jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoxuan Liu | Haile Yan | Nan Jia | Bo Yang | Zongbin Li | Xiang Zhao | Liang Zuo
Source: Materials (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Discovery of Empirical Rule for Martensite Transition Temperature of Shape Memory Alloys Shape memory alloys (SMAs) derive their unique functional properties from martensitic transformations, with the martensitic transformation temperature (<i>T</i><sub>M</sub>) serving as a key design parameter. However, existing empirical rules, such as the valence electron concentration (VEC) and lattice volume (V) criteria, are typically restricted to specific alloy families and lack general applicability. In this work, we used a data-driven methodology to find a generalizable empirical formula for <i>T</i><sub>M</sub> in SMAs by combining high-throughput first-principles calculations, feature engineering, and symbol regression techniques. Key factors influencing <i>T</i><sub>M</sub> were first identified and a predictive machine learning model was subsequently trained based on these features. Furthermore, an empirical formula of <i>T</i><sub>M</sub> = 82(ρ¯·MP¯)-700 was derived, where ρ¯ and MP¯ represent the weight-average value of density and melting point, respectively. The empirical formula exhibits strong generalizability across a wide range of SMAs, such as NiMn-based, NiTi-based, TiPt-based, and AuCd-based SMAs, etc., offering practical guidance for the compositional design and optimization of shape memory alloys. Martensite ; Shape-memory alloy ; Materials science ; Transition (genetics) ; Metallurgy ; Chemistry ; Microstructure Shape Memory Alloy Transformations ; Machine Learning in Materials Science
Authors: Jiali Ren | Guanghao Zhang | Xuemei Wang | Ye Han
Source: Physica B Condensed Matter (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Research on machine learning interatomic potentials for titanium oxide ceramic materials Materials science ; Ceramic ; Interatomic potential ; Titanium ; Oxide ; Titanium oxide ; Composite material ; Engineering physics ; Chemical engineering ; Molecular dynamics ; Metallurgy ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Nek Muhammad Katbar | Shengjun Liu | Hongjuan Liu
Source: Journal of Computational Biophysics and Chemistry (article, 2025-04-11)
Field: Chemistry — Spectroscopy

A Computational Investigation of the Glycolysis Reaction–Diffusion System via Radial Basis Function Neural Networks The metabolic process known as glycolysis, which occurs within the cytosol, is considered a fundamental model of a widely applicable pathway for fulfilling cellular energy needs. Its extensive presence suggests that glycolysis is among the earliest recognized metabolic routes, representing a prevalent method for supplying limited energy to organisms in the natural living environment. However, its importance is underscored by its ability to swiftly provide energy, particularly in oxygen-deprived scenarios, such as intense physical activity and high-altitude hypoxia. Glycolysis has emerged as an illustrative and classic system for biochemical reactions. The objective of this study was to evaluate the efficacy of a radial basis function neural network, which employs radial basis functions as activation functions, in conducting an in-depth mathematical examination of the general glycolysis model, particularly its spatiotemporal pattern formation. The glycolysis model was designed to compute pattern formation in glucose concentrations. The main objective is to compute the spatial and wavy patterns from the chemical concentration. Our findings also indicate that the radial basis function neural network is an excellent tool for analyzing models of this nature. Reaction–diffusion system ; Radial basis function ; Artificial neural network ; Basis (linear algebra) ; Function (biology) ; Glycolysis ; Diffusion ; Computer science ; Biological system ; Physics ; Mathematics ; Artificial intelligence ; Mathematical analysis ; Biology ; Geometry ; Nuclear magnetic resonance ; Thermodynamics ; Cell biology Analytical Chemistry and Chromatography
Authors: Kawther Laajimi | Emna Ammar Elhadjamor | Radhouane Laajimi | Mehdi Rahmani | Mohamed Hichem Gazzah
Source: Results in Engineering (article, 2025-04-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks • The magnetic properties of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys were studied. • The variation of heat capacity with temperature and magnetic field was treated by ANN model. • ANN model exhibits high prediction performance of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ magnetic behavior. The growing interest in environmentally friendly cooling technologies has intensified the search for efficient magnetocaloric materials (MCMs). In this context, accurately predicting the magnetocaloric effect is essential for accelerating materials discovery and reducing experimental efforts. In this study, we introduce novel approaches for modeling magnetocaloric materials using artificial neural networks (ANNs). Our method aims to establish a standardized framework to evaluate the magnetocaloric effect of polycrystalline La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys obtained by sol-gel method. A multilayer perceptron regression model was used to predict entropy values with temperature and magnetic field variations. The model showed outstanding performance with a coefficient of determination (R²) of 0.999241 and a root mean square error (MSE) of 0.003472. In addition, the variation of heat capacity (ΔCp) under different magnetic fields was successfully predicted using the same approach, yielding a low median absolute error (MedAE) of 0.008. Our results demonstrate the potential of ANNs model to accurately approximate magnetic experimental data and highlight their ability to significantly streamline the design and optimization of newly developed MCMs. Magnetic refrigeration ; Artificial neural network ; Materials science ; Artificial intelligence ; Metallurgy ; Thermodynamics ; Computer science ; Physics ; Magnetic field ; Magnetization Magnetic and transport properties of perovskites and related materials ; Neural Networks and Applications
Authors: Arpan Choudhury | Debashree Ghosh
Source: Journal of Computational Chemistry (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Hybrid Unsupervised/Supervised Machine Learning for Identifying Molecular Structural Fingerprints From Ensemble Property The ensemble properties of a system are obtained by averaging over the properties calculated for the various configurations it can have at a finite temperature and thus cannot be captured by a single molecular structure. Such ensemble properties are often important in material discovery. In designing new materials, the goal is to predict those ensemble structures that display a tailored property. However, mapping this average property to multiple structures introduces ambiguities and unreliable convergence in supervised machine learning. This presents a major obstacle in designing new materials. Here, we introduce a hybrid unsupervised/supervised learning method and demonstrate how to predict the structural parameters defining the conformers of a heterogeneous system, melanin, from its ensemble-averaged spectra. This also shows a new way to identify different structural fingerprints responsible for an ensemble-averaged superposition spectrum. Ensemble learning ; Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Superposition principle ; Convergence (economics) ; Unsupervised learning ; Ensemble forecasting ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: S. Sridharan | Ramamoorthy Velayutham | S. N. Behera | Jayaprakash Murugesan
Source: Shape Memory and Superelasticity (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Ensemble-Based Machine Learning Prediction of the Temperature-Induced Properties of Ti-Based High-Temperature Shape Memory Alloy Alloy ; Materials science ; Shape-memory alloy ; Ensemble learning ; Artificial intelligence ; Computer science ; Machine learning ; Metallurgy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: Yuxin Tan | Ruifeng Dong | Zhiqiang Duan | Huanqing Li | Hua Hou | Yuhong Zhao
Source: Materials & Design (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Machine learning coupled with an improved exhaustive method-assisted design of a low cost Ti-Al-Cr-Fe-Si series titanium alloy • Machine learning coupled with the improved exhaustive method is used for the design of titanium alloys. • The improved exhaustive method avoids the waste of storage space and resources existing in the traditional exhaustive method. • A high-performance, low-cost titanium alloy, Ti-4.98Cr-1.89Fe-2.66Al-0.1Si, is designed. In response to the high cost of titanium alloys and the low efficiency of traditional alloy design methods, this work combines machine learning (ML) with an improved exhaustion method (IEM) to accurately determine the relationship between alloy composition and properties, thereby accelerating the design of low-cost titanium alloys with considerable mechanical properties. A Ti-Al-Cr-Fe-Si series of titanium alloys was designed and this series belong to the type of near β titanium alloys, in which a large number of α phase with an acicular morphology are densely distributed within the β matrix. The designed alloys not only utilize cost-effective alloying elements as a substitute for more expensive alternatives, significantly reducing the cost of raw materials, but also exhibit considerable mechanical properties. As for the designed alloy of Ti-4.98Cr-1.89Fe-2.66Al-0.1Si, the yield strength (YS) reaches about 1080 MPa, the tensile strength (UTS) about1280 MPa and the elongation (EL) about 18.6 %. This work is expected to provide a new avenue that can efficiently assist in the design of alloys with high performance. Materials science ; Series (stratigraphy) ; Titanium alloy ; Alloy ; Titanium ; Metallurgy Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Intermetallics and Advanced Alloy Properties
Authors: Zepeng Jia | Xi Sun | Hang Zhang | Zishan Luo | Yuhang Zhou | Sen Lu | Pei Song | Hong Cui | Weizhi Tian | Guanqi Wang | Feng Rong | Lingxia Jin | Hongkuan Yuan
Source: International Journal of Hydrogen Energy (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Machine learning descriptor-assisted exploration of metal-modified graphene hydrogen storage materials Hydrogen storage ; Graphene ; Materials science ; Metal ; Hydrogen ; Computer science ; Chemical engineering ; Nanotechnology ; Chemistry ; Process engineering ; Metallurgy ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hybrid Renewable Energy Systems
Authors: Haoran Cui | Weijian Hua | Lei Cao | Yifei Jin | Yan Wang
Source: Computational Materials Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep-neural-network molecular dynamics investigation of phonon thermal transport in polyether ether ketone Molecular dynamics ; Phonon ; Ketone ; Ether ; Thermal ; Dynamics (music) ; Chemical physics ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Organic chemistry ; Thermodynamics ; Condensed matter physics Thermal properties of materials ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Wei David Wang | Mingfeng Li | Yang Zhang | Kang Qin | Li Zhuang | Ping Yang | Hong Nie
Source: Fuel (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Structure-performance relationship between zeolites properties and hydrocracking performance of tetralin over NiMo/Al2O3-Y catalysts: A machine-learning-assisted study Tetralin ; Catalysis ; Cracking ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Nanomaterials for catalytic reactions
Authors: Anurag Mishra | Vikesh G. Lade | Jyoti Ramesh Barmar | Ankush B. Bindwal | Ramesh P. Birmod
Source: Journal of Molecular Modeling (review, 2025-04-30)
Field: Materials Science — Materials Chemistry

A mini review on revolutionizing hydrogenation catalysis: unleashing transformative power of artificial intelligence Transformative learning ; Power (physics) ; Catalysis ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Psychology ; Organic chemistry ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Hydrodesulfurization Studies
Authors: Daniel Vizoso | Phillip Tsurkan | Ke Ma | Avinash M. Dongare | Rémi Dingreville
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring the transferability of machine-learning models for analyzing XRD data of shocked microstructures: from single crystal to polycrystals This study explores the transferability of machine-learning models to analyze X-ray diffraction (XRD) profiles of shock-loaded single-crystal and polycrystalline data. Transferability ; Microstructure ; Materials science ; Single crystal ; Crystal (programming language) ; Crystallography ; Computer science ; Metallurgy ; Machine learning ; Chemistry ; Programming language Machine Learning in Materials Science ; Microstructure and mechanical properties ; X-ray Diffraction in Crystallography
Authors: Zhao‐Qing Liu | Z. Y. Deng | Huabo Zhao | Yan Wang | Mohan Chen | Hong Jiang
Source: Journal of Materials Informatics (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

FT<sup>2</sup>DP: large atomic model fine-tuned machine learning potential for accelerating atomistic simulation of iron-based Fischer-Tropsch synthesis Density-functional theory (DFT)-based atomistic simulation methods have been essential in studying the structure-property relationships in heterogeneous catalysis. However, for complex catalytic processes, such as iron-based Fischer-Tropsch synthesis (FTS), the temporal or spatial scales involved are generally too large to perform DFT calculations. Recently, the development of machine learning potentials (MLPs) has demonstrated the capability for atomistic simulation on a large scale and long duration, and the rise of large atomic models (LAMs) is gaining much attention with unified descriptors incorporating a wide range of chemical knowledge and fine-tuning methodology for efficiently deploying the model to downstream tasks. In this work, we construct a MLP named fine-tuned Fischer-Tropsch deep potential (FT$$ ^2 $$ DP) model, which is fine-tuned from upstream DPA-2 LAM on a downstream dataset focused on the iron-based FTS process. We further applied this model to investigate iron-based FTS in both surface reactions and reconstructions of edge sites combined with the double-to-single transition state optimization method and the local genetic algorithm. Our work demonstrated the capability and efficiency of our model for iron-based FTS simulations, while revealing the reaction mechanism on common active sites containing [Fe$$ _4 $$ C] squares, and the abundant formation of [Fe$$ _4 $$ C] squares on several reconstructed surfaces. These insights highlight the potential of utilizing LAM for atomistic simulation for iron-based FTS processes and other complex catalytic reactions. Fischer–Tropsch process ; Materials science ; Physics ; Computer science ; Chemistry ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Neural Networks and Applications
Authors: Abdel-baset H. Mekky
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Machine learning-powered modelling of creep strain in shape-memory Nitinol alloys at different combination points of stress and temperature An integrated machine learning system has been developed to make creep strain predictions on shape-memory Nitinol alloys subjected to different stress ranging from 0 to 500 MPa and temperature ranges from 25°C to 80°C. Our work used experimental data from 35 strain-controlled tests through which we evaluated four machine learning approaches, including linear regression and k-nearest neighbours along with decision Tree and Random Forest because traditional methods showed insufficient capabilities for analysing stress/temperature/creep deformation relationships. The evaluation of the models occurred through the mean absolute error(MAE) along with the root mean square error (RMSE) and the coefficient of determination ( R ²). The Random Forest Ensemble Method exceeded the performance of linear models and Decision Tree models by providing near-perfect predictive power with R ² = 1.000 while maintaining a zero MAE = 0.000 and RMSE = 3.93 × 10 9 . This matched better than linear models (e.g. linear regression: R ² = 0.708, MAE = 0.009) and decision tree ( R ² = 0.999, MAE = 0.000). Stress exists as the main cause of creep strain based on observation because there is a positive correlation ( r &gt; 0.95) while temperature maintains a non-linear effect that hastens deformation rates to 40% at 80°C when compared to 25°C. This study leads the field by merging machine learning algorithms with thermomechanical Nitinol data to build a comprehensive predictive system that increases material reliability and optimisation potential for biomedical and aerospace and energy sectors. Creep ; Shape-memory alloy ; Materials science ; Stress (linguistics) ; Stress–strain curve ; Strain (injury) ; Metallurgy ; Composite material ; Mechanical engineering ; Deformation (meteorology) ; Engineering Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Zhengyu Wei | Qigang Zhong | Jinbo Pan | F. Lim | Lifeng Chi | Shixuan Du
Source: Journal of the American Chemical Society (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Two-Stage Machine Learning Framework for Accurate Discrimination of Isomers and Very-Similar Molecules on Surfaces The accurate detection and discrimination of on-surface organic isomers and very similar molecules are crucial for monitoring chemical reaction processes and analyzing various reaction mechanisms and molecular properties. Despite its importance, nano- and surface science communities still lack an efficient, robust, precise, and automated detection approach for on-surface isomers and highly similar molecules. Here, we present ReSTOLO, a convolution neural network (CNN)-based framework for precise detection and identification of multiple types of sparsely distributed molecules on surfaces, particularly designed for scanning tunneling microscopy (STM) images containing numerous molecules with analogous features. To address challenges arising from molecular shape and size similarities, we implemented a two-stage framework comprising two CNN models: YOLO v5.m was used for molecular localization, and ResNet-101 for classification. The framework optimally harnesses the advantages of both models by applying a box normalization connection. We demonstrated the framework's effectiveness by applying it to analyze a surface reaction process involving six molecules with nearly identical STM signatures. The training process employed an STM image database of single molecules augmented with physical and experimental tools constructed using standardized image boxes. This two-stage approach achieved approximately ∼20% improvements in performance metrics, including precision, recall, and accuracy, compared to conventional frameworks. The framework exhibits robust capabilities in automatically and efficiently pinpointing and discriminating between molecular species with similar configurations in complex surface reactions. This automated molecular discriminator represents a significant advance in facilitating STM tip-manipulated chemical reactions on surfaces. Discriminator ; Molecule ; Process (computing) ; Convolutional neural network ; Scanning tunneling microscope ; Artificial neural network ; Pattern recognition (psychology) ; Surface (topology) ; Molecular recognition Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Matthew Witman | Bradley Moore Davis | Vitalie Stavila | Terry A. Johnson
Source: ACS Applied Energy Materials (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook. Compression (physics) ; Computer science ; Materials science ; Composite material Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals