jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamad Ataya | Eric McCalla | Rustam Z. Khaliullin
Source: The Journal of Physical Chemistry C (article, 2024-08-19)
Field: Materials Science — Materials Chemistry

Machine Learning for High-Throughput Configuration Sampling of Li−La−Ti−O Disordered Solid-State Electrolyte Most solid-state lithium electrolytes are disordered ionic crystalline materials that possess crystallographic sites that can be vacant or occupied by different ions. The presence of these partially occupied sites enables lithium diffusion through their lattice and makes such materials promising for developing all-solid batteries. High-throughput computational screening of such materials must bypass costly DFT sampling of disordered configurations and, therefore, commonly relies on the computationally efficient Coulomb approximation to find just a few representative low-energy ionic configurations, for which DFT is then used to quickly predict a number of important materials’ properties, such as the electrochemical stability window. This work demonstrates, using the Li−La−Ti−O solid electrolyte (LLTO) as an example, that the Coulomb approximation fails to correctly detect the most stable arrangement of Li and La ions in the LLTO, which has a noticeable impact on the accuracy of subsequent computational prediction of the electrochemical stability window of the material. The analysis herein shows that the sampling problem arises from the relatively modest geometry relaxation of the LLTO lattice. A kernel ridge regression machine learning (ML) method employing the smooth overlap of atomic positions as a structure descriptor (SOAP-KRR) leads to significant improvements in detecting the most stable configurations of the LLTO. The universal ML potential based on the multiple atomic cluster expansion is also found to be reliable but to a lesser extent than SOAP-KRR. Remarkably, accurate energies can be obtained with SOAP-RKK trained on as few as 40 LLTO structures, making this method promising for designing force matching ML potentials that can serve as a computationally inexpensive alternative to the costly DFT structure relaxation in high-throughput screening of large data sets of ionic materials. Electrolyte ; Throughput ; Solid-state ; Sampling (signal processing) ; Materials science ; Computer science ; High-throughput screening ; Chemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Physical chemistry ; Electrode ; Telecommunications ; Biochemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Landon Johnson | Walter Malone | Jason Rizk | Renai Chen | Tammie Gibson | M. Cooper | Galen T. Craven
Source: Computational Materials Science (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Machine learning method to determine concentrations of structural defects in irradiated materials Context (archaeology) ; Cluster (spacecraft) ; Materials science ; Irradiation ; Computer science ; Field (mathematics) ; Structural material ; Biological system ; Statistical physics ; Physics ; Mathematics ; Composite material ; Nuclear physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Yilin Hu | Maokun Wu | Miaojia Yuan | Yichen Wen | Pengpeng Ren | S. Ye | Fayong Liu | Bo Zhou | Hui Fang | Runsheng Wang | Zhigang Ji | Ru Huang
Source: Applied Physics Letters (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach The conventional approach to exploring suitable dielectrics for future logic and memory devices relies on first-principle calculations, which are expensive and time-consuming. In this work, we adopt a data-driven machine learning (ML)-based approach to build a model for predicting these properties. By incorporating structural information into the input descriptors, we achieve record-high accuracy in predicting the dielectric constant, with the coefficients of determination (R2) of 0.886 and root mean square error (RMSE) of 0.083. Additionally, we achieve high predictions for the bandgap, with accuracies of 0.832 and 0.533 for R2 and RMSE, respectively. The features corresponding to specific properties are analyzed to obtain physical insights. Finally, we employ first-principle calculations to validate the feasibility of this model. This work proposes a highly efficient approach for using ML to predict material properties. Dielectric ; Materials science ; Engineering physics ; Computer science ; Optoelectronics ; Physics Machine Learning in Materials Science ; Semiconductor materials and devices ; X-ray Diffraction in Crystallography
Authors: Yukinori Koyama | Yukako Kohriki | Masamichi Harada | Naoto Hirosaki | Takashi Takeda
Source: Chemistry of Materials (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences An approach is presented to accelerate the discovery of host compounds for novel Eu2+-activated phosphor materials by integrating systematic data collection, machine learning, and experimental validation. A data set of Eu2+- and Eu3+-activated phosphors has been constructed using systematic data collection methodology from numerous academic articles. A machine-learning classification model has been developed using the collected data set to predict the oxidation states of Eu ions in potential hosts regarding luminescence. The model considers the nonexclusive nature of the divalent and trivalent oxidation states of Eu ions in phosphor applications. A comprehensive exploration of a materials database was conducted to identify host candidates for novel Eu2+-activated phosphor materials, leading to attempts to synthesize them. Photoluminescence analysis revealed the successful synthesis of 12 new Eu2+-activated phosphors, demonstrating the potential of the proposed approach for accelerating material discovery. Europium ; Phosphor ; Materials science ; Computer science ; Chemistry ; Optoelectronics ; Luminescence Machine Learning in Materials Science ; Radioactive element chemistry and processing ; X-ray Diffraction in Crystallography
Authors: Mohammed Al‐Fahdi | Changpeng Lin | Chen Shen | Hongbin Zhang | Ming Hu
Source: Materials Today Physics (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling Materials science ; Phonon ; Throughput ; Band gap ; Artificial neural network ; Optoelectronics ; Condensed matter physics ; Artificial intelligence ; Computer science ; Physics ; Telecommunications ; Wireless Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Hirokuni Jintoku | Don N. Futaba
Source: ACS Applied Materials & Interfaces (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals Dispersion represents a central processing method in the organization of nanomaterials; however, the strong interparticle interaction represents a significant obstacle to fabricating homogeneous and stable dispersions. While dispersants can greatly assist in overcoming this obstacle, the appropriate type is dependent on such factors as nanomaterial, solvent, experimental conditions, etc., and there is no general guide to assist in the selection from the vast number of possibilities. We report a strategy and successful demonstration of the machine-learning-based "Dispersant Explorer", which surveys and identifies suitable dispersants from open databases. Through the combined use of experimental and molecular descriptors derived from SMILES databases, the model showed exceptional predictive accuracy in surveying about ∼1000 chemical compounds and identifying those that could be applied as dispersants. Furthermore, fabrication of transparent conducting films using the predicted and previously unknown dispersant exhibited the highest sheet resistance and transmittance compared with those of other reported undoped films. This result highlights that, in addition to opening new avenues for novel dispersant discovery, machine learning has a potential to elucidate the chemical structures essential for optimal dispersion performance to assist in the advancement of the complex topic of nanomaterial processing. Dispersant ; Materials science ; Nanomaterials ; Homogeneous ; Nanotechnology ; Dispersion (optics) ; Identification (biology) ; Solvent ; Obstacle ; Fabrication ; Biochemical engineering ; Computer science ; Artificial intelligence ; Organic chemistry ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: J P Supriya | Raviraj Shetty | Nithesh Naik | M. Srinivasulu | Adithya Hegde
Source: Scientific Reports (article, 2024-09-28)
Field: Materials Science — Polymers and Plastics

Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach The integration of natural fibers into Fiber Reinforced Polymers (FRPs) has emerged as a promising avenue for sustainable and high-performance composite materials. Natural fibers, derived from plants, offer notable advantages such as renewability, low cost, and environmental friendliness. Among these natural fibers, Hibiscus Rosa-Sinensis (HRS) plant fibers have gained significant attention owing to their widespread availability and unique mechanical properties. In this study, HRS fibers were chemically treated using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO<sub>4</sub>), and Acetic Acid (CH<sub>3</sub>COOH) at different weight percentages (3, 4, 5 Wt.%) and solutionizing times (1, 2, 3 h) based on Taguchi's L<sub>27</sub> orthogonal array. The fibers, extracted from epidermis of the stems, underwent cleaning and chemical treatment after water retting. The crystallinity index, determined via X-ray Diffraction (XRD), indicated a maximum value of 65.77%. Thermo-gravimetric analysis (TGA) exhibited a degradation temperature of 365.24 °C and a material loss of 63.11%. Potassium Permanganate treatment at 4 Wt.% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. The results are in close alignment with the literature. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analyses have provided valuable insights into the microstructure and constituents of the chemically treated HRS fibers. This research emphasises on the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications. Retting ; Crystallinity ; Materials science ; Potassium permanganate ; Synthetic fiber ; Fiber ; Natural fiber ; Thermogravimetric analysis ; Scanning electron microscope ; Nuclear chemistry ; Composite material ; Chemical engineering ; Pulp and paper industry ; Chemistry ; Organic chemistry Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Mechanical Engineering and Vibrations Research
Authors: Zhiwei Zheng | Siyi Qiu | Xuezheng Yue | Jincheng Wang | Juan Hou
Source: Journal of Nuclear Materials (article, 2024-04-23)
Field: Materials Science — Materials Chemistry

Detecting irradiation defects in materials: A machine learning approach to analyze helium bubble images Bubble ; Irradiation ; Helium ; Materials science ; Liquid bubble ; Nuclear engineering ; Radiochemistry ; Artificial intelligence ; Computer science ; Nuclear physics ; Chemistry ; Physics ; Atomic physics ; Engineering ; Operating system Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Kavan Shah | Krishna Kiran Talamadupula | Pınar Acar | Gary D. Seidel
Source: Computational Materials Science (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Reduced-order model for multiphysics simulations of CNT/Polymer Composites via principal component regression and artificial neural networks Multiphysics ; Principal component analysis ; Artificial neural network ; Composite material ; Materials science ; Component (thermodynamics) ; Polymer ; Biological system ; Order (exchange) ; Computer science ; Artificial intelligence ; Finite element method ; Structural engineering ; Engineering ; Thermodynamics ; Physics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Ultrasonics and Acoustic Wave Propagation
Authors: Jingtao Huang | Jingteng Xue | Mingwei Li | Yuan Cheng | Zhonghong Lai | Jin Hu | Fei Zhou | Nan Qu | Yong Liu | Jingchuan Zhu
Source: Science China Materials (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Diffusive migration behavior of single atoms in aluminum alloy substrates: Explaining machine-learning-accelerated first principles calculations Diffusion ; Materials science ; Matrix (chemical analysis) ; Generalization ; Atom (system on chip) ; Alloy ; Statistical physics ; Computer science ; Algorithm ; Thermodynamics ; Mathematics ; Mathematical analysis ; Physics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Aluminum Alloy Microstructure Properties
Authors: Vikram Sudarshan | Warren D. Seider | Amish J. Patel | Ulku Oktem | Jeffrey E. Arbogast
Source: Chemical Engineering Science (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Path-Sampling and Machine Learning for Rare Abnormal Events: Application to Polymerization CSTRs • Multivariate alarm systems developed using path-sampling and machine learning. • BGFFS utilized to simulate efficiently data for numerous rare abnormal trajectories. • XGBoost, an advanced ML algorithm, utilized to develop accurate predictive models. • Initial alarm systems enhanced using alarm rationalization and DRAn. • Demonstrated on a polymerization CSTR controlled in its unstable operating region. Previously, we developed novel, unidirectional, static, multivariate, alarm systems for rare un-postulated abnormal events, demonstrated successfully for an exothermic CSTR. Herein, our techniques are improved significantly for a more-complex polystyrene CSTR, operating in its unstable region, capable of abnormal shifts to two undesirable regions; i.e., unsafe and unreliable regions. BG-FFS, a path-sampling algorithm, is utilized to simulate efficiently multiple rare abnormal trajectories; then, the XGBoost machine learning algorithm is utilized to develop accurate predictive models for committer probabilities; i.e., p B as a function of key process variables – such models, when deployed in real-time, result in improved bidirectional dynamic multivariate alarm systems, capable of response actions using real-time p B predictions. Then, using our rationalization strategies, the initial alarm systems are evaluated and modified, followed by DRAn (Dynamic Risk Analysis) studies and sensitivity analyses to investigate the effects of varying other process parameters to achieve more effective response actions. Path (computing) ; Polymerization ; Sampling (signal processing) ; Rare events ; Computer science ; Materials science ; Mathematics ; Polymer ; Statistics ; Composite material ; Operating system ; Telecommunications Machine Learning in Materials Science ; Fault Detection and Control Systems ; Industrial Vision Systems and Defect Detection
Authors: Goele Magchiels | Niels Claessens | Johan Meersschaut | A. Vantomme
Source: Scientific Reports (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected in two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties. Computer science ; Artificial intelligence ; Machine learning ; Data mining Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Waseem Akhtar | Ishfaq Ahmed | Jingtao Huang | Shanza Mubashir | Jiaying Chen | Jingteng Xue | Yong Liu | Qu Nan | Jingchuan Zhu
Source: Physica Scripta (article, 2024-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting elastic and electronic properties of quaternary Heusler alloys using machine learning and DFT calculations Abstract The Decision Tree (DT) Regression model is employed to predict the elastic and electronic characteristics of quaternary Heusler alloys using pre-existing and newly developed databases. The previously discussed were used to gather, preprocess, clean, and prepare the data so that the model would be more productive and efficient. The performance of the (DT) model demonstrates its robustness against overfitting, outliers, multicollinearity, and distribution of data points, in addition to being accurate enough to predict the response variables on the test data. The parity plots comparing the machine learning predicted values with the computed values using density functional theory (DFT) exhibit a linear relationship, with adjusted R 2 and MSE values predominantly lying within the range of 0.80 to 0.93 and 0.10 to 0.22 for all predicted properties of the quaternary Heusler alloy. In light of the results anticipated from the model analysis, a Validation is cited comparing ML predicted outcomes with first principle calculations based on (DFT) by selecting MnFeNiAl QHA, indicating the DT model’s excellent precision with a 13% average error, which suggests high accuracy of results predicted by the model. Materials science ; Electronic structure ; Condensed matter physics ; Physics Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Daniel Willimetz | Andreas Erlebach | Christopher J. Heard | Lukáš Grajciar
Source: Digital Discovery (article, 2024-12-09)
Field: Chemistry — Spectroscopy

<sup>27</sup> Al NMR chemical shifts in zeolite MFI <i>via</i> machine learning acceleration of structure sampling and shift prediction Accurate prediction of 27 Al NMR chemical shifts in zeolites at operating conditions via a combination of neural network potential-driven dynamics sampling relevant structures and regression models for shift prediction. Zeolite ; Acceleration ; Sampling (signal processing) ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Physical chemistry ; Physics ; Catalysis ; Organic chemistry ; Optics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Md. Amjad Hossain | Kira Coe-Sessions | J.W. Ault | Felix O. Gboyero | Michael J. Wenzel | Bhausaheb Dhokale | Alathea E. Davies | Qian Yang | Laura de Sousa Oliveira | Xuesong Li | John O. Hoberg
Source: Frontiers in Chemistry (review, 2024-12-18)
Field: Materials Science — Materials Chemistry

Covalent integration of polymers and porous organic frameworks Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research. Materials science ; Polymer ; Thermogravimetric analysis ; Nanotechnology ; Porosity ; Hybrid material ; Covalent organic framework ; Metal-organic framework ; Covalent bond ; Chemical engineering ; Adsorption ; Organic chemistry ; Chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials