jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. M. Uz | S. Sajjadifar | Guney Guven Yapici
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Comparative analysis on the phenomenological and artificial neural network modeling for flow curves of a beta titanium alloy This study investigates the elevated temperature mechanical behavior of Ti-5V-5Mo-5Cr-4Al alloy through uniaxial tensile experiments conducted at temperatures ranging from room temperature to 550 degrees C and strain rates of 0.001, 0.01, and 0.1 s(-1). The results reveal that the dominant softening mechanism is dynamic recovery, whereas dynamic precipitation took place at the lowest rate of deformation and at temperatures ranging from 400 degrees C to 500 degrees C. To predict the mechanical behavior of this recent beta titanium alloy, artificial neural network (ANN) approach and modified Hensel-Spittel (m-HS) model were employed. In the prediction of flow curves using the m-HS model, a correlation coefficient (R) of 0.901 and an average absolute relative error (AARE) of 8.891 % were obtained. In contrast, the ANN approach yielded significantly better results, with an R value of 0.997 and an AARE of 2.3 %. The findings from this study provide routes for determining the hot workability of next-generation metastable beta titanium alloys. Titanium alloy ; Softening ; Artificial neural network ; Deformation (meteorology) ; Metastability ; Titanium ; Correlation coefficient ; Strain rate Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Hiroki Moriwake | Kazuki Shitara | Rie Yokoi | Takeharu Kato | Isao Tanaka | A. Yamamoto | K. Murase | Yosuke Hamasaiki | Yoshitaka Ehara | Mitsuru Itoh
Source: Advanced Intelligent Discovery (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning We report a materials informatics framework that combines first‐principles phonon calculations and machine learning to discover perovskite oxides with giant dielectric constants. Initially, phonon dispersions were calculated for 61 experimentally reported perovskite compounds from the ICSD database to validate the effectiveness of hierarchical clustering in classifying phonon dispersion behaviors. The analysis was then extended to a broader set of 525 experimentally reported and hypothetical perovskite structures. Six promising candidates were identified, among which RbNbO 3 was synthesized under high‐pressure conditions. Its dielectric properties were validated: a single‐crystal RbNbO 3 exhibited a room‐temperature dielectric constant of 800–1000, consistent with theoretical predictions. These results demonstrate the power of soft‐mode‐based informatics, combined with comprehensive phonon calculations, in discovering high‐performance dielectric materials. Phonon ; Dielectric ; Perovskite (structure) ; Cluster analysis ; Dispersion (optics) ; Permittivity Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography
Authors: Murat Mert Uz | Seyedvahid Sajjadifar | Guney Guven Yapici
Source: Materials Performance and Characterization (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Comparative Analysis on the Phenomenological and Artificial Neural Network Modeling for Flow Curves of a Beta Titanium Alloy Abstract This study investigates the elevated temperature mechanical behavior of Ti-5V-5Mo-5Cr-4Al alloy through uniaxial tensile experiments conducted at temperatures ranging from room temperature to 550°C and strain rates of 0.001, 0.01, and 0.1 s−1. The results reveal that the dominant softening mechanism is dynamic recovery, whereas dynamic precipitation took place at the lowest rate of deformation and at temperatures ranging from 400°C to 500°C. To predict the mechanical behavior of this recent beta titanium alloy, artificial neural network (ANN) approach and modified Hensel-Spittel (m-HS) model were employed. In the prediction of flow curves using the m-HS model, a correlation coefficient (R) of 0.901 and an average absolute relative error (AARE) of 8.891 % were obtained. In contrast, the ANN approach yielded significantly better results, with an R value of 0.997 and an AARE of 2.3 %. The findings from this study provide routes for determining the hot workability of next-generation metastable beta titanium alloys. Titanium alloy ; Softening ; Metastability ; Artificial neural network ; Deformation (meteorology) ; Strain rate ; Alloy ; Ultimate tensile strength Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Zsuzsanna Koczor-Benda | Joe Gilkes | Francesco Bartucca | Abdulla Al-Fekaiki | Reinhard J. Maurer
Source: Journal of Chemical Information and Modeling (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structures are particularly suitable for quantum chemistry workflows, enabling direct property prediction. The performance of generative models is typically assessed based on their ability to produce novel, valid, and unique molecules. However, equally important is their ability to learn the prevalence of functional groups and certain chemical moieties in the underlying training data, that is, to faithfully reproduce the chemical space spanned by the training data. Here, we investigate the ability of the autoregressive generative machine learning model G-SchNet to reproduce the chemical space and property distributions of training data sets composed of large, functional organic molecules. We assess the elemental composition, size- and bond-length distributions, as well as the functional group and chemical space distribution of training and generated molecules. By principal component analysis of the chemical space, we find that the model leads to a biased generation that is largely unaffected by the choice of hyperparameters or the training data set distribution, producing molecules that are, on average, less saturated and contain more heteroatoms. Purely aliphatic molecules are mostly absent in the generation. We further investigate generation with functional group constraints and based on composite data sets, which can help to partially remedy the model generation bias. Decision tree models can recognize the generation bias in the models and discriminate between training and generated data, revealing key chemical differences between the two sets. The chemical differences we find affect the distributions of electronic properties such as the HOMO-LUMO gap, which is a common target for functional molecule design. Autoregressive model ; Generative grammar ; Organic molecules ; Artificial intelligence ; Machine learning ; Computer science ; Generative model ; Pattern recognition (psychology) ; Chemistry ; Biological system ; Molecule ; Econometrics ; Mathematics ; Organic chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Aynul Sakinah Ahmad Fauzi | Mohammad Khajavian | Teh Ubaidah Noh | Suzylawati Ismail | Mohamad Zailani Abu Bakar | Noorashrina A. Hamid
Source: International Journal of Hydrogen Energy (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Catalytic methane decomposition via nickel–mesoporous silica nanoparticle KIT6 catalyst for hydrogen production: A machine learning perspective Catalysis ; Hydrogen ; Hydrogen production ; Methane ; Nanoparticle ; Mesoporous silica ; Yield (engineering) ; Decomposition Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Machine Learning in Materials Science
Authors: Dodla Sivanageswara Rao | Mudra Narasimharao | Bashir N. Jamadar | Anil A. Powar | Balasaheb. B. Vhankhande | Anantkumar J. Umbarkar
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Machine Learning Applications in Composite Material Synthesis and Property Prediction Property (philosophy) ; Composite number ; Production (economics) ; Artificial neural network ; Feature (linguistics) Machine Learning in Materials Science ; Material Selection and Properties ; Epoxy Resin Curing Processes
Authors: Seohye Ha | Yunki Han | Tae-Hwan Kim | Jiwan Kim | Junyoung Park | G.-S. Park | Lee-Sup Kim
Source:  (article, 2025-10-26)
Field: Materials Science — Materials Chemistry

SAFF: Scalable Acceleration of GNN-based Machine Learning Force Fields using Tensor-Aware Hardware for Molecular Simulation Machine Learning Force Fields (MLFFs) have emerged as a key technique in molecular dynamics (MD) simulations, enabling accurate prediction of molecular energies and forces with a level of precision comparable to that of Density Functional Theory (DFT), while significantly reducing computational cost. However, applying MLFFs to real-world scenarios, such as semiconductor process simulations involving massive atomic scales and long simulation times, exposes severe performance limitations, resulting in current MLFF models being inadequate for practical use and emphasizing the necessity of significant computational acceleration. Graph Neural Networks (GNNs) are frequently utilized to construct MLFFs, with tensor product operations constituting a substantial portion of the computational workload. These operations involve high-dimensional tensors whose sizes are determined by the number of channels and the order of rotation. This results in frequent memory accesses and low data reuse. This leads to memory bottlenecks that limit the efficient use of GPU computational resources. In this work, we propose a tensor-based function fusion technique to improve data continuity between functions, thereby increasing on-chip data reuse and reducing off-chip memory access. Furthermore, we identified inefficient utilization of hardware resources and addressed this by restructuring the computation flow and removing redundant operations to enhance computational performance. To optimize performance, we have also designed a dedicated tensor product hardware architecture that is optimized for GNN-based MLFFs. The experimental results demonstrate that the proposed system achieves an average speedup of 6.26× and a 424× improvement in energy efficiency compared to GPU-based execution, while maintaining the original model accuracy. Scalability ; Speedup ; Computation ; Acceleration ; Artificial neural network ; Process (computing) ; Tensor (intrinsic definition) ; Hardware acceleration ; Reuse ; Computational model Machine Learning in Materials Science ; Graph Theory and Algorithms ; Protein Structure and Dynamics
Authors: Ruihui Wang | Linhu Pan | Mingren Fan | Yi Wang | Xiurong Yang | Xiaoyu Feng | Xiujuan Qi | Siwei Song | Qinghua Zhang
Source: Chemical Engineering Journal (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Intelligent high-energy molecules design for solid propellants via interpretable machine learning and latent space optimization Propellant ; Interpretability ; Bayesian optimization ; Chemical space ; Cheminformatics ; Artificial neural network ; Molecular descriptor ; Bayesian network ; Autoencoder ; Workflow Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Energetic Materials and Combustion
Authors: Mohamed amine Hechmi
Source: Research Square (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

Prediction of the Ground State Energy of the Hydrogen Molecule using Quantum Neural Networks Quantum ; Scalability ; Artificial neural network ; Ground state ; Parameterized complexity ; Scale (ratio) ; Energy (signal processing) ; Work (physics) ; Range (aeronautics) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Akira Hirako | Shota Kariya | Junko Umeda | Kenta Yamanaka | Xiao-Chun Li | Katsuyoshi Kondoh
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

Identification of Strengthening Factors in Hot-rolled Dual Phase PM Ti-Fe Alloys by a Machine Learning Approach Due to the complex and nonlinear correlations between microstructures and mechanical properties in dual phase alloys, it is difficult to estimate their strength by conventional methods. This study attempts to model the relationship between microstructure and mechanical properties in sintered and hot-rolled α + β titanium-iron (Ti-Fe) alloys using machine learning. In the preparation of the models, 4-9 microstructural factors were investigated to identify the most important predictors of mechanical properties. A Random Forest (RF) model was found to have best predictive power, producing a good match with experimental data in samples which were outside of the training dataset. Moreover, the average α grain diameters, β phase widths (by intercept method), and β phase area fractions were found to be the strongest predictors of mechanical behavior. Materials science ; Identification (biology) ; Metallurgy ; Dual (grammatical number) ; Phase (matter) Titanium Alloys Microstructure and Properties ; Advanced Welding Techniques Analysis ; Metallurgy and Material Forming
Authors: Wasut Pornpatcharapong
Source:  (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems Free energy reconstruction methods such as Gaussian Process Regression (GPR) require Jacobians of the collective variables (CVs), a bottleneck that restricts the use of complex or machine-learned CVs. We introduce a neural network surrogate framework that learns CVs directly from Cartesian coordinates and uses automatic differentiation to provide Jacobians, bypassing analytical forms. On an MgCl<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ion-pairing system, our method achieved high accuracy for both a simple distance CV and a complex coordination-number CV. Moreover, Jacobian errors also followed a near-Gaussian distribution, making them suitable for GPR pipelines. This framework enables gradient-based free energy methods to incorporate complex and machine-learned CVs, broadening the scope of biochemistry and materials simulations. Bottleneck ; Artificial neural network ; Computation ; Simple (philosophy) ; Energy (signal processing) ; Jacobian matrix and determinant ; Process (computing) ; Complex system Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Xingyu Xiao | Zhilei Wang | Peiwen Yun | Wei Sun | Mingying Chen | Xinhua Liu | Weidong Li | Jianxin Xie
Source: Journal of Material Science and Technology (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

An interatomic-energy-based new materials design strategy combining DFT calculations and active machine learning Density functional theory ; Yield (engineering) ; Work (physics) ; Alloy ; Interatomic potential ; Consistency (knowledge bases) ; Energy (signal processing) ; Binding energy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chenyang Zhao | Jiawei Lin | Yanbo Liu | Jianmin Zhou | Liang Li | Shi Tang | Jing Li | Songgu Wu | Pancě Naumov | Junbo Gong
Source: Chemistry of Materials (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

Generative Deep Learning-Aided Design of Flexible Molecular Crystals International audience Generative grammar ; Crystal (programming language) ; Generative model ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Matti Waris | Mohammad Abdus Salam | Arifa Akram | Fajar Shafeeq | M.Y. Malik | Abdul Rehman | Farrukh Bashir | Muhammad Aslam Khan | Tahir Hasan
Source: Physical Education Health and Social Sciences (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Recent Advances in Organocatalysis for Sustainable Organic Transformations Organocatalysis has emerged as a pivotal tool in green chemistry, offering metal-free, sustainable alternatives for organic transformations. This review explores recent advancements in organocatalyst design, mechanisms, and applications, emphasizing their role in reducing environmental impact through solvent-free conditions, recyclable catalysts, and integration with modern techniques like photoredox, electrochemical, and biocatalytic systems. Key developments include enantioselective syntheses, hybrid catalysis with transition metals, and process intensification via flow chemistry. The paper highlights challenges such as catalyst stability and reactivity, while proposing future directions involving earth-abundant materials, machine learning for optimization, and innovative solvents. Overall, organocatalysis aligns with green chemistry principles, promoting energy efficiency, waste minimization, and industrial scalability in pharmaceuticals, polymers, and biomass valorization. Organocatalysis ; Sustainability ; Scope (computer science) ; Process (computing) ; Enantioselective synthesis ; Green chemistry ; Biomass (ecology) Radical Photochemical Reactions ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rui Qu | Zhiqiang Zhang | Mingwen Ren | Hongjie Jia | Tianbao Lv
Source: Metals (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Optimization of Hot Stamping Parameters for Aluminum Alloy Crash Beams Using Neural Networks and Genetic Algorithms The hot stamping process of aluminum alloys involves multiple parameters, including blank holder force, stamping speed, die temperature, and friction coefficient. Traditional methods often fail to capture the nonlinear interactions among these parameters. This study proposes an optimization framework that integrates BP neural networks with genetic algorithms (GA), while six bio-inspired algorithms—Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Crested Porcupine Optimizer (CPO), Grey lag Goose Optimization (GOOSE), Dung Beetle Optimizer (DBO), and Parrot Optimizer (PO)—were employed to optimize the network hyperparameters. Comparative results show that all optimized models outperformed the baseline BP model (R2 = 0.702, RMSE = 0.106, MAPE = 20.8%). The PO-BP achieved the best performance, raising R2 by 27.3% and reducing MAPE by 27.1%. Furthermore, combining GA with the PO-BP model yielded optimized process parameters, reducing the maximum thinning rate to 17.0% with only a 1.16% error compared with experiments. Overall, the proposed framework significantly improves prediction accuracy and forming quality, offering an efficient solution for rapid process optimization in intelligent manufacturing of aluminum alloy automotive parts. Artificial neural network ; Mean absolute percentage error ; Genetic algorithm ; Process (computing) ; Stamping ; Mean squared error ; Gradient descent ; Automotive industry ; Optimization algorithm High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques
Authors: Reenu Elizabeth John | Suresh Babu M | Andrew P. Thomas | Prajina O. K | Anusree P G | Steffy Sara Varghese
Source:  (article, 2025-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead The field of EMI shielding materials is undergoing rapid transformation, driven by escalating demands for high-performance shielding in modern electronics. However, the design and development of advanced shielding materials depend on empirical, trial and error methodologies. This dependance on iterative experiments not only brings up the cost but also results in longer developmental cycles presenting a significant bottleneck for innovations. The integration of machine learning (ML) in the field of EMI shielding materials drastically reduces the cost and delay in the conventional experimental method, and effectively establishes a rapid prediction model for shielding effectiveness and proper analysis and optimization of new and advanced shielding materials. This review critically examines the progress in applying ML to the field of EMI shielding materials. Furthermore, the article discusses the key challenges and emerging trends in ML to EMI shielding materials. EMI ; Electromagnetic shielding ; Electromagnetic interference ; Bottleneck ; Key (lock) Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Measurements ; Electromagnetic Compatibility and Noise Suppression
Authors: Jian‐Ping Zou | Zhanghao Zhouyin | Qiangqiang Gu | Shishir Kumar Pandey
Source: ArXiv.org (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While density functional theory (DFT) combined with the non-equilibrium Green$'$s function (NEGF) formalism provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonians derived learned directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. To validate the method, we apply it to three prototypical 2D materials: graphene, hexagonal boron nitride (h-BN), and MoS$_2$. The resulting band structures and transmission spectra show excellent agreement with conventional DFT-NEGF results, while achieving orders-of-magnitude improvement in efficiency. These results highlight the capability of DeePTB-NEGF to enable accurate and efficient quantum transport simulations, thereby opening avenues for large-scale exploration and device design in 2D materials. Quantum ; Formalism (music) ; Density functional theory ; Transport theory ; Boron nitride ; Quantum simulator ; Quantum computer 2D Materials and Applications ; Graphene research and applications ; Thermal properties of materials