jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Dalheim Øien | Ole Runar Myhr | Geir Ringen
Source: Metals (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Parallel Hybrid Modeling of Al–Mg–Si Tensile Properties Using Density-Based Weighting A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought aluminium applications, this work proposes, analyses, and discusses a parallel framework that applies an adaptive weighting coefficient derived from local observation density. Based on existing datasets from a range of Al-Mg-Si alloys, such a model is trained and tested in an iterative manner to study its robustness, by emulating a shift in observed alloy composition. The results indicate that the hybrid model is able to combine the interpolative strength of machine learning for cases similar to previous observations with the explorative strength of physics-based (Kampmann–Wagner Numerical) modeling for previously unobserved parameter combinations, as the hybrid model shows higher or similar accuracy than the best of its constituents across the majority of the sequence. The observed model characteristics are promising for predicting the effect of increased compositional variation inherent in PCM. Finally, possible future research is discussed. Weighting ; Ultimate tensile strength ; Range (aeronautics) ; Work (physics) ; Scale (ratio) ; Aluminium alloy ; Material properties Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Chuanjie Wang | Yuejie Hu | Haiyang Wang | Mao Chen | Gang Chen | Bin Guo | Mingwang Fu
Source: Research Square (preprint, 2026-01-09)
Field: Materials Science — Materials Chemistry

An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data Nickel titanium ; Shape-memory alloy ; Slip (aerodynamics) ; Degradation (telecommunications) ; Transformation (genetics) ; Process (computing) ; Microstructure Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Chenyi Ji | Kian P. Abdolazizi | Hagen Holthusen | Christian J. Cyron | Kevin Linka
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain and stress. Machine learning has lead to considerable advances in this field lately. Here we introduce inelastic Constitutive Kolmogorov-Arnold Networks (iCKANs). This novel artificial neural network architecture can discover in an automated manner symbolic constitutive laws describing both the elastic and inelastic behavior of materials. That is, it can translate data from material testing into corresponding elastic and inelastic potential functions in closed mathematical form. We demonstrate the advantages of iCKANs using both synthetic data and experimental data of the viscoelastic polymer materials VHB 4910 and VHB 4905. The results demonstrate that iCKANs accurately capture complex viscoelastic behavior while preserving physical interpretability. It is a particular strength of iCKANs that they can process not only mechanical data but also arbitrary additional information available about a material (e.g., about temperature-dependent behavior). This makes iCKANs a powerful tool to discover in the future also how specific processing or service conditions affect the properties of materials. Viscoelasticity ; Constitutive equation ; Relation (database) ; Artificial neural network ; Process (computing) ; Identification (biology) ; Field (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Yingdi Jin | Xinming Qin | Ruichen Liu | Jie Liu | Zhenyu Li | Jinlong Yang
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction Density functional theory (DFT) is a cornerstone of computational chemistry and materials science, but its computational cost limits its use in large-scale and high-throughput applications. While machine learning has accelerated energy prediction for specific molecular classes, transferable prediction of electron density across diverse chemical spaces remains challenging. Here, we present a universal framework based on Fourier Neural Operators (FNOs) that directly learns the mapping from external potentials to electron density distributions. Unlike conventional approaches that rely on explicit atomic orbitals, basis sets, or handcrafted descriptors, the proposed method captures global electronic interactions and long-range correlations through operator learning in the spatial-frequency domain. Trained on datasets spanning multiple elements and molecular geometries, the model achieves zero-shot generalization to entirely unseen molecular systems and accurately predicts their electron densities without retraining. This transferability arises from the intrinsic ability of FNOs to represent global structure in continuous fields. Our work establishes neural operator learning as a promising route for fast, accurate, and transferable electronic structure prediction, with potential applications in high-throughput screening and chemical space exploration. Generalization ; Operator (biology) ; Artificial neural network ; Fourier transform ; Basis (linear algebra) ; Density functional theory ; Electronic structure ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Xiang Huang | Junfeng Zhu | Jing Zhao | Ji'an Wang | Bing Yao | Shenghong Ju | Weigang Ma
Source: Applied Physics Letters (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Quantum-inspired genetic algorithm for designing thermally conductive polymers Designing polymers with high intrinsic thermal conductivity (TC) faces challenges due to the vast chemical space and substantial resource requirements of conventional methods. Here, we develop a quantum-inspired genetic algorithm (QGA) that integrates quantum computing concepts with classical evolutionary optimization to enable efficient polymer design. Using a deep neural network trained on molecular fingerprints as a surrogate model for rapid property evaluation, the QGA demonstrates superior optimization capability and convergence stability compared to classical genetic algorithms in designing ternary alternating copolymers. When applied to the design of pentameric alternating copolymers within a candidate space comprising over 1 × 107 possible structures, the method successfully identified that 10.4% of the 9975 designed candidates achieved a predicted TC &amp;gt; 0.40 W m−1 K−1. Molecular dynamics simulations validate the predictions, while structural analysis reveals that rigid, conjugated fragments serve as critical building blocks that facilitate thermal transport primarily through intra-chain energy transfer. This work establishes an effective strategy for inverse design of thermally conductive polymers and demonstrates the potential of quantum-inspired optimization in the development of advanced materials. Ternary operation ; Genetic algorithm ; Polymer ; Convergence (economics) ; Electrical conductor ; Evolutionary algorithm ; Thermal stability ; Conductive polymer ; Stability (learning theory) Thermal properties of materials ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Haeshini Jegan | Alexandros Filippas | Enpei Yao | Aaditya Lakshmanan | Nian Liu
Source: ChemRxiv (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

EIS Prediction of degradation of Aluminum beverage can lids using LSTM neural networks Corrosion prediction in aluminum can lids is important to maintain the longevity and safety of beverage containers, with Electrochemical Impedance Spectroscopy (EIS) serving as a helpful modern tool to assess coating performance. This study aims to develop a Long Short-Term Memory (LSTM) network for predicting EIS spectra, enabling more efficient and real-time corrosion assessments. The impedance data was collected using a custom-designed experimental setup that replicated real beverage can environments, generating comprehensive EIS spectra under controlled conditions. This data was then used to train and validate an LSTM model tailored for time-series predictions. The model uses a three-dimensional input structure, incorporating frequency, real impedance (Z'), and imaginary impedance (Z'') as input features. This approach captured broader changes in the EIS spectra, allowing the model to simultaneously map changes in Nyquist plots, Bode magnitude, and phase angle plots. The results demonstrated good predictive accuracy, with the model capturing the complex dynamics of EIS spectra over conventional analytical methods. This study tries to integrate advanced machine learning with electrochemical analysis to enhance material evaluation. This approach can be expanded to predict corrosion not only in coated aluminum lids but also across broader applications involving aluminum and its alloys. By incorporating recurrent neural networks (RNNs) or advanced LSTM architectures, future work could further refine predictions of EIS spectra, advancing corrosion monitoring techniques in diverse industrial and environmental contexts. Dielectric spectroscopy ; Artificial neural network ; Corrosion ; Electrical impedance ; Degradation (telecommunications) ; Deep learning ; Nyquist plot Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: E. B. Ettah | M. E. Ishaje | K. A. Minakova | V. A. Sirenko | I. S. Bondar
Source: arXiv (Cornell University) (preprint, 2026-03-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: structural, electronic, mechanical, and thermodynamics properties We studied the structural, electronic, mechanical, and thermodynamic properties of N2CaNa full Heusler alloys using density functional theory (DFT). Results for the structural analysis establishes structural stability with a minimum formation energy of 29.90eV. The compounds is brittle and mechanically stable, having checked out with the Pugh criteria. B/G ratio for N2CaNa is 4.766 as computed in Tab.2, hence the material is ductile. N2CaNa alloy is ductile in nature. Debye model correctly predicts the low temperature dependence of heat capacity, which is proportional to the debye T3 law. Just like the Einstein model, it also recovers the Dulong-Petit law at high temperatures, suggests thermodynamic stability of the compounds at moderate temperatures. The results demonstrate potential for applications in spintronics, structural engineering, and other fields requiring materials with tailored properties. Alloy ; Debye model ; Brittleness ; Structural stability ; Density functional theory ; Debye ; Material properties ; Stability (learning theory) Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research ; Thermal Expansion and Ionic Conductivity
Authors: Joshan Grewal
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Based Approach for Optimal Ultimate Tensile Strength and Elongation of Cast Aluminium Alloys - Investigating the Impact of Tramp Elements Ultimate tensile strength ; Aluminium ; Elongation ; Tramp ; Aluminium alloy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Chenyi Ji | Kian P. Abdolazizi | Hagen Holthusen | Christian J. Cyron | Kevin Linka
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models Viscoelasticity ; Constitutive equation ; Relation (database) ; Artificial neural network ; Process (computing) ; Identification (biology) ; Field (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Chenyi Ji | Kian P. Abdolazizi | Hagen Holthusen | Christian J. Cyron | Kevin Linka
Source: ArXiv.org (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain and stress. Machine learning has lead to considerable advances in this field lately. Here we introduce inelastic Constitutive Kolmogorov-Arnold Networks (iCKANs). This novel artificial neural network architecture can discover in an automated manner symbolic constitutive laws describing both the elastic and inelastic behavior of materials. That is, it can translate data from material testing into corresponding elastic and inelastic potential functions in closed mathematical form. We demonstrate the advantages of iCKANs using both synthetic data and experimental data of the viscoelastic polymer materials VHB 4910 and VHB 4905. The results demonstrate that iCKANs accurately capture complex viscoelastic behavior while preserving physical interpretability. It is a particular strength of iCKANs that they can process not only mechanical data but also arbitrary additional information available about a material (e.g., about temperature-dependent behavior). This makes iCKANs a powerful tool to discover in the future also how specific processing or service conditions affect the properties of materials. Viscoelasticity ; Constitutive equation ; Relation (database) ; Artificial neural network ; Process (computing) ; Identification (biology) ; Field (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Max Großmann | Malte Grunert | Erich Runge
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Broken neural scaling laws in materials science In materials science, data are scarce and expensive to generate, whether computationally or experimentally. Therefore, it is crucial to identify how model performance scales with dataset size and model capacity to distinguish between data- and model-limited regimes. Neural scaling laws provide a framework for quantifying this behavior and guide the design of materials datasets and machine learning architectures. Here, we investigate neural scaling laws for a paradigmatic materials science task: predicting the dielectric function of metals, a high-dimensional response that governs how solids interact with light. Using over 200,000 dielectric functions from high-throughput ab initio calculations, we study two multi-objective graph neural networks trained to predict the frequency-dependent complex interband dielectric function and the Drude frequency. We observe broken neural scaling laws with respect to dataset size, whereas scaling with the number of model parameters follows a simple power law that rapidly saturates. Scaling law ; Scaling ; Artificial neural network ; Power law ; Function (biology) ; Physical law ; Dielectric ; Complex system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Daria M. Arkhipova | Daniil A. Boiko | Aleksandr A. Oganov | Alexey S. Kashin | Evgeniy G. Gordeev | Valentine P. Ananikov
Source: Scientific Data (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Image-to-molecule benchmarking dataset with fractal pattern and hierarchical morphology recognition The unique phenomenon of high morphological diversity of quaternary phosphonium salts (QPSs) has been observed via electron and optical microscopy. The molecular structure of the QPSs, which differ by one methylene group, was shown to be reflected in the microstructure of the crystallized droplets. Here, we describe experimental datasets of scanning electron and optical microscopy images at different magnifications, illustrating the versatile microstructures of 19 homologous QPSs. The unique patterns that appear in the microscopy images of the QPS are related to the molecular structure. The described datasets of microscopy images are made openly available for scientific purpose and include hierarchical morphological patterns and fractal elements. Importantly, the datasets are suitable for both directions of machine learning exploration: recognizing molecular formulas from microscopy images and, conversely, predicting morphological patterns from molecular structures. This bidirectionality establishes a benchmark for bridging the molecule-morphology gap and advancing data-driven materials design. Bridging (networking) ; Pattern recognition (psychology) ; Benchmarking ; Microscopy ; Benchmark (surveying) ; Fractal dimension ; Fractal ; Morphology (biology) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Crystallography and molecular interactions
Authors: Kai Riedmiller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

KIMMDY: Simulating Reactions in Molecular Dynamics KIMMDY is a software extending the possibilities of established Molecular Dynamic techniques to chemical reactions. Traditional Molecular Dynamics (MD) simulations can not model chemical reactions, although they would be crucial to understand many processes like aging of skin and tendons. KIMMDY enables studying such systems computationally by combining classical MD simulations with kinetic Monte Carlo (kMC) steps to model chemical reactions. The kMC steps depend on reaction rates, which are obtained using machine learning-, or physical models.This talk highlights the creation process and architecture of KIMMDY. At its core, it is a workflow manager written in python, handling and moving data between machine learning models, physical models, and existing MD software. KIMMDY is build in a modular way, with many parts implemented in separate plugins. To orchestrate dependencies between them, and ensure compatibility, we use uv workspaces. Since we welcome future contributions from outside parties, we aim to make the barrier of entry as low as possible. To that end, we have ensured that modern editors can recognize our input file format and provide help text, and perform type checking through the use of json schemas. Furthermore, we provide documentation and tutorials rendered by Quarto to include executable code in the webpage creation process, generating visually pleasing, up-to-date documentation. Executable ; Workflow ; Software ; Process (computing) ; Documentation ; Modular design ; JSON ; Molecular dynamics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Shoma Kawano | Futoshi Utsuno | Aoto Matsuo | Takahiro Ohkubo
Source: ChemRxiv (article, 2026-02-03)
Field: Chemistry — Spectroscopy

Li+ Transport in Ca-Substituted β-Li3PS4: Role of PS43- Vibrations and Rotations from Machine-Learning Molecular Dynamics Vibration ; Molecular dynamics ; Dynamics (music) ; Rotation (mathematics) ; Work (physics) Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: Fuchun Ge | Yuxinxin Chen | Pavlo O. Dral
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present a novel neural network architecture, MDtrajNet, and a pre-trained foundational model, MDtrajNet-1, that directly generates MD trajectories across chemical space, bypassing force calculations and integration. MDtrajNet combines equivariant neural networks with a transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories. This approach accelerates simulations by up to two orders of magnitude and yields better accuracy than MD propagated with established machine-learning interatomic potentials trained on the same data. Remarkably, the errors of the trajectories generated by MDtrajNet-1 for various seen and even unseen small-sized molecular systems are close to those of the conventional ab initio MD. The current limitations of MDtrajNet-1 are attributed to the relatively small size of the chemical space in its training data; however, even for bigger, unseen systems, MDtrajNet-1 provides a good starting point for fine-tuning and obtaining system-specific models. The architecture’s flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types. By overcoming the intrinsic speed barrier of conventional MD, MDtrajNet opens new frontiers in efficient and scalable atomistic simulations. Artificial neural network ; Molecular dynamics ; Scalability ; Point (geometry) ; Transferability ; Space (punctuation) ; Boundary (topology) ; Chemical space Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Yuichi Kamata | Quoc Hoan Tran | Yasuhiro Endo | Hirotaka Oshima
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Quantum Transformer for Molecular Learning: Multi-Configuration Ground-State Energy Prediction The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges with quantum data. With a growing focus on leveraging quantum machine learning for quantum data, particularly in quantum chemistry, we propose the Molecular Quantum Transformer (MQT) for modeling interactions in molecular quantum systems. By utilizing quantum circuits to implement the attention mechanism on the molecular configurations, MQT can efficiently calculate ground-state energies for all configurations. Numerical demonstrations show that in calculating ground-state energies for H2, LiH, BeH2, and H4, MQT outperforms the classical Transformer, highlighting the promise of quantum effects in Transformer structures. Furthermore, its pretraining capability on diverse molecular data facilitates the efficient learning of new molecules, extending its applicability to complex molecular systems with minimal additional effort. Our method offers an alternative to existing quantum algorithms for estimating ground-state energies, opening new avenues in quantum chemistry and materials science. Quantum ; Quantum machine learning ; Transformer ; Quantum computer ; Quantum algorithm ; Quantum sensor ; Quantum circuit ; Quantum annealing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Molecular spectroscopy and chirality