jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joshua A. Burrow | Andre Van Rynbach | Piyush Shah | Jonathan R. Thompson | Jonathan E. Slagle | Matthew S. Mills | John Demis | N. Cao | Ray Secondo | Mulaine Shih | Eric S. Harper | Imad Agha
Source:  (article, 2022-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Switchable metasurfaces from phase change materials designed with machine learning Chalcogenide phase change materials (PCMs) are uniquely suited for spectral tuning applications due to their contrasting dielectric material properties. Recent headway has been made towards realizing tunable photonic devices using twodimensional, sub-wavelength resonators by carefully designing geometries that optimize optical, electrical, and thermal performances using multi-physics analyses and machine learning. In this paper, we tackle two other essential aspects for creating application-specific, tunable PCM devices: (1) scalability of the device size and (2) high-throughput fabrication techniques. We employ a deep ultraviolet (DUV) stepper projection lithography to manufacture over 100 densely packed GST metasurfaces, each with a sample size of 5×7 mm<sup>2</sup>, all on a 4-inch Al<sub>2</sub>O<sub>3</sub> wafer. These metasurface structures were discovered using artificial neural network (ANN) techniques and confirmed by finite-difference-time domain calculations. The primary structures under investigation were nanobar configurations enabling amplitude modulation at short-wave infrared wavelengths to realize efficient optical switches for free space optical multiplexing. The DUV fabrication technique can easily be extended to other metasurface geometries to demonstrate multi-functional, non-volatile photonic devices. Materials science ; Photonics ; Fabrication ; Lithography ; Optoelectronics ; Wafer ; Nanoimprint lithography ; Dielectric ; Multiplexing ; Resonator ; Optics ; Electronic engineering ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Phase-change materials and chalcogenides
Authors: Sucheta Swetlana | Ashish Rout | Abhisek Singh
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An Interpretable Machine Learning Approach for Predicting Creep and Fatigue Life in Ti-Alloys Creep ; Materials science ; Psychology ; Artificial intelligence ; Metallurgy ; Computer science Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Md. Akib Khan | AKM M. Morshed | Titan C. Paul
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Molecular Dynamics Simulation of Liquids Molecular dynamics ; Dynamics (music) ; Computer science ; Chemistry ; Psychology ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Various Chemistry Research Topics
Authors: Yi-Ming Kong | Xiaobo Li | Mao Huang
Source: Journal of Molecular Structure (article, 2022-08-01)
Field: Chemistry — Inorganic Chemistry

Therapeutic effect of Cu(II) coordination polymer on acute asthma and ligand optimization using machine learning algorithms Chemistry ; Ligand (biochemistry) ; Coordination polymer ; Chelation ; Mechanism (biology) ; Polymer ; Algorithm ; Receptor ; Biochemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Synthesis and Characterization of Heterocyclic Compounds ; Metal complexes synthesis and properties
Authors: Sucheta Swetlana | Abhisek Singh
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An Interpretable Machine Learning Approach for Predicting Creep and Fatigue Life in Ti-Alloy Creep ; Alloy ; Materials science ; Artificial intelligence ; Machine learning ; Metallurgy ; Engineering ; Computer science ; Forensic engineering Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Uy Yoon Park | Chul‐Hong Park
Source: New Physics Sae Mulli (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Investigation of Atomic Interaction Energies in Semiconductors through Machine Learning Using Artificial Neural Networks Artificial neural network ; Semiconductor ; Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Arda Genç | Libor Kovařík | Hamish L. Fraser
Source: arXiv (Cornell University) (preprint, 2022-01-18)
Field: Materials Science — Surfaces, Coatings and Films

A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials Heterogeneous catalysts possess complex surface and bulk structures, relatively poor intrinsic contrast, and often a sparse distribution of the catalytic nanoparticles (NPs), posing a significant challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a $γ$-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net's fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 $\pm$ 0.003 in the $γ$-Alumina support material and 0.84 $\pm$ 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for $γ$-Alumina and Pt NPs segmentations. The complex surface morphology of the $γ$-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions. Segmentation ; Artificial intelligence ; Deep learning ; Materials science ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network ; Test set ; Data set ; Hausdorff distance ; Image segmentation ; Ground truth ; Boundary (topology) ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Ahmed Halilu | Mohamed Kamel Hadj-Kali | Mohd Ali Hashim | Hanee F. Hizaddin | Ahmad Alhadid | Liudmila Mokrushina | Emad Ali
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Model for Assessment of CO2 Electroreduction Current Response Asymptote in Deep Eutectic Electrolytes Asymptote ; Eutectic system ; Current (fluid) ; Electrolyte ; Computer science ; Chemistry ; Materials science ; Engineering ; Electrical engineering ; Electrode ; Metallurgy ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Thijs Stuyver | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2022-12-15)
Field: Chemistry — Organic Chemistry

Machine learning-guided computational screening of new bio-orthogonal click reactions Bio-orthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bio-orthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate bio-orthogonal click reactions. Sampling only around 0.05\% of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT-computed activation and reaction energies within ~2-3 kcal/mol across the entire space. Applying this model to screen the full search space through iterative rounds of learning, we identify a broad pool of candidate reactions with rich structural diversity, which can be used as a starting point or source of inspiration for future experimental development of both azide-based and non-azide-based bio-orthogonal click reactions. Click chemistry ; Computer science ; Workflow ; Azide ; Toolbox ; Chemical space ; Chemistry ; Theoretical computer science ; Artificial intelligence ; Combinatorial chemistry ; Database ; Organic chemistry ; Drug discovery ; Programming language Click Chemistry and Applications ; Computational Drug Discovery Methods ; Protein Degradation and Inhibitors
Authors: B. Eckert | Stefan Aschauer | E Hedley | M Huth | Paweł Majewski | L. Strüder
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Electron Image Reconstruction for Pixelated Semiconductor Tracking Detectors Based on Neural Networks Journal Article Electron Image Reconstruction for Pixelated Semiconductor Tracking Detectors Based on Neural Networks Get access B Eckert, B Eckert PNDetector GmbH, München, GermanyUniversity of Siegen, Department of Physics, Siegen, Germany Corresponding author: bjoern.eckert@pndetector.de Search for other works by this author on: Oxford Academic Google Scholar S Aschauer, S Aschauer PNDetector GmbH, München, Germany Search for other works by this author on: Oxford Academic Google Scholar E Hedley, E Hedley University of Oxford, Department of Materials, Oxford, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar M Huth, M Huth PNDetector GmbH, München, Germany Search for other works by this author on: Oxford Academic Google Scholar P Majewski, P Majewski PNDetector GmbH, München, Germany Search for other works by this author on: Oxford Academic Google Scholar L Strüder L Strüder University of Siegen, Department of Physics, Siegen, GermanyPNSensor GmbH, München, Germany Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3040–3042, https://doi.org/10.1017/S1431927622011357 Published: 01 August 2022 Tracking (education) ; Art history ; Library science ; Art ; Computer science ; Sociology Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Michela Sperti | Arnaud Gucciardi | Umberto Michelucci | Francesca Venturini | Marco A. Deriu
Source:  (article, 2022-03-30)
Field: Chemistry — Analytical Chemistry

Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks The chemical analysis of food is essential to monitor and guarantee its quality. The determination of the chemical parameters, like the concentration of particular substances, is performed by specialized laboratories and is a time-consuming and costly process. Therefore, alternative methods with easier handling are of great interest. Among these fluorescence spectroscopy offers great opportunities. Fluorescence spectra are one-dimensional arrays of values already successfully employed together with artificial neural networks for classification problems in chemistry, physics, and other fields. However, the extraction of specific quantities from the spectra poses a much harder challenge. This work analyzes and compares the ability of feed-forward neural networks (FFNN) and one-dimensional convolutional neural networks (1D-CNN) to extract relevant features from fluorescence spectra of olive oils. The results indicate that 1D-CNN, contrary to FFNN, successfully predicts the chemical parameters with high accuracy. The great advantages of the proposed method are: 1) the possibility of using optical methods instead of time-consuming chemical ones, like chromatography, 2) the lack of any special sample handling, like dilution and 3) the lack of any pre-processing of the data. The problem of small datasets, which may arise for novel techniques like the proposed one, is also addressed statistically by using the leave-one-out resampling technique. Convolutional neural network ; Artificial neural network ; Computer science ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Fluorescence ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Likun Wang | Hao Pei | Tong Zhu
Source: ChemRxiv (preprint, 2022-06-21)
Field: Materials Science — Materials Chemistry

A quantum chemical convolutional neural network model for predicting thermodynamics and kinetics of DNA molecules from sequences Accurate predicting thermodynamics and kinetics of DNA molecules are important for designing oligonucleotides in biotechnological applications, but current models in use are not accurate enough. Here, we propose a model combining quantum chemical calculations and convolutional neural networks (QCM) to predict the free energies of DNA duplexes and rate constants of DNA strand displacement reactions. The QCM achieves better accuracy than previous models on two limited datasets by active learning, the accuracy of predicting free energies of duplexes is 23% better than the nearest neighbor model, and the accuracy of predicting rate constants of strand displacement reactions is 60% better than the worm-like chain model. Kinetics ; Convolutional neural network ; DNA ; Reaction rate constant ; Biological system ; Oligonucleotide ; Displacement (psychology) ; Molecule ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Artificial intelligence ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; DNA and Nucleic Acid Chemistry
Authors: Satoshi Noguchi | Hui Wang | Junya Inoue
Source: Research Square (preprint, 2022-04-15)
Field: Materials Science — Materials Chemistry

Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists' thinking process Abstract In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers' process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background. Computer science ; Artificial intelligence ; Intuition ; Process (computing) ; Identification (biology) ; Property (philosophy) ; Creativity ; Machine learning ; Management science ; Engineering ; Cognitive science ; Psychology ; Epistemology Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Joyjit Chattoraj | Brahim Hamadicharef | Jian Feng Kong | Mohan Kashyap Pargi | Yingzhi Zeng | Chee Kok Poh | Luwei Chen | Fei Gao | Teck Leong Tan
Source: ChemCatChem (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

Cover Feature: Theory‐Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water‐Gas Shift Reaction (ChemCatChem 16/2022) The Cover Feature illustrates a theory-guided machine learning framework for water-gas shift reaction optimization. In their Research Article, J. Chattoraj, T. L. Tan and co-workers showed that purely data-driven models could violate the thermodynamic equilibrium principle as well as the models could predict non-physical carbon monoxide conversion percentage. They proposed a theory-guided machine learning model with a unique thermodynamic loss function to resolve these two problems. The proposed model showed promising reaction conditions, which could result in high carbon monoxide conversion rates. All the authors thank Zicong Marvin Wong for helping to design the cover picture. More information can be found in the Research Article by J. Chattoraj, T. L. Tan and co-workers. Cover (algebra) ; Carbon monoxide ; Water-gas shift reaction ; Catalysis ; Noble metal ; Feature (linguistics) ; Chemistry ; Computer science ; Thermodynamics ; Physical chemistry ; Physics ; Mechanical engineering ; Organic chemistry ; Engineering ; Philosophy Machine Learning in Materials Science
Authors: Yang Ling | Zhengxin Chen | Site Li | Yunxiao Guan | Cheng Peng | Biao Wu | Zhihai Cheng | Jiang Wu | Xuemei Qi | Mengjie Bai
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Mapping between Density of States and Energy Band Gap for Bismuth-Based Semiconductors Via Machine Learning Prediction Gradient boosting ; Random forest ; Ensemble learning ; Hyperparameter optimization ; Boosting (machine learning) ; Artificial intelligence ; Support vector machine ; Machine learning ; Mean squared error ; Computer science ; Hyperparameter ; Gaussian process ; Gaussian ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials
Authors: Massimo Bocus | Ruben Goeminne | Aran Lamaire | Maarten Cools‐Ceuppens | Toon Verstraelen | Véronique Van Speybroeck
Source: Research Square (preprint, 2022-10-04)
Field: Materials Science — Materials Chemistry

On the impact of nuclear quantum effects on zeolite proton hopping kinetics through machine learning potentials and path integral molecular dynamics simulations Proton ; Path integral formulation ; Molecular dynamics ; Zeolite ; Path (computing) ; Dynamics (music) ; Kinetics ; Quantum ; Surface hopping ; Chemical physics ; Physics ; Statistical physics ; Chemistry ; Nuclear physics ; Computer science ; Classical mechanics ; Quantum mechanics ; Catalysis Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: Lijing Wang | Shan Gao | Wenting Li | Ao Zhu | Huan Li | Chunning Zhao | Haijun Zhang | Weihua Wang | Weichao Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Assisted Screening of Mxenes Pseudocapacitive Materials MXenes ; Pseudocapacitance ; Electronegativity ; Materials science ; Carbide ; Density functional theory ; Nanotechnology ; Chemistry ; Supercapacitor ; Physics ; Computational chemistry ; Composite material ; Physical chemistry ; Quantum mechanics Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: M. Meuwly
Source: arXiv (Cornell University) (preprint, 2022-01-11)
Field: Materials Science — Materials Chemistry

Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis? Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulations closer to reality. This perspective delineates the present status of the field from efforts of others in the field and some of your own work and discusses open questions and future prospects. Status quo ; Molecular dynamics ; Field (mathematics) ; Computer science ; Perspective (graphical) ; Work (physics) ; Data science ; Nanotechnology ; Systems engineering ; Artificial intelligence ; Engineering ; Materials science ; Chemistry ; Political science ; Computational chemistry ; Mechanical engineering ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Gian‐Marco Rignanese | Xiaotong Liu | Pierre-Paul De Breuck | Linghui Wang
Source: Research Square (preprint, 2022-05-10)
Field: Materials Science — Materials Chemistry

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties Exploit ; Simple (philosophy) ; Fidelity ; Noise reduction ; Computer science ; Machine learning ; Artificial intelligence ; Computer security ; Telecommunications Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Chunjie Zhai | Siyu Zhang | Nan Jiang | Lizhong Yang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Kinetic Parameter Optimization for Pyrolysis of Thermally Thick Combustibles Via Chemistry Informed Neural Network Pyrolysis ; Kinetic energy ; Artificial neural network ; Chemistry ; Chemical engineering ; Biological system ; Computer science ; Organic chemistry ; Engineering ; Artificial intelligence ; Physics ; Biology ; Classical mechanics Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes