jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lee A. Griffin | Iaroslav Gaponenko | Nazanin Bassiri‐Gharb
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-04-06)
Field: Materials Science — Materials Chemistry

Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films This repository contains the data and code used in the corresponding study. Note: This study is based on previously reported data[1,2], which is contained in Data.mat and loop_1.mat. Data.mat and loop_1.mat are uploaded here for ease of access and are completely unaltered from how they were provided by the original authors. The data files are available from the original authors on Zenodo[3,4]. The abstract for this work is copied here: "Machine learning techniques are more and more often applied to the analysis of complex behaviors in materials research. Frequently used to identify fundamental behaviors within large and multidimensional datasets, these techniques are strictly based on mathematical models. Thus, without inherent physical or chemical meaning or constraints, they are prone to biased interpretation. Here, we demonstrate that, through physical insights and careful data handling, the interpretability of machine learning results in materials science, and specifically materials functionalities, can be vastly improved. The use of techniques such as dimensional stacking can provide the much needed physical and chemical constraints, while proper understanding of the assumptions imposed by model parameters can help avoid over-interpretation. These concepts are applied to recently reported ferroelectric switching experiments in PZT thin films. Through systematic analysis and introduction of physical constraints, we argue that the behaviors present are not necessarily due to exotic mechanisms, as previously suggested, but rather well described by classical ferroelectric switching superimposed by non-ferroelectric phenomena, such as electrochemical deformation, electrostatic interactions or charge injection." [1] Agar et. al. J. C. Agar, Y. Cao, B. Naul, S. Pandya, S. van der Walt, A. I. Luo, J. T. Maher, N. Balke,S. Jesse, S. V. Kalinin, R. K. Vasudevan, and L. W. Martin, Advanced Materials30, 1800701(2018).<br> [2] J. C. Agar, B. Naul, S. Pandya, S. van der Walt, J. Maher, Y. Ren, L.-Q. Chen, S. V. Kalinin,R. K. Vasudevan, Y. Cao, J. S. Bloom, and L. W. Martin, Nature Communications10, 4809(2019).<br> [3] J. C. Agar, JAgar, Y. Cao, B. Naul, S. Pandya, S. van der Walt, A. I. Luo, J. T. Maher,N. Balke, S. Jesse, S. V. Kalinin, R. K. Vasudevan, and L. W. C. Martin, (2018), 10.5281/ZEN-ODO.1242656<br> [4] J. C. Agar, B. Naul, S. Pandya, S. van der Walt, R. Yao, J. Maher, J. Neaton, S. Kalinin,R. Vasudevan, Y. Cao, J. Bloom, and L. Martin, (2018), 10.5281/ZENODO.1478089 Ferroelectricity ; Materials science ; Thin film ; Optoelectronics ; Computer science ; Engineering physics ; Nanotechnology ; Engineering ; Dielectric Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Alberto Fabrizio | Benjamin Meyer | Clémence Corminbœuf
Source: Open MIND (preprint, 2020-03-25)
Field: Materials Science — Materials Chemistry

Learning the energy curvature versus particle number in approximate density functionals The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition. Curvature ; Density functional theory ; Statistical physics ; Context (archaeology) ; Range (aeronautics) ; Applied mathematics ; Mathematics ; Physics ; Quantum mechanics ; Materials science ; Geometry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Saaketh Desai | Samuel Temple Reeve | James Belak
Source: arXiv (Cornell University) (preprint, 2020-01-31)
Field: Materials Science — Materials Chemistry

Implementing a neural network interatomic model with performance\n portability for emerging exascale architectures The two main thrusts of computational science are more accurate predictions\nand faster calculations; to this end, the zeitgeist in molecular dynamics (MD)\nsimulations is pursuing machine learned and data driven interatomic models,\ne.g. neural network potentials, and novel hardware architectures, e.g. GPUs.\nCurrent implementations of neural network potentials are orders of magnitude\nslower than traditional interatomic models and while looming exascale computing\noffers the ability to run large, accurate simulations with these models,\nachieving portable performance for MD with new and varied exascale hardware\nrequires rethinking traditional algorithms, using novel data structures, and\nlibrary solutions. We re-implement a neural network interatomic model in\nCabanaMD, an MD proxy application, built on libraries developed for performance\nportability. Our implementation shows significantly improved on-node scaling in\nthis complex kernel as compared to a current LAMMPS implementation, across both\nstrong and weak scaling. Our single-source solution results in improved\nperformance in many cases, with thread-scalability enabling simulations up to\n21 million atoms on a single CPU node and 2 million atoms on a single GPU. We\nalso explore parallelism and data layout choices (using flexible data\nstructures called AoSoAs) and their effect on performance, seeing up to ~25%\nand ~10% improvements in performance on a GPU simply by choosing the right\nlevel of parallelism and data layout, respectively.\n Computer science ; Software portability ; Scalability ; Petascale computing ; Exascale computing ; Parallel computing ; Artificial neural network ; Data parallelism ; Scaling ; Computational science ; Programming paradigm ; Node (physics) ; Supercomputer ; Thread (computing) ; Parallelism (grammar) ; Computer architecture ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Parallel Computing and Optimization Techniques
Authors: Giovanni Antonelli | Rosa Arboretti | Riccardo Ceccato | Paolo Centomo | Luca Pegoraro | Luigi Salmaso | Marco Zecca
Source: arXiv (Cornell University) (preprint, 2020-06-01)
Field: Chemistry — Analytical Chemistry

A Combined Approach To Detect Key Variables In Thick Data Analytics In machine learning one of the strategic tasks is the selection of only significant variables as predictors for the response(s). In this paper an approach is proposed which consists in the application of permutation tests on the candidate predictor variables in the aim of identifying only the most informative ones. Several industrial problems may benefit from such an approach, and an application in the field of chemical analysis is presented. A comparison is carried out between the approach proposed and Lasso, that is one of the most common alternatives for feature selection available in the literature. Key (lock) ; Analytics ; Computer science ; Data science ; Data analysis ; Data mining ; Computer security Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Gerardo Raggi | Ignacio Fdez. Galván | Christian L. Ritterhoff | Morgane Vacher | Roland Lindh
Source: ChemRxiv (preprint, 2020-04-27)
Field: Materials Science — Materials Chemistry

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected. Hessian matrix ; Kriging ; Mathematical optimization ; Mathematics ; Variance (accounting) ; Optimization problem ; Data point ; Algorithm ; Function (biology) ; Flexibility (engineering) ; Eigenvalues and eigenvectors ; Computer science ; Applied mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Gerardo Raggi | Christian L. Ritterhoff | Ignacio Fdez. Galván | Morgane Vacher | Roland Lindh
Source: ChemRxiv (preprint, 2020-03-13)
Field: Materials Science — Materials Chemistry

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected. Hessian matrix ; Mathematics ; Kriging ; Variance (accounting) ; Mathematical optimization ; Function (biology) ; Flexibility (engineering) ; Optimization problem ; Data point ; Algorithm ; Surrogate model ; Applied mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Stefano Rini | Hirotsugu Hiramatsu
Source: arXiv (Cornell University) (preprint, 2020-11-15)
Field: Chemistry — Analytical Chemistry

An efficient label-free analyte detection algorithm for time-resolved spectroscopy Time-resolved spectral techniques play an important analysis tool in many contexts, from physical chemistry to biomedicine. Customarily, the label-free detection of analytes is manually performed by experts through the aid of classic dimensionality-reduction methods, such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). This fundamental reliance on expert analysis for unknown analyte detection severely hinders the applicability and the throughput of these such techniques. For this reason, in this paper, we formulate this detection problem as an unsupervised learning problem and propose a novel machine learning algorithm for label-free analyte detection. To show the effectiveness of the proposed solution, we consider the problem of detecting the amino-acids in Liquid Chromatography coupled with Raman spectroscopy (LC-Raman). Analyte ; Dimensionality reduction ; Principal component analysis ; Non-negative matrix factorization ; Computer science ; Curse of dimensionality ; Artificial intelligence ; Machine learning ; Raman spectroscopy ; Matrix decomposition ; Pattern recognition (psychology) ; Algorithm ; Chemistry ; Chromatography ; Eigenvalues and eigenvectors ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Benyamin Motevalli | Baichuan Sun | Amanda S. Barnard
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning Machine learning is a powerful way of uncovering hidden structure/property relationships in nanoscale materials, and it is tempting to assign structural causes to properties based on feature rankings reported by interpretable models. In this study of defective graphene oxide nanoflakes, we use classification, regression, and causal inference to show that not all important structural features directly influence the concentration of broken bonds, as a representative property. We find that while the presence of oxygen is important for actual bond breakage the presence and distribution of hydrogen determines how often bond breakage occurs. Breakage ; Graphene ; Oxide ; Chemistry ; Property (philosophy) ; Hydrogen bond ; Nanostructure ; Nanoscopic scale ; Artificial intelligence ; Nanotechnology ; Feature (linguistics) ; Machine learning ; Chemical physics ; Statistical physics ; Molecule ; Computer science ; Materials science ; Organic chemistry ; Physics ; Composite material Machine Learning in Materials Science
Authors: Felix Mayr | Alessio Gagliardi
Source: arXiv (Cornell University) (preprint, 2020-10-20)
Field: Materials Science — Materials Chemistry

Global Property Prediction: A Benchmark Study on Open-Source,\nPerovskite-like Datasets Screening combinatorial space for novel materials - such as perovskite-like\nones for photovoltaics - has resulted in a high amount of simulated\nhigh-troughput data and analysis thereof. This study proposes a comprehensive\ncomparison of structural-fingerprint based machine-learning models on seven\nopen-source databases of perovskite-like materials to predict bandgaps and\nenergies. It shows that none of the given methods are able to capture arbitrary\ndatabases evenly, while underlining that commonly used metrics are highly\ndatabase dependent in typical workflows. In addition the applicability of\nvariance selection and autoencoders to significantly reduce fingerprint size\nindicates that models built with common fingerprints only rely on a submanifold\nof the available fingerprint space.\n Benchmark (surveying) ; Fingerprint (computing) ; Computer science ; Workflow ; Open source ; Variance (accounting) ; Property (philosophy) ; Data mining ; Photovoltaics ; Perovskite (structure) ; Submanifold ; Space (punctuation) ; Selection (genetic algorithm) ; Artificial intelligence ; Database ; Mathematics ; Engineering ; Photovoltaic system ; Cartography ; Software Machine Learning in Materials Science ; Expert finding and Q&A systems
Authors: Yousef Abbaspour‐Gilandeh | Sajad Sabzi | Farzad Azadshahraki | Rouhollah Karimzadeh | Elham Ilbeygi | Juan Ignacio Arribas
Source: Tarım Bilimleri Dergisi (article, 2020-08-21)
Field: Chemistry — Analytical Chemistry

Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92±0.040 and 0.89±0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result. Partial least squares regression ; Perceptron ; Cultivar ; Artificial neural network ; Mathematics ; Content (measure theory) ; Computer science ; Artificial intelligence ; Biological system ; Botany ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Hyun‐Taek Lee | Jonathan Landy | Jaeup U. Kim | YongSeok Jho
Source: Journal of the Korean Physical Society (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Local Dynamics of Excitations in Glassy Liquids In this study, we propose new elementary processes of the excitation dynamics in glassy liquids, which trigger the structural rearrangement in a local and isolated region known as the cooperative rearrangement region. We identify the existence of primary particles which facilitate local dynamics by using a machine learning technique and a time series analysis. The local soft domains are established by a few soft particles that hop between local energy basins. While particles in contact with these primary soft particles respond non-linearly, other particles not in direct contact with them respond linearly as elastic materials. Dynamics (music) ; Excitation ; Local structure ; Materials science ; Chemical physics ; Energy landscape ; Statistical physics ; Physics ; Thermodynamics ; Quantum mechanics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation
Authors: Miguel Angel Carmona | Juan Álvaro Gallego | Alfonso Martinez
Source: PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (article, 2020-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Method for Accurately Predicting Core Losses Using Deep Learning A new method for predicting ferrite properties, particularly core loss density, is proposed. Through the usage of this method, core loss in power transformers and inductors can be predicted with little use of measuring equipment, only in the training phase. Improvement on these predictions is done with the measurement at specific points with the help of deep learning methods. The architecture is described and the loss versus frequency, temperature, and peak magnetic field graphs from a ferrite material are processed. Finally, an inductor is built and measured, and its loss compared with the one predicted by the proposed method. Ferrite core ; Transformer ; Inductor ; Power loss ; Ferrite (magnet) ; Magnetic core ; Computer science ; Deep learning ; Electronic engineering ; Electrical engineering ; Materials science ; Engineering ; Artificial intelligence ; Electromagnetic coil Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Yuzhi Xu | Cheng‐Wei Ju | Bo Li | Qiu-Shi Ma | Lianjie Zhang | Junwu Chen
Source: ChemRxiv (preprint, 2020-11-19)
Field: Materials Science — Materials Chemistry

Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models Alternating conjugated copolymers have been regarded as promising candidates for photocatalytic hydrogen evolution due to the adjustability of their molecular structures and electronic properties. In this work, machine learning (ML) models with molecular fingerprint of segment descriptors (SD) have been successfully constructed to promote the accurate and universal prediction of electronic properties such as electron affinity, ionization potential and optical bandgap. Moreover, without any experimental values, a high-performance prediction classifier model toward photocatalytic hydrogen production of alternating copolymers has been developed with high accuracy (real-test accuracy = 0.91). Consequently, our results demonstrate accurate regression and classification models to disclose valuable influencing factors concerning hydrogen evolution rate (HER) of alternating copolymers. Copolymer ; Conjugated system ; Photocatalysis ; Materials science ; Hydrogen ; Band gap ; Ionization ; Computer science ; Biological system ; Polymer ; Optoelectronics ; Chemistry ; Organic chemistry ; Catalysis ; Ion Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Photocatalysis Techniques