jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fan-Shun Meng | Shuhei Shinzato | Kazuki Matsubara | Jun-Ping Du | Peijun Yu | Shigenobu Ogata
Source: JOM (article, 2025-10-06)
Field: Materials Science — Metals and Alloys

A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel Abstract A neural network interatomic potential (NNIP) has been developed for the ternary system of $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -iron, carbon, and hydrogen to clarify the degradation behavior of Fe-C steels in hydrogen-rich environments. The NNIP was trained on an extensive reference database generated from spin-polarized density functional theory (DFT) calculations. It demonstrates remarkable performance in various scenarios relevant to Fe and Fe-C systems under hydrogen, including the diffusion kinetics of H and C in Fe and their thermodynamic interactions with iron vacancies, grain boundaries, screw dislocations, cementite, and cementite–ferrite interfaces. Using this NNIP, we conducted large-scale (one-million-atom) molecular dynamics (MD) simulations of uniaxial tensile tests on C-containing $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -Fe both with and without H, showing that hydrogen enhances defect accumulation during plastic deformation, which may eventually lead to material failure. Ternary operation ; Embrittlement ; Hydrogen ; Molecular dynamics ; Hydrogen embrittlement ; Interatomic potential ; Grain boundary ; Diffusion Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Nuclear Materials and Properties
Authors: Rachel E. Lim | Shun‐Li Shang | Andrew Chihpin Chuang | Thien Q. Phan | Zi‐Kui Liu | Darren C. Pagan
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks. Diffraction ; Materials science ; Plasticity ; Residual stress ; X-ray crystallography ; Thermal expansion ; Thermal ; Composite material ; Mechanics ; Optics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Nuclear Materials and Properties
Authors: Tatiane Pretto | Sergey Dayneko | Antoine Pavesic | Nicholas Sandor | Alexander Hart | Yuki Haruta | Magdalena Bazalova‐Carter | Marcos J. L. Santos | Makhsud I. Saidaminov
Source: Advanced Functional Materials (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors Abstract Flux‐regulated crystallization (FRC), a method that dynamically monitors and adjusts crystal growth from solutions in real time using computer vision and feedback control, has been recently introduced. Using FRC, centimeter‐scale perovskite single crystals at a linear growth rate of 0.2 mm h −1 with a standard deviation ( σ ) of 0.061 mm h −1 is synthesized. Here, machine learning is integrated into FRC to predict solvent evaporation rates during crystallization in real time, thus leading to an over threefold decrease in σ to 0.018 mm h −1 . This also results in improved reproducibility of perovskite crystallinity, as evidenced by average full width at half maximum of 22 ± 5 arcsec in X‐ray rocking curve measurements; and of perovskite X‐ray detectors, as evidenced by an average sensitivity of 4500 ± 500 µC Gy air −1 cm −2 at an electric field of 100 V cm −1 across 13 devices. Materials science ; Crystallization ; Flux (metallurgy) ; Evaporation ; Detector ; X-ray detector ; Solvent ; X-ray ; Chemical engineering ; Optoelectronics ; Analytical Chemistry (journal) ; Optics ; Environmental chemistry ; Metallurgy ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Ioannis Kouroudis | Poonam Poonam | Neel Misciasci | Felix Mayr | L. Müller | Zhaosu Gu | Alessio Gagliardi
Source: npj Computational Materials (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites Abstract In this paper, we propose a novel flexible optimization pipeline for determining the optimal adsorption sites, named AUGUR (Aware of Uncertainty Graph Unit Regression). Our model combines graph neural networks and Gaussian processes to create a flexible, efficient, symmetry-aware, translation, and rotation-invariant predictor with inbuilt uncertainty quantification. This predictor is then used as a surrogate for a data-efficient Bayesian Optimization scheme to determine the optimal adsorption positions. This pipeline determines the optimal position of large and complicated clusters with far fewer iterations than current state-of-the-art approaches. Further, it does not rely on hand-crafted features and can be seamlessly employed on any molecule without any alterations. Additionally, the pooling properties of graphs allow for the processing of molecules of different sizes by the same model. This allows the energy prediction of computationally demanding systems by a model trained on comparatively smaller and less expensive ones. Identification (biology) ; Optimization algorithm ; Adsorption ; Algorithm ; Computer science ; Mathematical optimization ; Mathematics ; Chemistry ; Biology Machine Learning in Materials Science ; Machine Learning and Algorithms ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Animesh Karmakar | Dhananjay | Tarak Karmakar
Source: Nano Letters (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Understanding Dynamics of Nanocluster–Organic Frameworks and Gas Diffusion from Machine Learning Potential-Based Simulations Nanocluster-organic frameworks (NOFs) are emerging materials with broad applications in sensing, photocatalysis, and optoelectronics. These photofunctional materials have an excellent luminescence switching response to gases, such as oxygen (O<sub>2</sub>) and volatile organic compounds. However, their atomistic structural evolution and the mechanism governing the diffusion of small molecules inside them remain elusive. In this study, we developed machine learning potentials to accurately model an experimentally synthesized NOF, [Ag<sub>12</sub>(S<sup><i>t</i></sup>Bu)<sub>8</sub>(CF<sub>3</sub>COO)<sub>4</sub>(bpy)<sub>4</sub>)]<sub><i>n</i></sub>, and we investigated its structure and dynamics using molecular dynamics simulations. Furthermore, we used on-the-fly probability-enhanced sampling simulations to study O<sub>2</sub> gas diffusion inside NOF pores and construct the free energy surface. The O<sub>2</sub> gas predominantly localizes around the bipyridine linker, consistent with previous experimental observations. The pore-to-pore diffusion barrier of ∼16-18 kJ/mol for O<sub>2</sub> suggests feasible diffusion of the O<sub>2</sub> at room temperature. This work presents the first-ever integrated approach for modeling a fully flexible NOF and gas diffusion within it with DFT-level accuracy. Diffusion ; Molecular dynamics ; Chemical physics ; Nanotechnology ; Materials science ; Statistical physics ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Taiqiao Liu | Zeyuan Li | J. Zhao | Xiaoyu Fei | Jiaren Feng | Y L Zuo | Mengyuan Hua | Yuzheng Guo | Sheng Liu | Zhaofu Zhang
Source: Acta Materialia (article, 2025-08-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orientation-dependent surface radiation damage in β-Ga2O3 explored by atomistic simulations Materials science ; Orientation (vector space) ; Radiation damage ; Molecular dynamics ; Surface (topology) ; Crystallography ; Radiation ; Chemical physics ; Nanotechnology ; Optics ; Geometry ; Computational chemistry ; Physics Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Majid Shabir Najar | Muzafar Rasool | Assif Assad | G. Anjum | Mandeep Singh | Farooq Hussain Bhat
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Exploring feature generation and engineering techniques for machine learning prediction of ferromagnetic Curie temperatures in perovskites Abstract Rapid estimation of Curie temperature is essential for discovering advanced functional materials like ABO 3 -type ferromagnetic perovskites used in spintronics, sensors, and multiferroics. This study applies machine learning (ML) to predict Curie temperatures directly from chemical formulae, eliminating the need for computationally intensive simulations. A manually curated dataset of 300 perovskites with known Curie temperatures is used. Features are generated by separately considering A-site and B-site cation properties and also by treating the compound as a whole. Three strategies, feature selection, recursive elimination, and principal component analysis are employed for feature engineering. Multiple models are trained and evaluated using cross-validation, with coefficient of determination R 2 as the performance metric. Shapley Additive Explanations highlights mean absolute deviation of A-site ground state volume and B-site space-group number as key factors influencing Curie temperature. The best model achieves an R 2 of 0.8 and is applied to predict Curie temperatures for 21,467 ABO 3 compositions, demonstrating the potential of ML in accelerating magnetic materials discovery. Curie temperature ; Curie ; Ferromagnetism ; Feature (linguistics) ; Curie constant ; Magnet Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Heusler alloys: electronic and magnetic properties
Authors: Xuetao Li | Liang‐Shih Fan | Chenxi Xiong | Wei Nie | Yujiao Dong | Bo Zhu | Wei Guan
Source: Angewandte Chemie International Edition (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Prediction of Ground‐ and Excited‐State Redox Potentials in Iridium(III) Photocatalysts This study introduces a data-driven framework that combines DFT calculations with machine learning to facilitate accurate and scalable predictions of ground- and excited-state redox potentials for iridium(III) photocatalysts. We first constructed independent models to identify key geometric and electronic descriptors governing redox behavior. Shapley additive explanations-based analyses revealed clear structure-activity relationships, offering mechanistic insights and rational guidance for tuning redox potentials. Based on these insights, we developed unified multi-output models-Model G for ground-state and Model E for excited-state redox potentials-to enable rapid, cost-effective, and high-throughput predictions. By modeling oxidation and reduction processes within a shared descriptor space, we can reduce computational overhead while maintaining high predictive accuracy. To assess cross-metal generalizability, residual transfer learning was applied to osmium (Os) photocatalysts. Using feature-similar complexes, the resulting transfer models (G-T, E-T) achieved performance comparable to Os-only baselines, demonstrating efficient few-shot cross-metal transfer. Collectively, this study establishes an interpretable and transferable machine-learning framework for photocatalyst discovery. This framework provides a foundation for large-scale screening and rational design across diverse transition-metal platforms, accelerating advancements in photoredox catalysis, solar fuel production, and broader sustainable energy technologies. Redox ; Scalability ; Overhead (engineering) ; Reduction (mathematics) ; Rational design ; Electron transfer ; Key (lock) ; Sustainable energy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Masar Ali Awad | Afaf Murtadha Kadhum | Azal S. Waheeb | Hussein Ali Kadhim Kyhoiesh | Hassan E. Abd Elsalam | Islam H. El Azab
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of new organic photovoltaic dyes for OPVs using light absorbance as the primary screening criterion <i>via</i> machine learning and DFT This research provides an analysis of the light absorption properties of organic dyes in different organic solvents. By employing state-of-the-art machine learning (ML) techniques, including multi-output Gaussian process regression and ensemble methods like XGBoost and Random Forest regressors, we successfully predicted solvent-specific absorbance characteristics. XGBoost demonstrated outstanding predictive efficiency, and interpretation <i>via</i> SHapley Additive exPlanations (SHAP) analysis identified the topological polar surface area as the most critical molecular descriptor. For the <i>de novo</i> design of novel dyes, we developed a Transformer-Assisted Orientation (TAO) approach, generating three iterative rounds of new structures. The photovoltaic potential of these newly designed dyes was validated through density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations. Geometry optimizations and electronic property calculations were performed at the ωB97XD/LanL2DZ level, while electronic spectra were simulated using the CAM-B3LYP/6-31G+(d,p) method with a polarizable continuum model (PCM) for acetonitrile. This integrated ML/DFT pipeline yielded dyes with remarkable predicted photovoltaic parameters, including a peak open-circuit voltage (<i>V</i> <sub>oc</sub>) of 0.96 V, a light harvesting efficiency (LHE) of 95%, a fill factor (FF) of 0.87, and a short-circuit current density (<i>J</i> <sub>sc</sub>) of 28.75 mA cm<sup>-2</sup>. This study establishes a robust, data-driven framework for the rapid discovery and design of high-performance organic photovoltaic materials. Photovoltaic system ; Absorbance ; Density functional theory ; Gaussian ; Gaussian process ; Kriging ; Random forest Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Pigment Synthesis and Properties
Authors: Xinyang Li | Qiuping Yang | Di Jin | Shicong Ding | Aitor Bergara | Yansun Yao | Li Zhu | Jijun Zhao | Guochun Yang
Source: Physical review. B./Physical review. B (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Prediction of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>o</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi mathvariant="normal">N</mml:mi><mml:mn>16</mml:mn></mml:msub></mml:mrow></mml:math>: A layered polymeric nitrogen phase Nitrogen allotropes, especially under extreme conditions, play a crucial role in understanding atomic configurations and driving innovations in high-energy materials. The phase composition of layered polymeric nitrogen (LP-N) remains unresolved, with Pba2, $C2$/c, and Pccn structures proposed as candidates. However, their relative stabilities and ambiguous x-ray-diffraction (XRD) patterns---consistent with experimental data but not definitely---along with multiple possible explanations, have been topics of ongoing debate, particularly under varying pressures. In this study, we present an unexpected orthorhombic phase, $o\ensuremath{-}{\mathrm{N}}_{16}$, predicted using machine-learning driven swarm-intelligence approach. Gibbs free-energy calculations, kinetic barrier analysis during decompression, and simulated XRD patterns reveal $o\ensuremath{-}{\mathrm{N}}_{16}$ and Pba2 as dominant LP-N phases at elevated pressures, with a partial transition from $o\ensuremath{-}{\mathrm{N}}_{16}$ to $C2$/c upon decompression. Notably, the relative stabilities of nitrogen allotropes cg-N, $o\ensuremath{-}{\mathrm{N}}_{16}, C2$/c, and Pccn are dictated by configurational and conformational isomerism of armchair chains and interchain bonding. These findings shed light on the evolution of atomic configurations under high-pressure conditions. Beyond its favorable energy density, $o\ensuremath{-}{\mathrm{N}}_{16}$ exceeds all polymeric nitrogen structures in terms of detonation velocity and detonation pressure. Algorithm ; Mathematics ; Artificial intelligence ; Computer science Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Mohammad Shojaee | Seyedsobhan Rezaei | Oliver Weeger
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated Discovery of Effective Material Models for Nonhomogeneous Hyperelastic Materials Hyperelastic material ; Ogden ; Computer science ; Materials science ; Engineering ; Composite material ; Structural engineering ; Finite element method Material Properties and Failure Mechanisms ; Manufacturing Process and Optimization ; Elasticity and Material Modeling
Authors: Bo Zhao | Hongbin Zhang
Source: Physical review. B./Physical review. B (article, 2025-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and Mössbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to such properties. It is observed that magnetic moments and Mössbauer parameters provide complementary insights into the local crystalline environment, where the statistical features cannot be captured using phenomenological models. Furthermore, we find that the scarcity of the existing data in the Materials Project (MP) poses a significant challenge in developing predictive machine learning models, whereas smooth overlap of atomic positions (SOAP)-based descriptors can be applied for reliable modeling of the enriched datasets with extra structural prototypes. This work advances the mapping of local crystalline structures to magnetic and spectroscopic properties, bridging the gap between empirical observations and theoretical models. Computation ; Intermetallic ; Throughput ; Mössbauer spectroscopy ; Magnetic moment ; Materials science ; Computer science ; Computational science ; Artificial intelligence ; Metallurgy ; Algorithm ; Condensed matter physics ; Physics ; Nuclear physics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties of Alloys
Authors: Lincoln Clark | Ann Roberts | Lukas Wesemann
Source: APL Photonics (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural reparameterization for nonlocal metasurface topology optimization Metasurfaces are emerging as a paradigm for performing all-optical, energy efficient analog computing and image processing in an ultra-compact form-factor. While various operations have been demonstrated with simple metasurface geometries, broadening the range of available processing outputs will require non-trivial and non-intuitive designs. Topology optimization (TO) is a powerful inverse design technique capable of designing such metasurfaces with significantly enhanced optical properties. The two most important design considerations are the quality of the optical performance and the minimum feature size in the resulting device. Larger features can reduce the mismatch between design and realized device performance, increasing fabrication speed and extending the scalability of the fabrication. Metasurface TO is typically performed on a pixel grid to determine the material distribution at each point. Here, we compare two pixel-based methods to neural-network based parameterizations, where the parameters of a neural-network are trained to output a metasurface design. We produce 150 different computational metasurface design tasks to compare the performance of each parameterization with regard to the performance and feature sizes. In comparison to the best pixel-based approach, a hybrid neural-network and pixel method produced designs with a median minimum feature size 2.8 times larger with only a 1.4 times increase in RMS error. Compared to a pixel method capable of producing similar critical dimensions, the optical performance of the hybrid approach is improved by 39%. Finally, as a proof of concept, we use this neural-network based parameterization to design an angular bandpass filter with relatively large critical dimensions and non-trivial performance. Pixel ; Scalability ; Feature (linguistics) ; Filter (signal processing) ; Topology (electrical circuits) ; Range (aeronautics) ; Inverse ; Band-pass filter ; Image quality Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Photonic Crystals and Applications