jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leander Claes | Kevin Koch | O. Friesen | Lars Meihost
Source: Springer Link (Chiba Institute of Technology) (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Machine learning-supported inverse measurement procedure for broadband, temperature dependent piezoelectric material parameters This paper proposes an approach to identify a full set of piezoelectric material parameters by solving an inverse problem supported by data-driven methods, in particular neural networks. The accurate, quantitative description of piezoelectric material behaviour is challenging due to the large number of parameters, the complexity of the interacting physical quantities, and inability to infer material parameter values directly from certain measurements. Studies have shown that a full set of material parameters can be identified by solving an inverse problem, matching the electrical impedance of samples with the output of a simulation model. The solution method of the said inverse problem is hard to regularise due to vastly different sensitivities of the impedance with respect to certain material parameters and a large parameter space. Using synthetically generated training data, a method for initial value estimation using a neural network to invert the simulation model is proposed. The initial values are refined in an intermediate optimisation step using a second neural network that mimics the simulation model. The subsequent gradient-based optimisation process converges significantly faster than previous approaches and yields a better fit to the measurement data. Changes in properties that occur when the sample is exposed to different temperatures are examined to assess the ability of the method to resolve small differences in material behaviour. Inverse ; Inverse problem ; Artificial neural network ; Matching (statistics) ; Set (abstract data type) ; Piezoelectricity ; Process (computing) ; Material properties Ferroelectric and Piezoelectric Materials ; Ultrasound Imaging and Elastography ; Ultrasonics and Acoustic Wave Propagation
Authors: Habibollah Safari | Mona Bavarian
Source: ArXiv.org (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Chemically-Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization Predicting monomer reactivity ratios is crucial for controlling monomer sequence distribution in copolymers and their properties. Traditional experimental methods of determining reactivity ratios are time-consuming and resource-intensive, while existing computational methods often struggle with accuracy or scalability. Here, we present a method that combines unsupervised learning with artificial neural networks to predict reactivity ratios in radical copolymerization. By applying spectral clustering to physicochemical features of monomers, we identified three distinct monomer groups with characteristic reactivity patterns. This computationally efficient clustering approach revealed specific monomer group interactions leading to different sequence arrangements, including alternating, random, block, and gradient copolymers, providing chemical insights for initial exploration. Building upon these insights, we trained artificial neural networks to achieve quantitative reactivity ratio predictions. We explored two integration strategies including direct feature concatenation, and cluster-specific training, which demonstrated performance enhancements for targeted chemical domains compared to general training with equivalent sample sizes. However, models utilizing complete datasets outperformed specialized models trained on focused subsets, revealing a fundamental trade-off between chemical specificity and data availability. This work demonstrates that unsupervised learning offers rapid chemical insight for exploratory analysis, while supervised learning provides the accuracy necessary for final design predictions, with optimal strategies depending on data availability and application requirements. Cluster analysis ; Artificial neural network ; Reactivity (psychology) ; Unsupervised learning ; Supervised learning ; Sequence (biology) ; Pattern recognition (psychology) ; Monomer Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Sadaf Noreen | Mamduh J. Aljaafreh
Source: Journal of Fluorescence (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Accelerated Chemical Space Generation of High Molar Extinction Organic Sensitizers via Machine Learning Chemical space ; Boosting (machine learning) ; Curse of dimensionality ; Chromophore ; Molecular descriptor ; Gradient boosting ; van der Waals force ; Molar absorptivity ; Logarithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Ce Chen | Liujun Wu | Chenyang Xin | Wenbin Liu | Xin Yi | Huiling Duan
Source: Journal of the Mechanics and Physics of Solids (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

A neural-network-enhanced micromechanical framework with evolving reference medium for nonlinear heterogeneous materials Nonlinear system ; Isotropy ; Micromechanics ; Tangent ; Tangent stiffness matrix ; Linearization ; Tangent modulus ; Stiffness ; Work (physics) Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Elasticity and Material Modeling
Authors: Chengcheng Liu | Hang Su
Source: JOM (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Study on Hardness Prediction and Design of Ti-Zr-Nb-Ta Refractory Alloy Based on Interpretable Machine Learning and Generative Model Materials science ; Alloy ; Generative grammar ; Refractory (planetary science) ; Metallurgy ; Niobium ; Titanium alloy ; Artificial intelligence ; Computer science Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; High Entropy Alloys Studies
Authors: Yan Liu | Jiantao Wang | Hongli Deng | Yan Sun | Xing‐Qiu Chen | Peitao Liu
Source: ArXiv.org (preprint, 2025-10-19)
Field: Materials Science — Materials Chemistry

Efficient small-cell sampling for machine-learning potentials of multi-principal element alloys Multi-principal element alloys (MPEAs) exhibit exceptional properties but face significant challenges in developing accurate machine-learning potentials (MLPs) due to their vast compositional and configurational complexity. Here, we introduce an efficient small-cell sampling (SCS) method, which allows for generating diverse and representative training datasets for MPEAs using only small-cell structures with just one and two elements, thereby bypassing the computational overhead of iterative active learning cycles and large-cell density functional theory calculations. The efficacy of the method is carefully validated through principal component analysis, extrapolation grades evaluation, and root-mean-square errors and physical properties assessment on the TiZrHfCuNi system. Further demonstrations on TiZrVMo, CoCrFeMnNi, and AlTiZrNbHfTa systems accurately reproduce complex phenomena including phase transitions, chemical orderings, and thermodynamic properties. This work establishes an efficient one-shot protocol for constructing high-quality training datasets across multiple elements, laying a solid foundation for developing universal MLPs for MPEAs. Extrapolation ; Sampling (signal processing) ; Principal component analysis ; Work (physics) ; Overhead (engineering) ; Phase (matter) ; Face (sociological concept) ; Protocol (science) ; Component (thermodynamics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Shi-min Ai | dr fang | Yaowei Guo | Jie Ye | Xiaoping Lin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Machine Learning-Driven Prediction of Microstructure-Mechanical Property Relationships in Mg-Al Alloys Microstructure ; Materials science ; Property (philosophy) ; Artificial intelligence ; Metallurgy ; Computer science ; Philosophy Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Pengqiang Hu | Chao Zhou | Ruisheng Zhang | Shi‐Jin Ding | Yuanjun Guo | Bo Wang | Dezhen Xue | Yizhe Ma | Zhiyong Dai | Yin Zhang | Fanghua Tian | Sen Yang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing Laves-Phase Rfe2-Type Alloy with Excellent Magnetostrictive Performance by Physics-Informed Interpretable Machine Learning Laves phase ; Magnetostriction ; Alloy ; Type (biology) ; Materials science ; Phase (matter) ; Artificial intelligence ; Machine learning ; Computer science ; Metallurgy ; Physics ; Geology ; Intermetallic ; Magnetic field ; Quantum mechanics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Mingji Liu | Wenzhao Li
Source: Journal of Materials Engineering and Performance (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

An Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of Thermoelectric Materials Workflow ; Robustness (evolution) ; Feature selection ; Process (computing) ; Thermoelectric materials ; Feature (linguistics) ; Set (abstract data type) ; Characterization (materials science) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials
Authors: W.P. Cao | Yuyang Zhang | Shiping Guo | Qinghan Xu | Kexue Sun
Source:  (article, 2025-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Assisted Inverse Design of Parameters for Liquid Crystal Reconfigurable Coded Metasurface Units Traditional liquid crystal (LC) metasurface design methods are not suitable for real-time applications due to their high time cost and huge computational effort. To address this problem, we propose a deep learning assisted inverse design method for LC reconfigurable coding metasurfaces units. Our method employs a convolutional autoencoder to compress highdimensional binary masks into a latent vector space. Then, a lightweight feed-forward neural network predicts structural parameters and latent vectors directly from the target multifrequency response, thus enabling global design and significantly reducing the search space. Experimental results show that the proposed method achieves a 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> times faster design speed on the test set compared to the NSGA-II algorithm while achieving comparable performance. Therefore, the proposed framework is capable of fast and accurate design of programmable LC metasurfaces. Its excellent real-time and practical performance make it well suited for large-scale real-time reconfigurable systems that require fast iterations. Autoencoder ; Deep learning ; Binary number ; Convolutional neural network ; Set (abstract data type) ; Coding (social sciences) ; Inverse ; Field-programmable gate array ; Artificial neural network Metamaterials and Metasurfaces Applications ; Advanced Materials and Mechanics ; Liquid Crystal Research Advancements
Authors: Debabrata Datta | S. Seema | S. Suman Rajest | Biswaranjan Senapati | S. Silvia Priscila | Deepak K. Sinha
Source: International Journal of Critical Infrastructures (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ensemble machine learning regression technique to select the type of concrete as radiation shielding material The selection of exact material for shielding analysis is challenging in radiation protection. The primary objective of shielding analysis is to reduce radiation exposure to the occupational worker at their workplace. Generally, high-density concrete is selected as the shielding material to prevent accidental exposure to gamma and neutron radiation. Composite material or multilayer shielding materials are generally used to optimise the cost of concrete with maximum benefit to the society of occupational radiation workers. A surrogate model for concrete's overall strength using cement, fly ash, and coarse and fine aggregates is created using machine learning and ensemble learning. Ensemble learning in machine learning solves underfitting and overfitting problems when fitting a regression model for shielding analysis. As density increases, concrete overall strength decreases. Several samples of various types of concrete (different compositions) are collected as input data. Finally, a multi-attribute decision-making method is applied to select the appropriate type of concrete. The research presents the ensemble learning based regression technique coupled with multi attribute decision making method to recommend the exact variety of concrete for shielding gamma and neutron radiation. Radiation shielding ; Electromagnetic shielding ; Regression analysis ; Engineering ; Computer science ; Forensic engineering ; Machine learning ; Electrical engineering Graphite, nuclear technology, radiation studies ; Radiation Shielding Materials Analysis ; Advanced Neural Network Applications
Authors: Habibollah Safari | Mona Bavarian
Source: arXiv (Cornell University) (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Chemically-Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization Predicting monomer reactivity ratios is crucial for controlling monomer sequence distribution in copolymers and their properties. Traditional experimental methods of determining reactivity ratios are time-consuming and resource-intensive, while existing computational methods often struggle with accuracy or scalability. Here, we present a method that combines unsupervised learning with artificial neural networks to predict reactivity ratios in radical copolymerization. By applying spectral clustering to physicochemical features of monomers, we identified three distinct monomer groups with characteristic reactivity patterns. This computationally efficient clustering approach revealed specific monomer group interactions leading to different sequence arrangements, including alternating, random, block, and gradient copolymers, providing chemical insights for initial exploration. Building upon these insights, we trained artificial neural networks to achieve quantitative reactivity ratio predictions. We explored two integration strategies including direct feature concatenation, and cluster-specific training, which demonstrated performance enhancements for targeted chemical domains compared to general training with equivalent sample sizes. However, models utilizing complete datasets outperformed specialized models trained on focused subsets, revealing a fundamental trade-off between chemical specificity and data availability. This work demonstrates that unsupervised learning offers rapid chemical insight for exploratory analysis, while supervised learning provides the accuracy necessary for final design predictions, with optimal strategies depending on data availability and application requirements. Cluster analysis ; Artificial neural network ; Reactivity (psychology) ; Unsupervised learning ; Supervised learning ; Sequence (biology) ; Pattern recognition (psychology) ; Monomer Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Guangbao Mi | Yuanzhi Sun | Ruochen Sun | Peijie Li | Nan Sui | Liangju He | Fengyu Dong
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600°C high temperature burn resistant titanium alloys Titanium alloy ; Titanium ; Lath ; Alloy ; Nickel titanium ; Burn-in ; Laves phase Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Additive Manufacturing Materials and Processes
Authors: Anurag Singh | Devraj Singh | R. Khenata | Ajit Kumar Maddheshiya | A. Boumaza | Saleem Ayaz Khan | S. Bin‐Omran | Souraya Goumri‐Said
Source: Computational Condensed Matter (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Integrated computational and machine learning study of elastic, thermophysical, and ultrasonic properties of LiPb alloy Ultrasonic sensor ; Debye model ; Elastic modulus ; Anisotropy ; Thermal conductivity ; Shear modulus ; Stiffness ; Resonant ultrasound spectroscopy ; Specific modulus Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Nuclear Materials and Properties
Authors: Mohammad Rahimi | Shakirudeen A. Salaudeen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis Feature-Coupled Machine Learning Approaches to Predict the Capacitance of Biomass-Derived Carbon Electrodes in Supercapacitor Supercapacitor ; Capacitance ; Feature (linguistics) ; Biomass (ecology) ; Carbon fibers ; Electrode ; Materials science ; Process engineering ; Nanotechnology ; Computer science ; Chemistry ; Engineering ; Composite material ; Biology ; Ecology Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Shogo Wakazaki | Yuki Sakai | Kazuki Takahashi | H. Das | Masaki Azuma
Source: Research Square (preprint, 2025-12-26)
Field: Materials Science — Materials Chemistry

Large-scale molecular dynamics simulations of negative thermal expansion in Bi1-x Lax CoO3 using a pretrained machine learning force field Molecular dynamics ; Phase transition ; Negative thermal expansion ; Force field (fiction) ; Stability (learning theory) ; Field (mathematics) ; Nucleation ; Phase (matter) ; Thermal ; Process (computing) Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Zsuzsanna Koczor-Benda (8151189) | Joe Gilkes (18720741) | Francesco Bartucca (21598022) | Abdulla Al-Fekaiki (21598025) | Reinhard J. Maurer (1397926)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Structural Biasin Three-Dimensional AutoregressiveGenerative 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. Chemical space ; Property (philosophy) ; Autoregressive model ; Generative model ; Generative grammar ; Set (abstract data type) ; Space (punctuation) ; Hyperparameter ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Angus B. Keto | Elizabeth H. Krenske
Source: The Journal of Organic Chemistry (article, 2025-07-16)
Field: Chemistry — Inorganic Chemistry

Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations The barrier heights of Nazarov cyclizations are influenced by substituents in ways that can be challenging to predict. We explore the ability of machine learning (ML) models to predict the barriers by learning the activating and deactivating contributions of different substituents on the divinyl ketone. Random forest and graph neural network models are shown to achieve good predictive accuracies, with mean absolute errors of <i>ca</i>. 2 kcal/mol against density functional theory-calculated barriers, after training on a compact data set of 272 reactions using simple and interpretable features that do not require performing quantum mechanical calculations. Substituents' electronic and steric feature contributions to the barriers are quantified through feature importance analysis using the SHAP model interpretation framework. To achieve a low barrier for cyclization, the most impactful substrate design strategy entails placing substituents α to the carbonyl group to sterically control the accessible reactant conformations and electronically stabilize the forming oxyallyl cation. The analysis also explains low or high barriers for several substrates that do not conform to empirical rules. This work illustrates the ability of ML models to capture complicated synergistic and antagonistic effects on chemical reactivity, and demonstrates how ML model feature importance analysis complements insights available from density functional theory (DFT). Brønsted–Lowry acid–base theory ; Chemistry ; Catalysis ; Combinatorial chemistry ; Computer science ; Medicinal chemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Axial and Atropisomeric Chirality Synthesis
Authors: Hanqing Li | Jinlong Chen | Cheng Cheng | Li L | Ju Rong | Yudong Sui | Zhijie He | Jueyi Ye | Xiangjie Fu | Xiaohua Yu | Jing Feng
Source: JOM (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Molecular Dynamics Investigation of the Physical and Heat Transfer Properties of Magnesium Magnesium ; Molecular dynamics ; Dynamics (music) ; Materials science ; Heat transfer ; Chemical physics ; Thermodynamics ; Chemistry ; Psychology ; Physics ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Sergei Manzhos | 学 伊原 | Manabu Ihara | Methawee Nukunudompanich
Source: Institutional Repositories DataBase (IRDB) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

On the sufficiency of a single hidden layer in feed-forward neural networks used for machine learning of materials properties Artificial neural network ; Layer (electronics) ; Feature (linguistics) ; Deep learning ; Set (abstract data type) ; Key (lock) Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM