jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kerisha Williams | Fengyuan Zhang | D.N. Edwards | Aaron B. Naden | Yulian Yao | Sabine M. Neumayer | Amit Kumar | Brian J. Rodriguez | Nazanin Bassiri‐Gharb
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior. This repository contains the data and code used in the corresponding study: Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior. The abstract for the work appears as follows: Scanning Probe Microscopy (SPM) based techniques probe material properties over micrometers wide regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the <em>Off-field</em> piezoresponse, compared to <em>On-field</em> piezoresponse, based on the latter’s increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating <em>both</em> <em>Off-</em> and <em>On-field</em> piezoresponse response as well as <em>Off-field</em> resonance frequency to maximize information, we investigate switching piezoresponse in a defect-rich Pb(Zr,Ti)O<sub>3</sub> thin film. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during <em>On-field</em> measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response Nanoscopic scale ; Computer science ; Artificial intelligence ; Scale (ratio) ; Machine learning ; Psychology ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science
Authors: Rashmi Kapoor | Poonam Upadhyay | Thirmal Chinthakuntta | Ganapavarapu Neeraj Kumar
Source: 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (article, 2021-11-27)
Field: Materials Science — Materials Chemistry

Neuro-Estimation of Physical Properties of BaTiO<sub>3</sub>Ceramic Capacitors for High Power Applications Electronic components are the vital parts of future power system. It is required to develop high power sustainable electronic materials for capacitors, transducers, sensors etc. Good dielectric property of Barium Titanate makes it an important material for electronic industry. But this dielectric property, depends on properties of material like porosity, density, concentration etc. It is very costly and time consuming to determine these properties every time by experimentation. So, developing a system that can predict the properties of barium titanate can be helpful for electronic industries. The Current work is to develop AI based model that can estimate the physical properties of barium titanate dielectric material. A three-layer artificial neural network is trained to estimate the physical properties of the ceramic using the experimental data with the mean square error of less than 1. The performance of trained model is verified on different experimental data set. Barium titanate ; Capacitor ; Dielectric ; Materials science ; Ceramic capacitor ; Ceramic ; Artificial neural network ; Electronic engineering ; Porosity ; Electronics ; Engineering physics ; Computer science ; Composite material ; Electrical engineering ; Optoelectronics ; Voltage ; Engineering ; Artificial intelligence Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Electrical and Thermal Properties of Materials
Authors: Kerisha Williams | Fengyuan Zhang | D.N. Edwards | Aaron B. Naden | Yulian Yao | Sabine M. Neumayer | Amit Kumar | Brian J. Rodriguez | Nazanin Bassiri‐Gharb
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior. This repository contains the data and code used in the corresponding study: Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior. The abstract for the work appears as follows: Scanning Probe Microscopy (SPM) based techniques probe material properties over micrometers wide regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the <em>Off-field</em> piezoresponse, compared to <em>On-field</em> piezoresponse, based on the latter’s increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating <em>both</em> <em>Off-</em> and <em>On-field</em> piezoresponse response as well as <em>Off-field</em> resonance frequency to maximize information, we investigate switching piezoresponse in a defect-rich Pb(Zr,Ti)O<sub>3</sub> thin film. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during <em>On-field</em> measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response Nanoscopic scale ; Computer science ; Scale (ratio) ; Artificial intelligence ; Machine learning ; Human–computer interaction ; Materials science ; Nanotechnology ; Geography ; Cartography Machine Learning in Materials Science
Authors: Om Smaran Mohapatra
Source:  (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Towards prediction of Porous Silicon formation using Machine Learning models Porous Silicon (PS) is a spongy material with applications in semiconductor industry. The fabrication of PS in laboratory scale is time consuming and requires correct estimation of PS characteristics like porosity, pore size and thickness. Therefore, Artificial Intelligence (AI) techniques like Machine Learning (ML) models are used to predict the characteristics accurately and within a short time. In this paper, I have used ML models for predicting the formation of porous silicon obtained from electrochemical anodization process from various sources. I have applied three ML models i.e., K Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) in the present study. The model is first trained by giving formation parameter values of Hydrofluoric acid (HF) concentration, type of Silicon wafer used (p or n type), current density (in mA/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), and the time required (in minutes) for the formation of pores as input. The training of the three models is done on a data set for the models to learn to predict the values of porosity, pore size, and thickness of the sample. I have performed the testing on a different dataset and the classification scores obtained for the three models are: 0.713 for SVM classifier, 0.693 for KNN and 0.68 for RF model. Further, the graphical comparisons between the actual and predicted values of PS characteristics have been presented using regression. Support vector machine ; Artificial intelligence ; Hydrofluoric acid ; Wafer ; Machine learning ; Porous silicon ; Porosity ; Silicon ; Computer science ; Random forest ; Materials science ; Classifier (UML) ; Nanotechnology ; Optoelectronics ; Composite material Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Anodic Oxide Films and Nanostructures
Authors: Florian Fricke | Safdar Mahmood | Javier Hoffmann | Marcelo Brandalero | Sascha Liehr | Simon Kern | Meyer Klas | Stefan Kowarik | Stephan Westerdick | Michael Maiwald | Michael Hübner
Source:  (article, 2021-02-01)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. Context (archaeology) ; Automation ; Artificial neural network ; Computer science ; Mass spectrometry ; Process (computing) ; Process control ; Process engineering ; Artificial intelligence ; Chemistry ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Johannes Niskanen | Anton Vladyka | Johannes Kettunen | Christoph J. Sahle
Source: arXiv (Cornell University) (preprint, 2021-04-06)
Field: Materials Science — Materials Chemistry

Neural networks in interpretation of electronic core-level spectra We explore the applicability of artificial intelligence for molecular structure - core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H$_2$O molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid of the spectrum improves prediction. However, the accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space. Spectral line ; Artificial neural network ; Interpretation (philosophy) ; Grid ; Core (optical fiber) ; Hamiltonian (control theory) ; Focus (optics) ; Computer science ; Artificial intelligence ; Test set ; Algorithm ; Parameter space ; Spectrum (functional analysis) ; Physics ; Mathematics ; Geometry ; Quantum mechanics ; Optics ; Mathematical optimization ; Telecommunications Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics
Authors: Fabio Urbina | Kushal Batra | Kevin J Luebke | Jason D. White | Daniel Matsiev | Lori L. Olson | Jeremiah P. Malerich | Maggie A. Z. Hupcey | Peter B. Madrid | Sean Ekins
Source:  (preprint, 2021-08-19)
Field: Chemistry — Analytical Chemistry

UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to reference spectra. Here, we present UV-adVISor as a new computational tool for predicting UV-Vis spectra from a molecule’s structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint diameter 6 or molecule SMILES to generate predictive models for UV-spectra. We have produced two spectrum datasets (Dataset I, N = 949 and Dataset II, N = 2222) using different compound collections and spectrum acquisition methods to train, validate, and test our models. We evaluated the prediction accuracy of the complete spectra by the correspondence of wavelengths of absorbance maxima and with a series of statistical measures (the best test set median model parameters are in parentheses for Model II), including RMSE (0.064), R2 (0.71), and dynamic time warping (DTW, 0.194) of the entire spectrum curve. Scrambling molecule structures with experimental spectra during training resulted in a degraded R2, confirming the utility of the approaches for prediction. UV-adVISor is able to provide fast and accurate predictions for libraries of compounds. Spectral line ; Artificial neural network ; Dynamic time warping ; Ultraviolet ; Absorbance ; Pattern recognition (psychology) ; Computer science ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Optics ; Chromatography Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Xingyu Ma | Hou-Yi Lyu | Kuan-Rong Hao | Zhen‐Gang Zhu | Qing‐Bo Yan | Gang Su
Source: arXiv (Cornell University) (preprint, 2021-02-22)
Field: Materials Science — Materials Chemistry

Highly Efficient and Accurate ab initio Bayesian Active Learning Method for Accelerating Discovery of Advanced Functional Materials Discovery of advanced materials with conventional trial-and-error method usually suffers tremendous difficulties. Machine learning offers a great opportunity to discover target materials, but unbalanced distribution of data often causes less accuracy of the method. Here, we propose the ab initio Bayesian active learning method that combines the active learning and high-throughput ab initio calculations to accelerate discovery of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3,119) of two-dimensional hexagonal binary compounds with unbalanced data, and accurately screen out the materials with maximal electric polarizations and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of possible candidates in comparison to the random search. This approach shows enormous advantages for the cases with unbalanced distributions of data. It can be readily applied to seek for a broad range of advanced materials. Ab initio ; Computer science ; Bayesian probability ; Range (aeronautics) ; Binary number ; Experimental data ; Active learning (machine learning) ; Machine learning ; Throughput ; Artificial intelligence ; Algorithm ; Materials science ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Brais Galdo | Enrique Fernández-Blanco | Daniel Rivero
Source:  (article, 2021-10-18)
Field: Chemistry — Analytical Chemistry

Detection of Chocolate Properties Using Near-Infrared Spectrophotometry Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities. Infrared Spectrophotometry ; Fluid ounce (US) ; Infrared ; Electromagnetic spectrum ; Computer science ; Near-infrared spectroscopy ; Process engineering ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Chemistry ; Chromatography ; Engineering ; Optics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Alexander Winckelmann | Sascha Nowak | Silke Richter | Sebastian Recknagel | Jens Riedel | Jochen Vogl | Ulrich Panne | Carlos Abad
Source: ChemRxiv (preprint, 2021-01-18)
Field: Chemistry — Analytical Chemistry

High-Resolution Atomic Absorption Spectrometry Combined with Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium &lt;a&gt;An alternative method for lithium isotope amount ratio analysis based on a combination of high-resolution atomic absorption spectrometry and spectral data analysis by machine learning (ML) is proposed herein. It is based on the well-known isotope shift of approximately 15 pm for the electronic transition 2&lt;sup&gt;2&lt;/sup&gt;P←2&lt;sup&gt;2&lt;/sup&gt;S at around the wavelength of 670.8 nm, which can be measured by state-of-the-art high-resolution continuum source graphite furnace atomic absorption spectrometry. For isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with &lt;sup&gt;6&lt;/sup&gt;Li isotope amount fractions ranging from 0.06 to 0.99 mol mol&lt;sup&gt;−1&lt;/sup&gt;, previously determined by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied to the isotope amount ratio determination of a set of stock chemicals (Li&lt;sub&gt;2&lt;/sub&gt;CO&lt;sub&gt;3&lt;/sub&gt;, LiNO&lt;sub&gt;3&lt;/sub&gt;, LiCl, and LiOH) and a BAM candidate reference material, that is, LiNi&lt;sub&gt;1/3&lt;/sub&gt;Mn&lt;sub&gt;1/3&lt;/sub&gt;Co&lt;sub&gt;1/3&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; (NMC111) cathode material. The results of these determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰ and the &lt;/a&gt;&lt;a&gt;precision obtained ranged from 1.9‰ to 6.2‰&lt;/a&gt;. This precision was sufficient to resolve naturally occurring variations, as demonstrated for samples ranging from approximately −3‰ to +15‰. To assess its suitability to technical applications, the NMC111 cathode candidate reference material was analyzed using high-resolution continuum source molecular absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other. Chemistry ; Analytical Chemistry (journal) ; Isotope ; Stable isotope ratio ; Physics ; Chromatography Analytical chemistry methods development ; Advanced Chemical Sensor Technologies ; Isotope Analysis in Ecology
Authors: Jana Gregorová | Sabina Adamová | Lukáš Pečínka | Lukas Moran | Volodymyr Porokh | Martin Štork | Luděk Pour | Josef Havel | Petr Vaňhara | Sabina Ševčı́ková
Source: Clinical Lymphoma Myeloma & Leukemia (article, 2021-10-01)
Field: Chemistry — Spectroscopy

P-105: Mass spectrometry and artificial neural networks for discrimination of extramedullary Multiple Myeloma Linear discriminant analysis ; Mass spectrometry ; Principal component analysis ; Bone marrow ; Artificial neural network ; Multiple myeloma ; Mass spectrum ; Diagnostic model ; Biomedical engineering ; Chemistry ; Pattern recognition (psychology) ; Pathology ; Chromatography ; Medicine ; Artificial intelligence ; Internal medicine ; Computer science ; Data mining Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications
Authors: Haichuan Lou | Zhiwei Zhang | Baihui Zha
Source:  (article, 2021-10-19)
Field: Chemistry — Analytical Chemistry

Sales Demand Prediction Model of Gated Recurrent Unit Neural Network Based on Improved Shape Distance Loss Function Under the background of diversification and refinement of chemical products, product demand prediction is playing a guiding role in production planning. In this paper, a new sales demand prediction model based on improved shape distance Loss function of Gated Recurrent Unit Neural Network (ISD_GRUNN) is proposed for the long-term prediction of the sales quantity of chemical products. The improved shape distance is determined by the change trend, amplitude and distance between the two points. Compared with MSE which only considers the difference between the corresponding time point sequence values as the loss function, the change trend and range of the time series will be taken into account in the improved shape distance as the loss function. The experimental results show that the improved shape distance as the loss function can be better used for sales long-term prediction. Artificial neural network ; Function (biology) ; Computer science ; Diversification (marketing strategy) ; Range (aeronautics) ; Demand forecasting ; Algorithm ; Artificial intelligence ; Mathematics ; Operations research ; Engineering Spectroscopy and Chemometric Analyses ; Industrial Technology and Control Systems ; Fault Detection and Control Systems
Authors: Lei Shen | Jun Zhou | Tong Yang | Ming–Hsuan Yang | Yuan Ping Feng
Source: arXiv (Cornell University) (preprint, 2021-12-17)
Field: Materials Science — Materials Chemistry

High-throughput Discovery and Intelligent Design of 2D Functional Materials for Various Applications Novel technologies and new materials are in high demand for future energy-efficient electronic devices to overcome the fundamental limitations of miniaturization of current silicon-based devices. Two-dimensional (2D) materials show promising applications in the next generation devices because they can be tailored on the specific property that a technology is based on, and be compatible with other technologies, such as the silicon-based (opto)electronics. Although the number of experimentally discovered 2D materials is growing, the speed is very slow and only a few dozen 2D materials have been synthesized or exfoliated since the discovery of graphene. Recently, a novel computational technique, dubbed "high-throughput computational materials design", becomes a burgeoning area of materials science, which is the combination of the quantum-mechanical theory, materials genome, and database construction with intelligent data mining. This new and powerful tool can greatly accelerate the discovery, design and application of 2D materials by creating database containing a large amount of 2D materials with calculated fundamental properties, and then intelligently mining (via high-throughput automation or machine learning) the database in the search of 2D materials with the desired properties for particular applications, such as energy conversion, electronics, spintronics, and optoelectronics. Electronics ; Throughput ; Computer science ; Miniaturization ; Nanotechnology ; Computer architecture ; Materials science ; Electrical engineering ; Engineering ; Wireless 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques