jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yining Zhang | Sijie Xing | Lai Wei | Tongfei Shi
Source: ACS Omega (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning Models for Predicting Diamagnetic Susceptibility of Organic Compounds This research is centered on examining the magnetic characteristics of organic molecules, with a particular emphasis on magnetic susceptibility, an essential physical property that provides insights into molecular microstructures and reaction processes. Traditional approaches for determining and calculating magnetic susceptibility are generally inefficient and demanding. To overcome these challenges, we have introduced a novel approach using quantitative structure-property relationships, which efficiently elucidates the relationship between the structural properties of molecules and their molar magnetic susceptibility. In our study, we utilized a comprehensive database comprising molar magnetic susceptibility data for 382 organic molecules. We applied six distinct molecular fingerprinting methods-RDKit Fingerprint, Morgan Fingerprint, MACCS Keys, atom pair fingerprint, Avalon Fingerprint, and topology fingerprint-as feature inputs for training seven different machine learning models, namely random forest, AdaBoost, gradient boosting, extra trees, elastic net, support vector machine, and multilayer perceptron (MLP). Our findings revealed that the integration of the atom pair fingerprint with the MLP model yielded <i>R</i><sup>2</sup> values of 0.88 and 0.90 in the validation and test sets, respectively, showcasing exceptional predictive accuracy. This advancement significantly expedites research and development processes related to the magnetic properties of organic molecules. Moreover, by employing this effective predictive method, it is expected to considerably reduce both experimental and computational expenses while maintaining high accuracy. This development represents a breakthrough in the rapid screening and prediction of properties for various compounds, offering a new and efficient pathway in this field of study. Fingerprint (computing) ; Diamagnetism ; Magnetic susceptibility ; Artificial intelligence ; Computer science ; Machine learning ; Random forest ; Gradient boosting ; Multilayer perceptron ; Cheminformatics ; Organic molecules ; Biological system ; Pattern recognition (psychology) ; Data mining ; Chemistry ; Molecule ; Artificial neural network ; Magnetic field ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ivelina Shiskova | Dicho Stratiev | Sotir Sotirov | Evdokia Sotirova | Rosen Dinkov | Iliyan Kolev | Denis D. Stratiev | Svetoslav Nenov | Simeon Ribagin | Krassimir Atanassov | Dobromir Yordanov | Frans van den Berg
Source: Processes (article, 2024-08-20)
Field: Chemistry — Analytical Chemistry

Predicting Petroleum SARA Composition from Density, Sulfur Content, Flash Point, and Simulated Distillation Data Using Regression and Artificial Neural Network Techniques The saturate, aromatic, resin, and asphaltene content in petroleum (SARA composition) provides valuable information about the chemical nature of oils, oil compatibility, colloidal stability, fouling potential, and other important aspects in petroleum chemistry and processing. For that reason, SARA composition data are important for petroleum engineering research and practice. Unfortunately, the results of SARA composition measurements reported by diverse laboratories are frequently very dissimilar and the development of a method to assign SARA composition from oil bulk properties is a question that deserves attention. Petroleum fluids with great variability of SARA composition were employed in this study to model their SARA fraction contents from their density, flash point, sulfur content, and simulated distillation characteristics. Three data mining techniques: intercriteria analysis, regression, and artificial neural networks (ANNs) were applied. It was found that the ANN models predicted with higher accuracy the contents of resins and asphaltenes, whereas the non-linear regression model predicted most accurately the saturate fraction content but with an accuracy that was lower than that reported in the literature regarding uncertainty of measurement. The aromatic content was poorly predicted by all investigated techniques, although the prediction of aromatic content was within the uncertainty of measurement. The performed study suggests that as well as the investigated properties, additional characteristics need to be explored to account for complex petroleum chemistry in order to improve the accuracy of SARA composition prognosis. Asphaltene ; Distillation ; Fraction (chemistry) ; Artificial neural network ; Composition (language) ; Petroleum ; Linear regression ; Chemistry ; Petroleum engineering ; Process engineering ; Computer science ; Chromatography ; Machine learning ; Organic chemistry ; Geology ; Engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Phase Equilibria and Thermodynamics
Authors: Edem Honu | Shengmin Guo | M. Shafiqur Rahman | Congyuan Zeng | Patrick Kwabena Mensah
Source: Hydrogen (article, 2024-09-01)
Field: Materials Science — Metals and Alloys

Advancing Hydrogen Gas Utilization in Industrial Boilers: Impacts on Critical Boiler Components, Mitigation Measures, and Future Perspectives This review sets out to investigate the detrimental impacts of hydrogen gas (H2) on critical boiler components and provide appropriate state-of-the-art mitigation measures and future research directions to advance its use in industrial boiler operations. Specifically, the study focused on hydrogen embrittlement (HE) and high-temperature hydrogen attack (HTHA) and their effects on boiler components. The study provided a fundamental understanding of the evolution of these damage mechanisms in materials and their potential impact on critical boiler components in different operational contexts. Subsequently, the review highlighted general and specific mitigation measures, hydrogen-compatible materials (such as single-crystal PWA 1480E, Inconel 625, and Hastelloy X), and hydrogen barrier coatings (such as TiAlN) for mitigating potential hydrogen-induced damages in critical boiler components. This study also identified strategic material selection approaches and advanced approaches based on computational modeling (such as phase-field modeling) and data-driven machine learning models that could be leveraged to mitigate potential equipment failures due to HE and HTHA under elevated H2 conditions. Finally, future research directions were outlined to facilitate future implementation of mitigation measures, material selection studies, and advanced approaches to promote the extensive and sustainable use of H2 in industrial boiler operations. Boiler (water heating) ; Hydrogen embrittlement ; Hydrogen ; Environmental science ; Risk analysis (engineering) ; Process engineering ; Engineering ; Waste management ; Business ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Daniel T. Speckhard | Tim Bechtel | Luca M. Ghiringhelli | Martin Kubáň | Santiago Rigamonti | Claudia Draxl
Source: Faraday Discussions (article, 2024-07-11)
Field: Materials Science — Materials Chemistry

How big is big data? Big data has ushered in a new wave of predictive power using machine-learning models. In this work, we assess what big means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work. Big data ; Ask price ; Computer science ; Context (archaeology) ; Data science ; Predictive power ; Quality (philosophy) ; Set (abstract data type) ; Data set ; Volume (thermodynamics) ; Feature (linguistics) ; Work (physics) ; Data quality ; Machine learning ; Artificial intelligence ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Kirill Sidnov | D. A. Konov | E. A. Smirnova | Alena V. Ponomareva | M. P. Belov
Source: Metals (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Elastic Properties Using Reduced Datasets of Accurate Calculations Results In this paper, the applicability of machine learning for predicting the elastic properties of binary and ternary bcc Ti and Zr disordered alloys with 34 different doping elements is explored. The original dataset contained 3 independent elastic constants, bulk moduli, shear moduli, and Young’s moduli of 1642 compositions calculated using the EMTO-CPA method and PAW-SQS calculation results for 62 compositions. The architecture of the system is made as a pipeline of a pair of predicting blocks. The first one took as the input a set of descriptors of the qualitative and quantitative compositions of alloys and approximated the EMTO-CPA data, and the second one took predictions of the first model and trained on the results of the PAW-SQS calculations. The main idea of such architecture is to achieve prediction accuracy at the PAW-SQS level, while reducing the resource intensity for obtaining the training set by a multiple of the ratio of the training subsets sizes corresponding to the two used calculation methods (EMTO-CPA/PAW-SQS). As a result, model building and testing methods accounting for the lack of accurate training data on the mechanical properties of alloys (PAW-SQS), balanced out by using predictions of inaccurate resource-effective first-principle calculations (EMTO-CPA), are demonstrated. Ternary operation ; Moduli ; Binary number ; Set (abstract data type) ; Elastic modulus ; Computer science ; Pipeline (software) ; Materials science ; Mathematics ; Algorithm ; Physics ; Composite material ; Arithmetic Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Himanshu Priyadarshi | Gulzar Ahmed | Dhaneshwar Mishra | Ashish Kumar Srivastava | Randolph C.V. McGee | Ashish Shrivastava | Kulwant Singh
Source: Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences (article, 2024-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainable graphene-based energy storage device technology: Materials, methods, monitoring and digital twin Energy harvesting is possible through capable energy transfer materials, and one such impressive material is graphene, which has exhibited promising properties like unprecedentedly high theoretical surface area, enhanced electrical conductivity, thermal conductivity, mechanical stability, flexibility, recyclability, and so on. Herein, for the sake of everyone desirous of contributing to the field of graphene materials for high-speed energy storage devices, the fundamentals, analytics, synthesis, prospects, and challenges of energy storage cell design for fast charging of electric vehicles have been reviewed. To overcome the limitations of mechanistic models for energy-storage devices, empiricism conjointly with data-driven machine learning has been quite effective in multiple domains. The limitations in modeling of energy storage devices, in terms of swiftness and accuracy in their state prediction can be surmounted by the aid of machine learning. Conclusively, in the context of energy management, we underscore the significant challenges related to modeling accuracy, performing original computations, and relevant big data generation. This review, by dint of its futuristic insights, will help researchers to develop digital twin approach for sustainable energy management using energy storage technology toward dependable, economic, and scalable optimization processes at the multiple stages of energy conversion and energy management. Graphene ; Sustainable energy ; Digital storage ; Nanotechnology ; Materials science ; Engineering physics ; Process engineering ; Computer science ; Engineering ; Electrical engineering ; Renewable energy ; Computer hardware Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Sun-Ho Choi | Joonbum Lee | Jangwon Seo | Sung Won Han | Sang Hyun Lee | Ji‐Hun Seo | Junhee Seok
Source: Scientific Data (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigSMILES as an alternative method suitable for the representation of macromolecules. Nevertheless, research on BigSMILES remains limited due to its preprocessing requirements. Thus, this study proposes a conversion workflow of BigSMILES, focusing on its automated generation from SMILES representations of homopolymers. BigSMILES representations for 4,927,181 records are provided, thereby enabling its immediate use for various research and development applications. Our study presents detailed descriptions on a validation process to ensure the accuracy, interchangeability, and robustness of the conversion. Additionally, a systematic overview of utilized codes and functions that emphasizes their relevance in the context of BigSMILES generation are produced. This advancement is anticipated to significantly aid researchers and facilitate further studies in BigSMILES representation, including potential applications in deep learning and further extension to complex structures such as copolymers. Workflow ; Computer science ; Computational biology ; Information retrieval ; Chemistry ; Biology ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Longfei Guo | Shuang Shan | Xiaoqing Liu | Wanxuan Zhang | Peng Xu | Fanzhe Ma | Zhen Li | Chongyang Wang | Junpeng Wang | Fuyi Chen
Source: The Journal of Chemical Physics (article, 2024-05-03)
Field: Materials Science — Materials Chemistry

Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential The design of heterogeneous catalysts generally involves optimizing the reactivity descriptor of adsorption energy, which is inevitably governed by the structure of surface-active sites. A prerequisite for understanding the structure-properties relationship is the precise identification of real surface-active site structures, rather than relying on conceived structures derived from bulk alloy properties. However, it remains a formidable challenge due to the dynamic nature of nanoalloys during catalytic reactions and the lack of accurate and efficient interatomic potentials for simulations. Herein, a generalizable deep-learning potential for the Ag-Pd-F system is developed based on a dataset encompassing the bulk, surface, nanocluster, amorphous, and point defected configurations with diverse compositions to achieve a comprehensive description of interatomic interactions, facilitating precise prediction of adsorption energy, surface energy, formation energy, and diffusion energy barrier and is utilized to investigate the structural evolutions of AgPd nanoalloys during fluorination. The structural evolutions involve the inward diffusion of F, the outward diffusion of Ag in Ag@Pd nanoalloys, the formation of surface AgFx species in mixed and Janus AgPd nanoalloys, and the shape deformation from cuboctahedron to sphere in Ag and Pd@Ag nanoalloys. Moreover, the effects of atomic diffusion and dislocation formation and migration on the reconstructing pathway of nanoalloys are highlighted. It is demonstrated that the stress relaxation upon F adsorption serves as the intrinsic driving factor governing the surface reconstruction of AgPd nanoalloys. Mechanism (biology) ; Computer science ; Biochemical engineering ; Data science ; Artificial intelligence ; Engineering ; Epistemology ; Philosophy Machine Learning in Materials Science ; ZnO doping and properties ; Catalytic Processes in Materials Science
Authors: Rong Cheng | Xuyan Xue | Cai‐Zhuang Wang
Source: The Journal of Physical Chemistry C (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Exploration of Ternary Metal Borohydrides We employ deep machine learning (ML) combined with first-principles calculations to explore energetically favorable ternary metal borohydrides. Using La–B–H as a prototype system, we demonstrate that iteratively trained ML models can efficiently screen hundreds of thousands of hypothetical structures and accurately select a small fraction of promising structures and compositions for further studies by first-principles calculations. Such an ML-guided approach dramatically accelerates the pace of materials discovery. A number of new La–B–H ternary compounds with formation energies within 100 meV/atom above the known ternary convex hull are discovered, including a known stable La(BH4)3 phase. Moreover, by replacing La with Group 1, 2, 3, 13, and 14 elements in the four lowest-energy La–B–H structures from our ML-guided predictions, several low-energy X–B-H (X = Mg, Ca, Sr, Ba, Sc, Y, Ac, Al, Ga, In, Si, Ge, Sn, Pb) compounds are predicted. Ternary operation ; Convex hull ; Atom (system on chip) ; Metal ; Group (periodic table) ; Materials science ; Crystallography ; Regular polygon ; Chemistry ; Physics ; Computer science ; Mathematics ; Geometry ; Metallurgy ; Parallel computing ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Boron and Carbon Nanomaterials Research
Authors: Feng Zhang | Ryo Tamura | Fanyu Zeng | Daichi Kozawa | Ryo Kitaura
Source: ACS Applied Materials & Interfaces (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS<sub>2</sub> We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS<sub>2</sub> using photoluminescence (PL) intensity as the objective function. Through iterative experiments guided by BO, an improvement of 86.6% in PL intensity is achieved within 13 optimization rounds. Statistical analysis revealed the relationships between growth conditions and PL intensity, highlighting the importance of critical conditions, including the tungsten source concentration and Ar flow rate. Furthermore, the effectiveness of BO is demonstrated by comparison with random search, showing its ability to converge to optimal conditions with fewer iterations. This research highlights the potential of ML-driven approaches in accelerating material synthesis and optimization processes, paving the way for advances in two-dimensional (2D) material-based technologies. Bayesian optimization ; Materials science ; Intensity (physics) ; Photoluminescence ; Chemical vapor deposition ; Monolayer ; Bayesian probability ; Random search ; Biological system ; Nanotechnology ; Computer science ; Optoelectronics ; Machine learning ; Algorithm ; Artificial intelligence ; Optics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Shiyu He | Fei Xiao | Ruihang Hou | Shungui Zuo | Ying Zhou | Xiaorong Cai | Li Zhu | Yanming Wang | A. Aysu Catal-Isik | E.I. Galindo-Nava | Xuejun Jin
Source: Rare Metals (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Accelerated learning and co‐optimization of elastocaloric effect and stress hysteresis of elastocaloric alloys Abstract The development of high‐performance advanced elastocaloric alloys is crucial for the implementation of elastocaloric refrigeration. Here, we present a machine learning (ML) framework to accelerate the development of novel elastocaloric alloys with large adiabatic temperature change (Δ T ad ) and low stress hysteresis (Δ σ hy ). The comprehensive framework comprises database construction, feature selection, model construction, alloy design and validation, and model interpretation. Features are selected according to the physical attributes they represent. Properties that may reflect the compatibility between parent and product phases, lattice distortion and the free energy in the alloy are considered in the model. Among them, the key features are selected by recursive feature elimination and exhaustive search methods. The trained models in combination with the Bayesian optimization method are exploited to achieve multi‐objective optimization. According to the results, a newly designed elastocaloric alloy shows a large adiabatic temperature change of 15.2 K and low average stress hysteresis of 70.3 MPa at room temperature, which is consistent with our predictions. The predictions of our ML model are interpreted by the Shapley additive exPlainations (SHAP) approach, which explicitly quantifies the effects of each feature in our model on the adiabatic temperature change and stress hysteresis. Additionally, we employ the Sure‐independence screening and sparsifying operator (SISSO) method in conjunction with the key features to formulate explicit model. Hysteresis ; Materials science ; Stress (linguistics) ; Metallurgy ; Condensed matter physics ; Physics Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: Jiaxin Zhang | Lingxue Zhang | Yuxuan Sun | Wei Li | Ruge Quhe
Source: Computational Materials Science (article, 2024-04-13)
Field: Materials Science — Materials Chemistry

Named entity recognition in the perovskite field based on convolutional neural networks and MatBERT Conditional random field ; Bottleneck ; Convolutional neural network ; Computer science ; Perovskite (structure) ; Field (mathematics) ; Named-entity recognition ; Artificial intelligence ; Feature (linguistics) ; Characterization (materials science) ; Artificial neural network ; Deep learning ; Natural language processing ; Layer (electronics) ; Machine learning ; Materials science ; Nanotechnology ; Task (project management) ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Sentiment Analysis and Opinion Mining