derivatives (SOD), standard normal variate (SNV) techniques and both together were used before calibration. Two chemometric calibration techniques: partial least square regression (PLSR) and artificial neural network (ANN) were assessed for predicting chemical compositions of Jute fibers. Result shows that prediction efficiency ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mtext>R</m:mtext> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {\text{R}^{2}} ) of ANN varies from 72–99 % for calibration, validation and test datasets. However, by PLSR, <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mtext>R</m:mtext> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {\text{R}^{2}} are much higher and consistent than those by earlier one. For α-cellulose, lignin, pentosan and holocellulose <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mtext>R</m:mtext> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {\text{R}^{2}} values hover around 95–99 %. Thereby, a non-destructive, simple and cost effective novel method is being proposed to determine chemical compositions of jute with pretreated FT-NIR spectral data and chemometric calibration techniques. Chemometrics ; Lignin ; Cellulose ; Partial least squares regression ; Calibration ; Analytical Chemistry (journal) ; Mathematics ; Chemical composition ; Materials science ; Chemistry ; Chromatography ; Organic chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Seed and Plant Biochemistry