jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiaoyu Guo | Fei Xie | Zhe Sun | Xuechen Jiao | Wancheng Yu
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for accelerated prediction of size distributions of spherical nanoparticles from small-angle X-ray scattering Small-angle X-ray scattering (SAXS) is widely used for characterizing the particle size distribution (PSD) at the mesoscale. Conventional extraction of PSD from SAXS data typically relies on traditional numerical methods such as Monte Carlo algorithms. However, these approaches often suffer from low computational efficiency and inherent difficulties in resolving complex multimodal distributions, thus limiting their applicability in high-throughput or real-time SAXS data analysis. To overcome these limitations, here we develop a feed-forward neural network (FNN) model for the accurate and efficient PSD analysis. By embedding physical constraints <i>via</i> iterative fine-tuning, the FNN model yields physically plausible predictions and resolves key PSD features accurately, including peak positions, peak widths, and low-abundance subpopulations for both simulated and experimental SAXS data. Validation against synchrotron radiation SAXS measurements and scanning electronic microscopy (SEM) characterization of silica and polystyrene nanoparticles shows strong agreement with PSDs obtained from the Monte Carlo algorithm (McSAS) and direct imaging analysis. Importantly, the FNN model achieves approximately 1800-fold acceleration in computation speed with a processing time of ∼50 ms per one-dimensional scattering curve, far surpassing the conventional McSAS method while maintaining robust predictive performance for both monodisperse and polydisperse systems. This work provides a practical tool for the rapid, high-precision analysis of complex particle systems in materials science and nanotechnology, partially addressing the long-standing challenge of real-time scattering data analysis. Scattering ; Small-angle X-ray scattering ; Particle (ecology) ; Particle-size distribution ; Nanoparticle ; Particle size ; Distribution (mathematics) X-ray Diffraction in Crystallography ; Electrostatics and Colloid Interactions ; Machine Learning in Materials Science
Authors: Philipp Schienbein
Source: ArXiv.org (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Mimyria: Machine learned vibrational spectroscopy for aqueous systems made simple Vibrational spectroscopy provides a powerful connection between molecular dynamics (MD) simulations and experiment, but its routine use in condensed-phase systems remains limited. We introduce mimyria, a modular and automated framework that orchestrates electronic-structure reference calculations, trains atom-resolved machine-learning response models, and generates IR and Raman spectra from MD trajectories within a unified workflow. We introduce the polarizability gradient tensor (PGT) as a novel atom-resolved machine-learning target property for Raman spectroscopy, complementing the established atomic polar tensor (APT) for IR spectroscopy. As a necessary prerequisite, we demonstrate how both PGTs and APTs can accurately be computed from electronic-structure theory, validate them across formally equivalent derivative formulations, and thereby benchmark their numerical consistency. We then employ machine learning as an efficient surrogate to represent the validated APT and PGT response functions on aqueous benchmark systems. We validate the trained models directly at the level of the spectrum against explicit ab initio reference calculations and find that IR and Raman spectra converge with surprisingly small training sets. Moreover, spectral agreement improves more rapidly than the root-mean-square error (RMSE). While RMSE is straightforward to compute, statistically converged reference spectra are generally impractical to obtain, motivating the need to relate model-level errors to observable-level accuracy. By connecting these complementary error measures, we provide practical guidelines and early-stopping criteria for achieving sufficient spectral fidelity. By integrating response-tensor learning, automated training, and spectral-domain validation into a unified workflow, mimyria enables data-efficient and quantitatively reliable vibrational spectroscopy. Benchmark (surveying) ; Raman spectroscopy ; Tensor (intrinsic definition) ; Polarizability ; Mean squared error ; Spectral line ; Flexibility (engineering) ; Ab initio Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Guoyu Tong | Liping Wu | Xiuling Shang | Xiaoying Sun | Dongjiu Zhang | Changgang Wang
Source: Corrosion Science (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for visual fingerprinting of copper tubes' atmospheric corrosion in diverse environments Corrosion ; Discriminative model ; Pitting corrosion ; Support vector machine ; Tracing ; Set (abstract data type) ; Pattern recognition (psychology) Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Machine Learning in Materials Science
Authors: Alex Finkelstein | Ron Moneta | Or Zohar | Michal Rivlin | Zaiss, Moritz | Dinora Friedmann Morvinski | Or Perlman
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE) Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from signal evolutions. However, the pattern-matching algorithms or neural networks used in such inverse problems often lack principled uncertainty quantification, which limits the trustworthiness and transparency, required for clinical acceptance. Here, we describe a physics-structured variational autoencoder (PS-VAE) designed for rapid extraction of voxelwise multi-parameter posterior distributions. Our approach integrates a differentiable spin physics simulator with self-supervised learning, and provides a full covariance that captures the inter-parameter correlations of the latent biophysical space. The method was validated in a multi-proton pool chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) molecular MRF study, across in-vitro phantoms, tumor-bearing mice, healthy human volunteers, and a subject with glioblastoma. The resulting multi-parametric posteriors are in good agreement with those calculated using a brute-force Bayesian analysis, while providing an orders-of-magnitude acceleration in whole brain quantification. In addition, we demonstrate how monitoring the multi-parameter posterior dynamics across progressively acquired signals provides practical insights for protocol optimization and may facilitate real-time adaptive acquisition. Autoencoder ; Pattern recognition (psychology) ; Bayesian probability ; Covariance ; Inverse problem ; Magnetic resonance imaging ; Artificial neural network ; Uncertainty quantification Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Yejun Cao | Haosen Liu | Guoxi Zeng | Md. Zillur Rahman | Zihan Xu | Shuaicheng Guo | Deju Zhu
Source: Journal of Building Engineering (article, 2026-01-08)
Field: Materials Science — Polymers and Plastics

A review on mechanical properties and prediction models of fiber-reinforced polymer composites at elevated temperatures Ultimate tensile strength ; Fibre-reinforced plastic ; Degradation (telecommunications) ; Fiber ; Modulus ; Polymer ; Glass transition ; Thermal ; Reliability (semiconductor) Flame retardant materials and properties ; Fire effects on concrete materials ; Fire dynamics and safety research
Authors: Khoa Nguyen | Brendt Wohlberg | Oleg Korobkin | Marc Klasky
Source: arXiv (Cornell University) (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Material Identification using Multi-Modal Intrinsic Radiation and Radiography We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composition whose radii are assumed known from radiography. High-purity germanium (HPGe) spectra are reduced to net counts in selected Pu-239 photo-peaks, while neutron multiplicity information is summarized by Feynman variances Y2 and Y3 computed from factorial moments of the neutron counting statistics. Using synthetic data generated with the Gamma Detector Response and Analysis Software (GADRAS) for a range of shielding materials and thicknesses, we cast the material identification problem as a supervised multi-class classification task over all admissible shell-material combinations. We demonstrate that a random forest classifier trained on combined gamma and neutron features achieves almost perfect identification accuracy for single-shell cases, and substantial performance gains for more challenging double-shell configurations relative to gamma-only classification. Alternative statistical and machine-learning formulations for this multi-class problem are examined along with examination of the impact of model-mismatch between the forward model and the test cases as given by variations in the statistical noise. Opportunities for extending the approach to more complex geometries and experimental data are also discussed. Neutron ; Detector ; Beryllium ; Electromagnetic shielding ; Gamma spectroscopy ; SPHERES ; Identification (biology) ; Multiplicity (mathematics) ; Classifier (UML) Radiation Shielding Materials Analysis ; Nuclear Physics and Applications ; Radiation Detection and Scintillator Technologies
Authors: Hendrik Hensel | Markus Clemens
Source: IET Science Measurement & Technology (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Optimisation of FGM Application in HVDC GIL Based on Deep Neural Network ABSTRACT High‐voltage direct‐current gas‐insulated transmission lines are employed due to their advantages considering safety, voltage capacity, self‐healing of the gas and reliability. However, under DC condition, high electric field stress and charge accumulation may occur, which can lead to partial discharges and therefore to insulation failures. The control of the electric field within the system is critical for ensuring operational reliability. Field control techniques are applied to regulate the electric field distribution. This work investigates the optimisation of the field control technique functional grading materials, where the spatial distribution of conductive filler particles within the spacer is adjusted to obtain a minimised and homogenised electric field distribution. Therefore, a deep neural network is utilised as a surrogate model to find the optimised distribution to keep the computational costs lower compared to conventional FEM simulations, since several simulations need to be performed for the optimisation. The results show that the DNN‐based surrogate model enables a computationally efficient optimisation of the HVDC GIL spacer. Artificial neural network ; High-voltage direct current ; Finite element method ; Voltage ; Electric power transmission ; Electric field ; Electrical conductor ; Surrogate model High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Thermal Analysis in Power Transmission
Authors: Simon Divilov | Hagen Eckert | Nico Hotz | Xiomara Campilongo | Stefano Curtarolo
Source: Acta Materialia (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Disorder viscosity correction approach to calculate spinodal temperature and wavelength Spinodal ; Spinodal decomposition ; Viscosity ; Ab initio ; Microstructure ; Energy (signal processing) ; Transient (computer programming) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Quantum many-body systems
Authors: Simon Divilov | Hagen Eckert | Nico Hotz | Xiomara Campilongo | Stefano Curtarolo
Source: arXiv (Cornell University) (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Disorder viscosity correction approach to calculate spinodal temperature and wavelength Spinodal decomposition, a key mechanism to microstructure formation in materials, has long posed challenges for predictive modeling, due to the need for parameter-free approaches that accurately capture local energy landscapes. In this work, we propose an approach to predict spinodal behavior by introducing a disorder viscosity correction to bulk free energies computed from finite, small, representative cells. We approximate the energy penalty required to transition into a disordered state to enable the stabilization of locally concave bulk free energy regions - essential for interface formation - while suppressing long-range concentration fluctuations. This approximation circumvents the complexity of full ab initio parameterization of interfacial properties and is well-suited for high-throughput and machine-learning frameworks. Our approach captures the necessary physics underpinning spinodal kinetics, offering a scalable route to predict spinodal regions in compositionally complex and high-entropy materials. Spinodal ; Spinodal decomposition ; Viscosity ; Ab initio ; Energy (signal processing) ; Microstructure ; Transient (computer programming) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Quantum many-body systems
Authors: Thomas Warford | Fabian L. Thiemann | Gábor Csányi
Source: Open MIND (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that fMLIPs trained on large datasets such as MPtrj, Alexandria, and OMat24 encode inconsistencies from the Materials Project's selective use of the Hubbard U correction, which is applied to certain transition metals only if O or F atoms are present in the simulation cell. This inconsistent use of +U creates two incompatible potential-energy surfaces (PES): a lower-energy GGA surface and a higher-energy GGA+U one. When trained on both, MLIPs interpolate between them, leading to systematic underbinding, or even spurious repulsion, between U-corrected metals and oxygen- or fluorine-containing species. Models such as MACE-OMAT and -MPA exhibit repulsion between U-corrected metals and their oxides, limiting their value for studying catalysis and oxidation. We link the severity of this pathology to the oxygen number density in U-corrected training configurations. This explains why OMAT-trained models are most affected and suggests the issue might worsen as expanding future datasets increasingly include configurations with low oxygen content, such as those generated through combinatorial exploration of multi-element or defect-containing systems. Our simple per-U-corrected-atom shift aligns PBE+U and PBE energies for identical structures, yielding a smoother PES compared to existing correction schemes, which target phase diagram accuracy. As a result, models trained on datasets with our shift applied exhibit smaller mean absolute errors for the adsorption energies of oxygen on U-corrected elemental slabs. Since datasets omitting +U entirely (e.g. MatPES, MP-ALOE) avoid these pathologies, we recommend excluding +U in future fMLIP datasets. For existing datasets, our post-hoc correction provides a low-cost improvement. Spurious relationship ; Limiting ; Ab initio ; Hubbard model ; Density functional theory ; Surface (topology) ; Ab initio quantum chemistry methods ; Transition metal Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Thomas Warford | Fabian L. Thiemann | Gábor Csányi
Source: ArXiv.org (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that fMLIPs trained on large datasets such as MPtrj, Alexandria, and OMat24 encode inconsistencies from the Materials Project's selective use of the Hubbard U correction, which is applied to certain transition metals only if O or F atoms are present in the simulation cell. This inconsistent use of +U creates two incompatible potential-energy surfaces (PES): a lower-energy GGA surface and a higher-energy GGA+U one. When trained on both, MLIPs interpolate between them, leading to systematic underbinding, or even spurious repulsion, between U-corrected metals and oxygen- or fluorine-containing species. Models such as MACE-OMAT and -MPA exhibit repulsion between U-corrected metals and their oxides, limiting their value for studying catalysis and oxidation. We link the severity of this pathology to the oxygen number density in U-corrected training configurations. This explains why OMAT-trained models are most affected and suggests the issue might worsen as expanding future datasets increasingly include configurations with low oxygen content, such as those generated through combinatorial exploration of multi-element or defect-containing systems. Our simple per-U-corrected-atom shift aligns PBE+U and PBE energies for identical structures, yielding a smoother PES compared to existing correction schemes, which target phase diagram accuracy. As a result, models trained on datasets with our shift applied exhibit smaller mean absolute errors for the adsorption energies of oxygen on U-corrected elemental slabs. Since datasets omitting +U entirely (e.g. MatPES, MP-ALOE) avoid these pathologies, we recommend excluding +U in future fMLIP datasets. For existing datasets, our post-hoc correction provides a low-cost improvement. Spurious relationship ; Limiting ; Ab initio ; Hubbard model ; Density functional theory ; Surface (topology) ; Ab initio quantum chemistry methods ; Transition metal Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Tieyuan Bian | Wenjia Zhu | Qiong Lei | Jun Yin
Source: ACS Applied Materials & Interfaces (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces Inorganic and hybrid lead halide perovskites are among the most promising candidates for next-generation optoelectronic devices. However, their further development is critically constrained by stability issues, including phase degradation, migration, and interfacial reactions. Overcoming these challenges requires a detailed understanding of the underlying atomic-scale mechanisms. Density functional theory (DFT) and ab initio molecular dynamics (AIMD) offer essential electronic and structural insights, but their high computational cost limits simulations to relatively small systems and short time scales, making it difficult to capture long-term evolution. Classical MD can access larger length and time scales but often lacks accurate and transferable force fields for lead halide perovskite systems. Machine learning potentials (MLPs) have emerged as an alternative solution to bridge this gap, enabling large-scale, long-time-scale MD simulations with near-DFT accuracy at a fraction of the computational cost. In this perspective, we review recent applications of MLPs to inorganic and hybrid lead halide perovskites, including phase behavior, ion migration, and perovskite interfaces. We further discuss current challenges related to model efficiency and transferability and outline future opportunities for deploying MLPs to tackle outstanding questions in perovskite stability, degradation, and device-relevant operation. Halide ; Perovskite (structure) ; Lead (geology) ; Transferability ; Stability (learning theory) ; Density functional theory Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Milan Pejić | Dejan Zagorac | Christian Schön | Jelena Zagorac | Tamara Škundrić | Matej Fonović | Branko Matović
Source: VinaR (Institute of Nuclear Sciences "Vinča") (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Modeling Ternary Rare Earth Compounds: From Global Search to Electronic Structure Prediction Rare earth compounds are a large and versatile class of materials relevant to electronic, energyrelated, catalytic, biomedical, optical, and magnetic applications, and they often exhibit rich polymorphism under varying thermodynamic conditions. In this context, theory- and computationdriven structure prediction has become an important complement to experiment, enabling systematic identification of low-energy polymorphs and evaluation of their stability and properties across wide pressure and temperature ranges. Here we present computational investigations of the energy landscapes of five ternary rare earth crystal systems: LaOI [1], ScOCl [2], Ce3O3N [3], HoFSe [4], and LaFS. For each system, extensive global searches are performed with periodic cells comprising multiple formula units (up to 16 f.u. for some systems). Candidate structures are generated using simulated annealing algorithms implemented in G42+ [5] with empirical interatomic potentials (Lennard-Jones type) under pressures of 0–100 GPa, typically producing millions of trial configurations per system. The generated structures are clustered and ranked according to their energy, recurrence statistics, and symmetry. In parallel, data-mining and machine-learning potentials are applied to accelerate the identification of promising minima and to support robust structural classification. For each system, several hundred of the most promising structures are selected and subsequently refined using density functional theory (DFT) calculations implemented in CRYSTAL, Quantum ESPRESSO, or VASP. For the lowest-energy polymorphs, we perform detailed crystallographic analysis and compute energy–vs.–volume relations, E(V). Pressure-dependent thermodynamic stability is assessed from enthalpy–vs.–pressure plots, H(p), to identify competitive phases and potential pressure-induced phase transitions. Finally, the electronic structure of representative polymorphs is evaluated using DFT to compute band structures and band gaps, thus providing both the thermodynamic stability and electronic properties for the investigated structures of each ternary rare earth system. Crystal structure prediction ; Ternary operation ; Electronic structure ; Maxima and minima ; Density functional theory ; Crystal structure ; Stability (learning theory) ; Simulated annealing ; Energy minimization Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; High-pressure geophysics and materials