jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sharmin, Mahmuda | Han, Taihao | Huang, Jie | Neithalath, Narayanan | Sant, Gaurav | Kumar, Aditya
Source: arXiv (Cornell University) (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials Machine learning (ML) has been increasingly applied in concrete research to optimize performance and mixture design. However, one major challenge in applying ML to cementitious materials is the limited size and diversity of available databases. A promising solution is the development of multi-modal databases that integrate both numerical and graphical data. Conventional ML frameworks in cement research are typically restricted to a single data modality. Graph neural network (GNN) represents a new generation of neural architectures capable of learning from data structured as graphs, capturing relationships through irregular or topology-dependent connections rather than fixed spatial coordinates. While GNN is inherently designed for graphical data, they can be adapted to extract correlations from numerical datasets and potentially embed physical laws directly into their architecture, enabling explainable and physics-informed predictions. This work is among the first few studies to implement GNNs to design concrete, with a particular emphasis on establishing a clear and reproducible pathway for converting tabular data into graph representations using the k-nearest neighbor (K-NN) approach. Model hyperparameters and feature selection are systematically optimized to enhance prediction performance. The GNN shows performance comparable to the benchmark random forest, which has been demonstrated by many studies to yield reliable predictions for cementitious materials. Overall, this study provides a foundational roadmap for transitioning from traditional ML to advanced AI architectures. The proposed framework establishes a strong foundation for future multi-modal and physics-informed GNN models capable of capturing complex material behaviors and accelerating the design and optimization of cementitious materials. Cementitious ; Hyperparameter ; Benchmark (surveying) ; Graph ; Artificial neural network ; Feature selection Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Fu JieMing | Shanshan Wang | Chenglong Xiao
Source:  (preprint, 2025-03-25)
Field: Materials Science — Materials Chemistry

Heterogeneous Graph Neural Networks for High-Level Synthesis Design Space Exploration High-Level Synthesis (HLS) design space exploration plays a pivotal role in electronic design automation by automatically converting high-level descriptions (e.g., C/C++/SystemC) into register-transfer level (RTL) code, significantly enhancing hardware design efficiency. However, the HLS design space remains vast and complex, involving multiple factors such as computational modules, data flow, control flow, and memory allocation, making traditional heuristic searches and experience-driven methods ineffective for efficient optimization. Conventional heuristic searches and graph neural network-based learning approaches exhibit limitations when handling heterogeneous structures. Current research predominantly focuses on homogeneous graph neural networks or statistical model-based methods, neglecting the inherent heterogeneity in HLS designs. This paper presents the first heterogeneous graph neural network (HGNN)-based prediction framework that models HLS designs as heterogeneous graphs to precisely capture complex interactions among different types of computational units, data dependencies, and optimization strategies. We design a multi-layer heterogeneous graph message-passing mechanism combined with a self-supervised learning strategy to enhance prediction accuracy and generalization capability in design space exploration. Experimental results demonstrate that our model can accurately estimate latency and resource utilization for unseen designs (i.e., novel CDFGs) within a few milliseconds. Computer science ; Graph ; Artificial neural network ; Space (punctuation) ; High-level synthesis ; Theoretical computer science ; Artificial intelligence ; Embedded system ; Operating system ; Field-programmable gate array Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Haoqing Wang | Dewang Zhao | Hanjie Liu
Source: Journal of Materials Engineering and Performance (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Development of a Novel Constitutive Model for Ti-6Al-4V Titanium Alloy Bars Based on Experimental Data and a Hybrid “Shallow” + “Deep” Neural Network Framework Constitutive equation ; Titanium alloy ; Ultimate tensile strength ; Alloy ; Titanium ; Artificial neural network ; Finite element method ; Tensile testing Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Tahir Mahmood | Muhammad Waseem Ashraf | Shahzadi Tayyaba | Muhammad Munir
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Based Simulation, Synthesis, and Characterization of Zinc Oxide/Graphene Oxide Nanocomposite for Energy Storage Applications Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In general, the energy parameters were predicted more accurately by hybrid models. The hydrothermal method was used to synthesize graphene oxide (GO) and zinc oxide (ZnO) nanocomposites. The increased surface area, conductivity, and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage. X-ray diffraction (XRD) confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO (12.8°) peaks. The scanning electron microscope (SEM) showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93 μm. Energy-dispersive X-ray spectroscopy (EDX) confirmed the elemental composition, and Fourier-transform infrared spectroscopy (FTIR) revealed the impact of GO on functional groups and electrochemical behavior. Photoluminescence (PL) wavelength of (439 nm) and band gap of (2.81 eV) show that the material is suitable for energy applications in nanocomposites. Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis, characterization, fuzzy logic, and machine learning in this work. Graphene ; Nanocomposite ; Crystallite ; Oxide ; Band gap ; Nanoparticle ; Zinc Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; ZnO doping and properties
Authors: Jobin K. Joy | Behrouz Haghgouyan | Anargyros A. Karakalas | Manish Vasoya | Dimitris C. Lagoudas
Source: Journal of Intelligent Material Systems and Structures (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

A neural network model for shape memory alloy actuation response with physical constraints for partial phase transformation In this work, we demonstrate a data-based Machine Learning (ML) framework that can capture the strain evolution during Shape Memory Alloy (SMA) actuation with a minimum of four state variable inputs.These inputs describe the thermomechanical stress-strain state of the SMA and changes in the stress state during thermal actuation. Furthermore, we identify the physics-based constraints to incorporate partial phase transformation and make predictions beyond the training regime. The ML framework uses a recurrent neural network (RNN) model to capture the nonlinear strain rate variation during phase transformation. Physics-based constraints are introduced in the RNN model to include the activation of thermoelasticity, while unloading away from transformation surfaces within the thermoelastic domain. The framework is trained using experimental thermal cycle responses of a NiTiHf SMA at different stress levels. The framework can then accurately predict actuation responses undergoing complete and partial phase transformations, and also extrapolate responses for new thermal cycling that involve complex thermomechanical loading paths. An uncertainty quantification analysis using ensemble training of the NN model is also presented to show the variability of the framework. Shape-memory alloy ; Transformation (genetics) ; Artificial neural network ; Actuator ; Phase (matter) ; Control theory (sociology) ; Materials science ; Computer science ; Structural engineering ; Control engineering ; Mechanical engineering ; Engineering ; Physics ; Artificial intelligence ; Control (management) Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Cheikh lakhdar Ben Messaoud | Zoulikha Hebboul | I.K. Lefkaier | Ahmed Draoui | Ahmed Lamine Ben Kamri | Souraya Goumri‐Said | Mohammed Benali Kanoun | Romualdo S. Silva | J. A. Alonso | Sophie Laurent
Source: Nanomaterials (article, 2025-04-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unlocking the Potential of Mg-Doped Rare Earth Manganites: Machine Learning-Guided Synthesis and Insights into Structural and Optical Properties By leveraging machine learning insights from prior perovskite studies and employing the sol-gel method, we successfully synthesized two novel perovskite nanoceramics-M<sub>0.5</sub> Ca<sub>0.25</sub>Mg<sub>0.25</sub>MnO<sub>3</sub> (M = La, Pr)-as multifunctional nanomaterials. X-ray diffraction (XRD) confirmed their orthorhombic Pnma crystal structure. The Williamson-Hall method estimated average particle sizes of 59.5 nm for PCMMO and 21.8 nm for LCMMO, while the Scherrer method provided corresponding values of 32.59 nm and 20.43 nm. SEM, UV-Vis, and FTIR analyses validated the chemical composition, homogeneity, and optical properties of the synthesized compounds, revealing band gaps of 3.25 eV (LCMMO) and 3.71 eV (PCMMO) with Urbach energies of 0.29 eV and 0.26 eV, respectively. These findings provide valuable insights into the structural and optical properties of LCMMO and PCMMO, highlighting their potential as multifunctional materials for advanced device applications. Orthorhombic crystal system ; Materials science ; Nanomaterials ; Doping ; Homogeneity (statistics) ; Band gap ; Crystal structure ; Perovskite (structure) ; Diffraction ; Rare earth ; Fourier transform infrared spectroscopy ; Analytical Chemistry (journal) ; Nanotechnology ; Crystallography ; Optoelectronics ; Optics ; Chemistry ; Metallurgy ; Computer science ; Physics Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Saiwei Ge | Zhiqiang Wang | Jingjing Pei
Source: Journal of Chemical Information and Modeling (article, 2025-10-25)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Guided High-Throughput Screening of Metal–Organic Frameworks for Atmospheric Ozone Capture: A Computational Materials Informatics Approach Computational materials informatics has emerged as a powerful approach for accelerating functional materials discovery through high-throughput screening and machine learning. This study presents the first systematic high-throughput computational screening and machine learning analysis of metal-organic frameworks (MOFs) for atmospheric ozone capture under environmentally relevant conditions (295 K, 250 ppb O<sub>3</sub>), establishing a dual-mechanism design framework with broad implications for reactive gas separations. A data set of 2116 representative MOF structures from the CoRE database was evaluated using grand canonical Monte Carlo (GCMC) simulations, with molecular dynamics characterizing transport properties and hierarchical machine learning enabling performance prediction across 5 orders of magnitude (2.58 × 10<sup>-</sup><sup>7</sup>-1.50 × 10<sup>-</sup><sup>2</sup> mol/kg). The key conceptual advance is a dual-mechanism framework governing trace-level capture: chemical affinity mediated by heavy metal centers determines ppb-level uptake efficiency, while geometric packing optimization controls maximum storage capacity. Results revealed that only 14.6% of MOFs exhibit detectable ozone adsorption, with top-performing materials featuring heavy metal centers (Mn, Mo, U, and Cd) achieving capacities exceeding 1.0 × 10<sup>-</sup><sup>2</sup> mol/kg. Machine learning analysis achieved exceptional predictive performance (<i>R</i><sup>2</sup> = 0.852) and identified pore efficiency as the universal predictor for MOF activity. The validated computational framework enables rational design of next-generation adsorbents for atmospheric remediation and demonstrates the effectiveness of hierarchical machine learning for complex environmental separations. This work establishes fundamental design principles applicable to reactive gas capture and accelerates materials discovery through interpretable computational screening. Materials informatics ; Computational model ; Informatics ; Key (lock) ; Monte Carlo method ; Rational design ; Work (physics) ; Uncertainty quantification Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Sharmin, Mahmuda | Han, Taihao | Huang, Jie | Neithalath, Narayanan | Sant, Gaurav | Kumar, Aditya
Source: ArXiv.org (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials Machine learning (ML) has been increasingly applied in concrete research to optimize performance and mixture design. However, one major challenge in applying ML to cementitious materials is the limited size and diversity of available databases. A promising solution is the development of multi-modal databases that integrate both numerical and graphical data. Conventional ML frameworks in cement research are typically restricted to a single data modality. Graph neural network (GNN) represents a new generation of neural architectures capable of learning from data structured as graphs, capturing relationships through irregular or topology-dependent connections rather than fixed spatial coordinates. While GNN is inherently designed for graphical data, they can be adapted to extract correlations from numerical datasets and potentially embed physical laws directly into their architecture, enabling explainable and physics-informed predictions. This work is among the first few studies to implement GNNs to design concrete, with a particular emphasis on establishing a clear and reproducible pathway for converting tabular data into graph representations using the k-nearest neighbor (K-NN) approach. Model hyperparameters and feature selection are systematically optimized to enhance prediction performance. The GNN shows performance comparable to the benchmark random forest, which has been demonstrated by many studies to yield reliable predictions for cementitious materials. Overall, this study provides a foundational roadmap for transitioning from traditional ML to advanced AI architectures. The proposed framework establishes a strong foundation for future multi-modal and physics-informed GNN models capable of capturing complex material behaviors and accelerating the design and optimization of cementitious materials. Cementitious ; Hyperparameter ; Benchmark (surveying) ; Graph ; Artificial neural network ; Feature selection Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Kousik Bhunia | Vandana Sinha | Sumit Saha
Source:  (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Doping Variability Modeling in 2D Material Channel FETs Using Artificial Neural Network Two-dimensional (2D) material-based Field-effect transistors (FETs) are the future for digital technologies due to their excellent scalability and superior material properties compared to conventional silicon material. However, accurately and efficiently modeling these devices remains challenging due to quantum mechanical effects, which complicate transport through 2D materials. In this work, a compact modeling framework for the n-type and p-type 2D material FETs is proposed and implemented using an artificial neural network (ANN). A shallow ANN has been implemented to model the FETs for efficient SPICE implementation and circuit simulation. The ANN has been optimized for the number of hidden layers, the number of neurons, the activation function, the learning rate, and the number of epochs. The neural network model is capable of capturing device performance variations due to doping variations in the channel region, as well as in the source and drain regions. Artificial neural network ; Scalability ; Spice ; Transistor ; Channel (broadcasting) ; Silicon ; Doping ; Semiconductor device modeling 2D Materials and Applications ; Advancements in Semiconductor Devices and Circuit Design ; Nanowire Synthesis and Applications
Authors: Tianhong Xia (19533098) | Xiaofang Hu (206622)
Source: Figshare (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Machine-Learning-AssistedDensity Functional TheoryCalculations: A New Approach to Screening Thermal Runaway Gas Sensorsfor Lithium-Ion Batteries Lithium-ion batteries face safety risks, such as spontaneous combustion and explosion, during long-term use and will rupture and release combustible gases, such as CH<sub>4</sub>, CO<sub>2</sub>, H<sub>2</sub>, and CO, during thermal runaway. Therefore, the detection of these specific gases becomes the key to prevent thermal runaway accidents of lithium batteries. In response to this demand, this study proposed a method of selecting gas-sensitive materials with the aid of machine learning (ML) to efficiently obtain ideal materials from a huge candidate space. The doped substrates formed by 28 transition metals with MoS<sub>2</sub> were selected as candidates for the study, and the initial stabilized configurations of the four gases were determined. By construction of 8 ML models, the optimal model is selected for adsorption energy prediction. In this process, the importance of each feature in model prediction is then deeply analyzed. The sensing performance of the selected materials is further validated to screen the best performing materials. This study combines density functional theory calculations with ML, which significantly saves the time and cost of screening thermal runaway gas materials for lithium-ion batteries and provides a new research idea for rapid screening of nanomaterials. Thermal runaway ; Density functional theory ; Thermal ; Energy density ; Key (lock) ; Combustion ; Ideal (ethics) ; Feature (linguistics) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Shilin Wang | Chao Zhou | Tianhao Wang | Sen Yang
Source: Computational Materials Science (article, 2025-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the composition - property relationship of magnetostrictive materials: A deep learning driven saturation magnetostriction coefficient predictor Magnetostriction ; Saturation (graph theory) ; Mean squared error ; Mean squared prediction error ; Deep learning ; Mean absolute error ; Spintronics ; Composition (language) Multiferroics and related materials ; Magnetic Properties and Applications ; Machine Learning in Materials Science
Authors: 兴坪 魏 | 奇 赖 | 禹 谢
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetization intensity prediction and performance analysis of Fe-based alloys based on machine learning Magnetization ; Coercivity ; Robustness (evolution) ; Intensity (physics) ; Mean squared error ; Magnet ; Test set Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Vasić, Milica | Awoyera, Paul | Radojević, Zagorka
Source: RIMS - Repository of the Institute for Material Testing (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for predicting complex properties of ceramic materials: A Big Data approach As ceramic production evolves toward digital transformation, the role of data-driven modeling becomes increasingly vital. This work explores how advanced machine learning (ML) techniques can extract actionable insights from large, multi-dimensional datasets describing the chemical, mineralogical, and granulometric characteristics of ceramic raw materials. Two key production phases—shaping/drying and firing—are modeled to predict critical quality parameters such as plasticity, drying loss, water absorption, and bending strength. Ensemble models, particularly Gradient Boosting and CatBoost, delivered superior performance (R² > 0.98), while SHAP analysis enabled interpretability of feature contributions. By revealing complex, nonlinear relationships and material thresholds, these models support real-time process tuning, smarter material selection, and enhanced sustainability. The study showcases how big data and explainable AI (XAI) techniques can drive innovation in materials science and industrial process control. Interpretability ; Big data ; Boosting (machine learning) ; Gradient boosting ; Process (computing) ; Raw data ; Ceramic ; Production (economics) ; Quality (philosophy) Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; High-Temperature Coating Behaviors
Authors: Lianduan Zeng | Xiao Zhou | Xinxi Lu | Li Huang | Lan Yang | Lihao Wang | Gang Liu | Zhongyang Wang | Tongxiang Fan
Source: Materials Genome Engineering Advances (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Predicting Infrared Optical Properties of Materials Using Machine Learning Interatomic Potentials ABSTRACT Infrared optical materials are critical for numerous applications, yet accurately characterizing their intrinsic optical properties remains challenging. Traditional theoretical approaches—ranging from empirical molecular dynamics to first‐principles methods like density functional perturbation theory (DFPT)—face trade‐offs between accuracy and computational cost, particularly for complex or low‐symmetry material systems. Here, we tackle these challenges by introducing a fast and accurate infrared spectroscopy computational framework using machine learning interatomic potentials. By leveraging machine‐learned interatomic forces, this method bypasses costly higher order DFPT calculations, enabling rapid extraction of phonon vibrational parameters. These parameters are then integrated into infrared‐active vibration models to compute dielectric functions and infrared optical properties. Validated across diverse materials, our proposed framework demonstrates broad applicability while achieving a drastic reduction in computational cost compared to conventional methods. This framework bridges the gap between experimental characterization and theoretical predictions, offering a scalable tool for high‐throughput screening and design of infrared optical materials. Infrared ; Phonon ; Scalability ; Density functional theory ; Perturbation theory (quantum mechanics) ; Dielectric ; Interatomic potential ; Reduction (mathematics) Machine Learning in Materials Science ; 2D Materials and Applications ; Thermal properties of materials