jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Probst
Source: CHIMIA International Journal for Chemistry (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding, the exclusiveness of research, and thus the inequality between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community that could affect chemistry and present potential solutions, including more detailed assessments of model performance, increased adherence to open science and open data practices, an increase in multinational and multi-institutional collaboration, and a focus on thematic and cultural diversity. Equity (law) ; Multinational corporation ; Diversity (politics) ; Academic community ; Political science ; Inequality ; Engineering ethics ; Computer science ; Chemistry ; Management science ; Data science ; Knowledge management ; Economics ; Engineering ; Library science ; Mathematics ; Law Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: D. Chen | Shu Li | Tingpeng Tao | Shuai Li | Dong-Rong Liu | Xin Liu | Minghua Chen
Source: Materials Today Communications (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

A machine learning framework for predicting physical properties in configuration space of gate alloys Materials science ; Extrapolation ; Artificial neural network ; Convolutional neural network ; Debye model ; Machine learning ; Biological system ; Artificial intelligence ; Computer science ; Thermodynamics ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Yong-Guo Shi | Man Wang | Zhiling Zhou | Min Zhao | Yanqiang Hu | Jian Yang | Shengfu Tong | Fuming Lai
Source: Coatings (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

Artificial Neural Network-Based Prediction and Morphological Evolution of Cu2O Crystal Surface Energy In this study, we investigate the crystal structure, surface energy, and atomic arrangement of Cu2O. Understanding these properties is crucial for exploring the potential applications and understanding the behavior of this material. We employ the Wulff construction method and an artificial neural network (ANN) model to analyze the relative surface energies of different crystal facets and predict the surface energy of Cu2O. The ANN model exhibits excellent performance, demonstrating its effectiveness in predicting material properties and providing automated feature-learning and nonlinear-modeling capabilities. Moreover, we analyze the atomic arrangements on various crystal facets and observe the presence of oxygen atoms on the {100} facet, as well as exposed under-coordinated copper atoms on the {111} and {110} facets. High-index facets such as {211} exhibit a higher atomic step density and screw dislocation density. By precisely controlling the synthesis process, it is possible to manipulate the proportion of high-index facets. These findings highlight the significance of understanding the surface energy and atomic arrangement of Cu2O crystals for comprehending their properties and surface reactions. In summary, this study provides valuable insights into the crystal structure, surface energy, and atomic arrangement of Cu2O, offering inspiration for its properties and potential applications. The combination of the Wulff construction method and ANN modeling provides a comprehensive understanding of Cu2O crystals and their surface behavior, contributing to the field of materials science and laying the foundation for various future applications utilizing the unique properties of Cu2O. Facet (psychology) ; Crystal (programming language) ; Artificial neural network ; Dislocation ; Materials science ; Surface (topology) ; Surface energy ; Chemical physics ; Field (mathematics) ; Nanotechnology ; Artificial intelligence ; Computer science ; Physics ; Geometry ; Mathematics Copper-based nanomaterials and applications ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Badal Mondal | Julia Westermayr | Ralf Tonner
Source: The Journal of Chemical Physics (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors Quaternary III-V semiconductors are one of the most promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and characteristics of optoelectronic devices. Experimental approaches screening a large range of possible combinations of III- and V-elements with variations in composition and strain are impractical for every target application. We present a combination of accurate first-principles calculations and machine learning based approaches to predict the properties of the bandgap for quaternary III-V semiconductors. By learning bandgap magnitudes and their nature at density functional theory accuracy based solely on the composition and strain features of the materials as an input, we develop a computationally efficient yet highly accurate machine learning approach that can be applied to a large number of compositions and strain values. This allows for a computationally efficient prediction of a vast range of materials under different strains, offering the possibility of virtual screening of multinary III-V materials for optoelectronic applications. Semiconductor ; Band gap ; Materials science ; Strain (injury) ; Range (aeronautics) ; Optoelectronics ; Computer science ; Machine learning ; Composite material ; Biology Machine Learning in Materials Science ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Shubin Liu
Source: ChemRxiv (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Harvesting Chemical Understanding with Machine Learning and Quantum Computers It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective article, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workforce of in silico simulations in the next few decades. nobody ; Computer science ; Function (biology) ; Perspective (graphical) ; Face (sociological concept) ; Artificial intelligence ; Density functional theory ; Management science ; Theoretical computer science ; Data science ; Chemistry ; Sociology ; Engineering Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Haidi Wang | Tao Li | Yufan Yao | Xiaofeng Liu | Weiduo Zhu | Zhao Chen | Zhongjun Li | Wei Hu
Source: Chinese Journal of Chemical Physics (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Atomistic modeling of lithium materials from deep learning potential with <b> <i>ab initio</i> </b> accuracy Lithium has been paid great attention in recent years thanks to its significant applications for battery and lightweight alloy. Developing a potential model with high accuracy and efficiency is important for theoretical simulation of lithium materials. Here, we build a deep learning potential (DP) for elemental lithium based on a concurrent-learning scheme and DP representation of the density-functional theory (DFT) potential energy surface (PES), the DP model enables material simulations with close-to DFT accuracy but at much lower computational cost. The simulations show that basic parameters, equation of states, elasticity, defects and surface are consistent with the first principles results. More notably, the liquid radial distribution function based on our DP model is found to match well with experiment data. Our results demonstrate that the developed DP model can be used for the simulation of lithium materials. Density functional theory ; Lithium (medication) ; Ab initio ; Potential energy surface ; Representation (politics) ; Materials science ; Computer science ; Statistical physics ; Computational science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Laura Neuendorf | Stefan Höving | Lennard Bennemann | Norbert Kockmann
Source: Chemie Ingenieur Technik (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity‐Based Approach for Size Distribution Detection Abstract The majority of fine chemical and pharmaceutical processes includes some form of crystallization steps. For process optimization and control of further downstream steps, the crystal size distribution of the product is a crucial factor. To identify characteristic particle size classes from a large number of measurements, each individual probe has to be separated from the mother liquor and manually analyzed. In this contribution a deep learning‐based method is presented using microscopic images as input for crystal size analysis. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. A high segmentation accuracy of the crystals was achieved with 93.02 %. To evaluate the classification performed by the presented convolutional neural network (CNN), it is tested on two sets of images, containing a previously determined particle fraction. With the classifications of the CNN, a Q 3 distribution is calculated. To validate the developed approach in terms of its accuracy it is compared to two other methods as well. Crystallization ; Convolutional neural network ; Crystal (programming language) ; Particle-size distribution ; Artificial neural network ; Artificial intelligence ; Process (computing) ; Particle size ; Segmentation ; Biological system ; Computer science ; Particle (ecology) ; Limit (mathematics) ; Pattern recognition (psychology) ; Fraction (chemistry) ; Distribution (mathematics) ; Materials science ; Mathematics ; Chemistry ; Chromatography ; Mathematical analysis ; Geology Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Ganesan Raman
Source: Journal of Solid State Chemistry (article, 2023-08-26)
Field: Chemistry — Inorganic Chemistry

Forecasting low framework density zeolites from synthesis descriptors using machine learning Zeolite ; Density functional theory ; Hydroxide ; Porosity ; Heuristic ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Computational chemistry ; Catalysis ; Inorganic chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Xuandong Wang | Hang Su | Nan Li | Huiling Chen | Yilin Yang | Huimin Meng
Source: Processes (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

A Deep Learning Labeling Method for Material Microstructure Image Segmentation In the existing deep learning modeling process for material microstructure image segmentation, the manual pixel labeling process is time-consuming and laborious. In order to achieve fast and high-accuracy modeling, this work proposes a convenient deep learning labeling method and a workflow for generating a synthetic image data set. Firstly, a series of label templates was prepared by referring to the distribution of the material microstructure. Then, the typical textures of different microstructures were box-selected in the images to be segmented to form texture templates. The manual pixel labeling was simplified to the box-selection of the typical microstructure texture. Finally, a synthetic data set can be generated using the label and texture templates for further deep learning model training. Two image cases containing multiple types of microstructures were used to verify the labeling method and workflow. The results show that the pixel segmentation accuracy of the deep learning model for the test images reaches 95.92% and 95.40%, respectively. The modeling workflow can be completed within 20 min, and the labeling time that requires manual participation is within 10 min, significantly reducing the modeling time compared to traditional methods where the labeling process may take several hours. Workflow ; Artificial intelligence ; Computer science ; Template ; Pixel ; Deep learning ; Segmentation ; Process (computing) ; Texture (cosmology) ; Image segmentation ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Database Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Advanced Neural Network Applications
Authors: H. Ferhati | Tarek Berghout | Abderraouf Benyahia | F. Djeffal
Source:  (article, 2023-11-15)
Field: Materials Science — Polymers and Plastics

Machine Learning DFT-Based Approach to Predict the Electrical Properties of Tin Oxide Materials The effects of oxygen concentration and growth technique during the deposition process on the electrical properties of tin oxide alloy (SnOx) should be investigated for developing new eco-friendly photosensors and photovoltaic devices. The present work aims to predict the electrical key governing parameters throughout the device developing processes such as the Energy level values and band-gap energy as function of the injected oxygen concentrations. For realization, over 100 data points were collected by modeling the effect of oxygen contents on the SnOx electrical properties using Density Function Theory (DFT). Through extensive Machine Learning (ML) analysis, the impact of the oxygen concentration on the electrical properties and the material type is well predicted, where the applied ML prediction model for band-gap energy showed a good correlation between predicted values and the calculated ones using DFT computations. It is revealed that the combined DFT-ML-based approach can be a powerful tool to study and accelerate the developing of new highly efficient materials for microelectronic applications. Tin ; Tin oxide ; Materials science ; Computer science ; Oxide ; Metallurgy Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Rajesh Kumar | Pawan Kumar Rakesh | Dungali Sreehari
Source:  (article, 2023-05-25)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Identification of the Chemical and Crystallinity of Pine Needle Fiber at Different Altitudes in India's Garhwal Region Pine needles are found at various altitudes in the Himalayas' Garhwal region. This study examined how altitude affects the chemical composition and crystallinity of pine needle fibre. Multiplelocations in the Garhwal area of Uttarakhand were chosenwith an elevation from600to 2,000 metres above sea level (masl). The pine needle fiber (PNF) was collected from these locations using an altimeter, and their chemical composition, crystallinity index (CI) and crystal size (CS) were investigated. It was found that fibers grown at higher altitude locations exhibited improved properties compared to those grown at lower altitude locations. Moreover, the degree of crystallinity of the fibers increased with altitude, indicating that pine needle fibers collected from higher altitudes may have a more significant potential for use in various applications. Artificial Neural Networks (ANN) based model is developed to predict the properties of fibers from various altitudes. According to the findings, the ANN model successfully predicted the occurrence of chemical composition and crystallinity shifts in the fibers as a function of altitude. The findings of this study provide insights into the potential of pine needle fibers as a sustainable and renewable material and highlight the value of artificial neural networks for determining how natural fibers will perform. Crystallinity ; Altitude (triangle) ; Fiber ; Chemical composition ; Environmental science ; Effects of high altitude on humans ; Materials science ; Composite material ; Meteorology ; Geography ; Chemistry ; Mathematics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Plant Surface Properties and Treatments
Authors: Robin Hilgers | Daniel Wortmann | Stefan Blügel
Source: arXiv (Cornell University) (preprint, 2023-11-26)
Field: Materials Science — Materials Chemistry

Machine Learning-based estimation and explainable artificial intelligence-supported interpretation of the critical temperature from magnetic ab initio Heusler alloys data Machine Learning (ML) has impacted numerous areas of materials science, most prominently improving molecular simulations, where force fields were trained on previously relaxed structures. One natural next step is to predict material properties beyond structure. In this work, we investigate the applicability and explainability of ML methods in the use case of estimating the critical temperature for magnetic Heusler alloys calculated using ab initio methods determined materials-specific magnetic interactions and a subsequent Monte Carlo (MC) approach. We compare the performance of regression and classification models to predict the range of the critical temperature of given compounds without performing the MC calculations. Since the MC calculation requires computational resources in the same order of magnitude as the density-functional theory (DFT) calculation, it would be advantageous to replace either step with a less computationally intensive method such as ML. We discuss the necessity to generate the magnetic ab initio results to make a quantitative prediction of the critical temperature. We used state-of-the-art explainable artificial intelligence (XAI) methods to extract physical relations and deepen our understanding of patterns learned by our models from the examined data. Ab initio ; Monte Carlo method ; Artificial intelligence ; Density functional theory ; Ab initio quantum chemistry methods ; Computer science ; Statistical physics ; Machine learning ; Materials science ; Condensed matter physics ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Molecule ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Catalysis and Oxidation Reactions
Authors: Haodong Rao | Dong Liu | Feng Jin | Nan Lv | Jungang Nan | Haiping Wang | Yanhui Yang | Jianyu Wang
Source: Crystals (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for Segmentation and Characterization of Microtextured Regions in a Near-α Titanium Alloy The development of automated segmentation and quantitative characterization of microtextured regions (MTRs) from the complex heterogeneous microstructures is urgently needed, since MTRs have been proven to be the critical issue that dominates the dwell-fatigue performance of aerospace components. In addition, MTRs in Ti alloys have similarities to microstructures encountered in other materials, including minerals and biomaterials. Meanwhile, machine learning (ML) offers new opportunities. This paper addresses segmentation and quantitative characterization of MTRs, where an ML approach, the Gaussian mixture models (GMMs) coupled with density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms, was employed in order to process the orientation data acquired via EBSD in the Matlab environment. Pixels with orientation information acquired through electron backscatter diffraction (EBSD) are divided and colored into several “classes” (MTRs) within the defined c-axis misorientations (i.e., 25°, 20°, 15°, 10°, and 5°), the precision and efficacy of which are verified by the morphology and pole figure of the segmented MTR. An appropriate range of c-axis misorientations for MTR segmentation was derived, i.e., 15~20°. The contribution of this innovative technique is compared with previous studies. At the same time, the MTRs were statistically characterized in the global region. Electron backscatter diffraction ; Materials science ; Segmentation ; Cluster analysis ; Artificial intelligence ; Characterization (materials science) ; Computer science ; Microstructure ; Pattern recognition (psychology) ; Composite material ; Nanotechnology Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Prachi Garg | Scott Broderick | Baishakhi Mazumder
Source: Journal of the American Ceramic Society (article, 2023-04-05)
Field: Materials Science — Ceramics and Composites

Machine learning‐based accelerated design of fluorphlogopite glass ceramic chemistries with targeted hardness Abstract In this work, we develop and employ an accelerated design strategy using a machine learning algorithm to overcome the challenges for designing a new machinable glass ceramic. The trained machine learning model predicts the specific hardness value for numerous possibilities of processing conditions such as growth temperature and time. We report that the optimized growth parameters of 1200°C and 5 h achieve the highest machinability of 0.4 in the glass ceramic. Furthermore, we predicted the eight most promising candidates containing specific ratios of silicon, magnesium, aluminum, lithium, boron, potassium, barium, and oxygen. Combining machine learning with experimental data enables a systemic and rapid design of a ceramic material while capturing the underlying physics represented in the experimental data. Machinability ; Ceramic ; Glass-ceramic ; Materials science ; Barium ; Lithium (medication) ; Boron ; Silicon ; Aluminium ; Machine learning ; Mechanical engineering ; Composite material ; Nanotechnology ; Process engineering ; Metallurgy ; Computer science ; Engineering ; Chemistry ; Machining Glass properties and applications ; Ferroelectric and Piezoelectric Materials ; Luminescence Properties of Advanced Materials