jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kousuke Nakano | Stefano Battaglia | Jürg Hutter
Source: The Journal of Chemical Physics (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Fast evaluation of unbiased atomic forces in <i>ab initio</i> variational Monte Carlo via the Lagrangian technique Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schrödinger equation, suitable for modern many-core supercomputer architectures. Despite its potential, one of the major drawbacks that still hinders QMC applications, especially when targeting dynamical properties of large systems or extensive datasets, is the lack of an affordable method to compute atomic forces that are consistent with the corresponding potential energy surfaces (PESs), also known as unbiased atomic forces. Recently, one of the authors in the present paper proposed a way to obtain unbiased forces with the Jastrow-correlated Slater determinant Ansatz, where the determinant part is frozen to the values obtained by a mean-field method, such as density functional theory [K. Nakano, M. Casula, and G. Tenti, Phys. Rev. B 109, 205151 (2024)]. However, the proposed method has a significant drawback for its applications: for a system with N nuclei, one requires 6N additional density functional theory (DFT) calculations to get unbiased forces, which is not negligible as the system size increases. This paper presents a way to replace the 6N DFT calculations with a single coupled-perturbed Kohn-Sham calculation, following the so-called Lagrangian technique established in quantum chemistry. This improves the computational cost and scalability of the method. We also demonstrate that the developed unbiased variational Monte Carlo (VMC) force calculation improves not only the consistency with PESs but also its accuracy, by investigating three molecules from the rMD17 benchmark set, and comparing the unbiased VMC forces with those obtained by the coupled-cluster singles and doubles with perturbative triples [CCSD(T)] calculations. We found that the bare VMC forces are biased from the CCSD(T) ones, while the unbiased ones give values closer to those of the CCSD(T) ones. Our benchmark test also reveals that the unbiased VMC forces yield very consistent values with hybrid and meta generalized gradient approximations (e.g., ωB97X-D3BJ and ωB97M-D3BJ), but do not necessarily yield values that are very close to those of CCSD(T). Our finding paves the way to generate machine learning interatomic potentials based on VMC forces more efficiently and accurately. Benchmark (surveying) ; Quantum Monte Carlo ; Monte Carlo method ; Ab initio ; Consistency (knowledge bases) ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiaoguang Sun | Pranpreeya Wangjina | Piya Khamsuk | Chuanying Li | Jie Wang | Ekkarut Viyanit | Wanida Pongsaksawad
Source: Preprints.org (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

In‐Situ Atmospheric Corrosion Monitoring of Coated Aluminum Alloys Exposed in Tropical Monsoon Climate Organic coating is the most applied method for corrosion protection. However, they can degrade over time by the effect of UV, moisture, and corrosive media. In order to monitor the coating performance for proper maintenance planning, an electrochemical sensor was fabricated from aluminum alloy and coated with 4 coating systems: (1) epoxy primer, (2) epoxy primer/polyurethan topcoat, (3) epoxy primer/ polyurethan topcoat/ aluminum powder-containing polyester resin, and (4) epoxy primer/ polyurethan topcoat/ aluminum powder-containing polyester resin/ acrylic. The sensors were exposed together with corresponding coupon samples at Pathum Thani (PTI: suburban) and Chon Buri (CBI: mild marine) in Thailand for 2 years. Electrochemical impedance spectroscopy measurement (EIS) via the sensor recorded the impedance and capacitance of coatings with parallel meteorological monitoring. Impedance data were converted into a Coating Aging Index to evaluate degradation. Rapid coating deterioration occurred at PTI during wet seasons, while CBI showed negligible changes. Among the examined variables via machine learning model, exposure time most strongly influenced coating degradation. Single epoxy layer exhibited the lowest durability, whereas additional polyurethane, aluminum‑pigmented polyester, and acrylic coatings provided progressively superior protection. Epoxy ; Coating ; Dielectric spectroscopy ; Aluminium ; Corrosion ; Polyester ; Alloy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations through Ewald summation of electrostatic contributions. In contrast, machine learning potentials (MLPs) typically rely on local atomic environment descriptors, and long-range interactions are often neglected. Such approximations may introduce systematic energetic errors and lead to inaccuracies in predicted material properties. To systematically investigate the impact of long-range interactions in ferroelectric BaTiO3 within the framework of MLPs, we developed a long-range MACELES model and compared its performance with the previously reported BaTiO3 MACE model across four key properties (phonon dispersion, phase transition behavior, mechanical response, and ferroelectric properties including dielectric constants). We find that qualitative behaviors, including phase transitions, stress-induced polarization switching, and polarization-electric field hysteresis, are consistently reproduced by both models. In contrast, quantitative properties such as transition temperatures, elastic constants, and dielectric constants exhibit systematic improvements in MACELES model, highlighting the importance of incorporating long-range electrostatics for accurately describing the structural and dielectric responses of BaTiO3. These results suggest that while long-range interactions play a role in improving quantitative accuracy, their omission does not significantly alter the qualitative ferroelectric behavior of BaTiO3. Ferroelectricity ; Dielectric ; Phase transition ; Electrostatics ; Polarization (electrochemistry) ; Electric field ; Dielectric response ; Phase (matter) ; Field (mathematics) Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Lianlian Cao | Ming Chen | Zilan Wang
Source: Journal of Food Measurement & Characterization (article, 2026-01-17)
Field: Chemistry — Analytical Chemistry

Causal deep learning for tea quality assessment via counterfactual generation and interpretability verification Interpretability ; Counterfactual thinking ; Causal model ; Quality (philosophy) ; Deep learning ; Enhanced Data Rates for GSM Evolution ; Quality assessment ; Artificial neural network Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Raul O. Ribeiro | G. P. S. Rosa | Rafael A. Penchel | M. A. G. Martinez | J.R. Bergmann | Fernando L. Teixeira
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient Higher Order Spectral Element Method for Analyzing Modal Fields in Anisotropic Metamaterial Fibers We present an efficient higher-order spectral element method formulated in cylindrical coordinates for analyzing electromagnetic fields in anisotropic metamaterial fibers. The formulation employs higher-order two-dimensional basis functions associated with the zeros of the completed Lobatto polynomial to represent the electromagnetic fields and uses a complex coordinate-stretched perfectly matched layer to mimic a radially unbounded surrounding air medium. We apply our technique to model anisotropic graded-index, strongly biaxial anisotropic, lossless, and lossy hyperbolic metamaterial fibers. The results show that the SEM requires less than 1% of the degrees of freedom (memory cost) used by FEM while providing solutions with similar accuracy. In addition, the computation time required by SEM is approximately 1/3 of that needed by FEM for the examples considered. A highly efficient and flexible modeling method, such as the proposed SEM, is highly desirable for synthetic data generation and training of machine learning methods and for rapid design of anisotropic and metamaterial fibers, offering potential applications in optoelectronics and advanced material design. Metamaterial ; Lossy compression ; Finite element method ; Anisotropy ; Electromagnetic field ; Degrees of freedom (physics and chemistry) ; Computation ; Modal ; Spectral element method Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Electromagnetic Simulation and Numerical Methods
Authors: Fabrizio Camerin | Susana Marín-Aguilar | Anna Stradner | Peter Schurtenberger | Emanuela Zaccarelli
Source: ArXiv.org (article, 2026-03-19)
Field: Chemistry — Physical and Theoretical Chemistry

Deciphering Molecular Charge Anisotropy: the Case of Antibody Solutions Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective properties via a colloid-inspired coarse-grained modeling combined with neural network-assisted optimization. Using monoclonal antibodies as model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. Close inspection of our data further uncovers how specific physical features and spatial arrangements of localized charge patches significantly influence the solution structure. This transferable strategy provides a predictive pathway to decode and control charge-driven interactions in complex biomolecules and, more generally, in heterogeneously-charged soft matter systems, with immediate relevance to protein formulation and biomaterials engineering. Electrostatics ; Charge (physics) ; Static electricity ; Biomolecule ; Molecular biophysics ; Inverse ; Compressibility ; Diffusion ; Monoclonal antibody Electrostatics and Colloid Interactions ; Pickering emulsions and particle stabilization ; Nanopore and Nanochannel Transport Studies
Authors: Shuangyun Li | Lingxi Tu | Yan Jin | Yao Lu | Siting Liu | Yiting Xu | Jiahao Ding | Pengyu Gan | Ya Jin | Meifeng Xu | Chaonan Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-05)
Field: Materials Science — Surfaces, Coatings and Films

Highly sensitive superhydrophobic SERS substrate combined with machine learning for precise identification and classification of nanoplastics Substrate (aquarium) ; Identification (biology) ; Substrate specificity ; Pattern recognition (psychology) ; Nanoparticle Surface Modification and Superhydrophobicity ; Gold and Silver Nanoparticles Synthesis and Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Haining Liu | Ruifeng Zheng | Zichen Deng | Haimin Yao
Source: International Journal of Engineering Science (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Inclined mode-I elliptic crack problem in one-dimensional hexagonal quasicrystals Phason ; Stress intensity factor ; Isotropy ; Quasicrystal ; Perpendicular ; Transverse isotropy ; Displacement (psychology) ; Plane (geometry) ; Fracture (geology) Quasicrystal Structures and Properties ; Microstructure and mechanical properties ; Cellular and Composite Structures
Authors: Mohammad Bakhsh | Victor Trinquet | Rogério Almeida Gouvêa | Gian-Marco Rignanese | Samuel Poncé
Source: arXiv (Cornell University) (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$ Cs$_2$KInI$_6$ is a promising lead-free halide double perovskite with a calculated direct band gap of 1.24 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm$\bar{3}$m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first principles calculations on 11 of these. The most stable phase has Cmc$2_1$ symmetry with 20 atoms/unit cell. It lies 41.9 meV/atom below the cubic reference but lacks octahedral cation coordination. The most stable perovskite-like structure has P$\bar{3}$ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first-principles validation for discovering stable, complex materials. Symmetry (geometry) ; Octahedron ; Flattening ; Perovskite (structure) ; Band gap ; Phase (matter) ; Ideal (ethics) ; Electronic band structure Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Po-Yen Chen | Teruyasu Mizoguchi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential Ferroelectricity ; Tetragonal crystal system ; Polarization (electrochemistry) ; Phase boundary ; Hysteresis ; Compressive strength ; Stress (linguistics) Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Dmytro Tymoshchuk | Oleh Yasniy | Iryna Didych | Andrii Stanko | Volodymyr Medvid
Source: Progress in Engineering Science (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of SMA hysteresis behavior by ensemble stacking machine learning model with SHAP-based explanation Hysteresis ; Hyperparameter ; Stability (learning theory) ; Support vector machine ; Shape-memory alloy ; Ensemble learning ; Consistency (knowledge bases) ; Nonlinear system Magnetic Properties and Applications ; Shape Memory Alloy Transformations ; Magnetic properties of thin films
Authors: Chuang Liu | Zelin Yao | Xueqi Ma | Mukun Chen | Luzhi Wang | Jia Wu | Wenbin Hu
Source:  (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Hi-GMAE: Hierarchical Graph Masked Autoencoders Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as single-scale GMAEs. This methodology, while effective in certain contexts, tends to overlook the complex hierarchical structures inherent in many real-world graphs. For instance, molecular graphs exhibit a clear hierarchical organization in the form of the atoms-functional groups-molecules structure. Therefore, the inability of single-scale GMAE models to incorporate these hierarchical relationships often results in an inadequate capture of crucial high-level graph information, leading to a noticeable decline in performance. To address this limitation, we propose Hierarchical Graph Masked AutoEncoders (Hi-GMAE), a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. First, Hi-GMAE constructs a multi-scale graph hierarchy through graph pooling, enabling the exploration of graph structures across different granularity levels. To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to finer scales. Furthermore, we integrate a gradual recovery strategy with the masking process to mitigate the learning challenges posed by completely masked subgraphs. Diverging from the standard graph neural network (GNN) used in GMAE models, Hi-GMAE modifies its encoder and decoder into hierarchical structures. This entails using GNN at the finer scales for detailed local graph analysis and employing a graph transformer at coarser scales to capture global information. Such a design enables Hi-GMAE to effectively capture the multi-level information inherent in complex graph structures. Our experiments on 17 graph datasets, covering two graph learning tasks, consistently demonstrate that Hi-GMAE outperforms 29 state-of-the-art self-supervised competitors in capturing comprehensive graph information. Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science
Authors: Eli Usheunepa Yunana | Jesse Usheujawa Yunana | Mustapha Abdullahi | Ibrahim Mohammed Inuwa
Source: Applied Mechanics and Materials (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Development of Machine Learning Models for Thermoelectric Figure of Merit Predictions of Doped Chalcogenides Machine Learning (ML) approach seeks to open new frontiers in the search for novel thermoelectric materials that convert heat waste into useful electrical energy. Five regression-type ML algorithms Linear Regression, Random Forest, XGBoost, Bagging Regressor, and Gradient Boosting Regressor were employed in this study to forecast the thermoelectric figure of merit (ZT) of doped chalcogenide compounds. Gradient Boosting Regressor achieved the best baseline performance (R 2 = 94.5%, MAE = 0.073, RMSE = 0.128), further improved with hyperparameter tuning to R 2 = 95.8%, MAE = 0.065, and RMSE = 0.112. Compared to the baseline, tuning reduced RMSE by 12.6% and MAE by 10.8%. The optimized model reliably reproduced experimental ZT trends in doped Bi 2 Te 2 Se and Ag 2 Te, validating its predictive capacity. Our findings show that hyperparameter tuning is greatly recommended for high-fidelity predictions in thermoelectrics. Hyperparameter ; Gradient boosting ; Boosting (machine learning) ; Mean squared error ; Figure of merit ; Thermoelectric effect ; Doping Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Haoyu Zhou | Ping Xue | Hao Zhang | Tianfan Fu
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially with high-order representations. While low-bit quantization offers a solution, applying it naively to rotation-sensitive features destroys the SO(3)-equivariant structure, leading to significant errors and violations of conservation laws. To address this issue, in this work, we propose a Geometric-Aware Quantization (GAQ) framework that compresses and accelerates equivariant models while rigorously preserving continuous symmetry in discrete spaces. Our approach introduces three key contributions: (1) a Magnitude-Direction Decoupled Quantization (MDDQ) scheme that separates invariant lengths from equivariant orientations to maintain geometric fidelity; (2) a symmetry-aware training strategy that treats scalar and vector features with distinct quantization schedules; and (3) a robust attention normalization mechanism to stabilize gradients in low-bit regimes. Experiments on the rMD17 benchmark demonstrate that our W4A8 models match the accuracy of FP32 baselines (9.31 meV vs. 23.20 meV) while reducing Local Equivariance Error (LEE) by over 30x compared to naive quantization. On consumer hardware, GAQ achieves 2.39x inference speedup and 4x memory reduction, enabling stable, energy-conserving molecular dynamics simulations for nanosecond timescales. Equivariant map ; Quantization (signal processing) ; Speedup ; Vector quantization ; Inference ; Invariant (physics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Mohammad Bakhsh | Victor Trinquet | Rogério Almeida Gouvêa | Gian-Marco Rignanese | Samuel Poncé
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$ Cs$_2$KInI$_6$ is a promising lead-free halide double perovskite with a calculated direct band gap of 1.24 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm$\bar{3}$m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first principles calculations on 11 of these. The most stable phase has Cmc$2_1$ symmetry with 20 atoms/unit cell. It lies 41.9 meV/atom below the cubic reference but lacks octahedral cation coordination. The most stable perovskite-like structure has P$\bar{3}$ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first-principles validation for discovering stable, complex materials. Symmetry (geometry) ; Octahedron ; Flattening ; Perovskite (structure) ; Band gap ; Phase (matter) ; Ideal (ethics) ; Electronic band structure Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Yaping Tian | Zhiqin Geng | Zhendong Su | Y Liu | Dingfang Zhang | Shoufeng Tang | Pengyan Wu | Xinming Nie
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Trace-level detection of free polycyclic aromatic hydrocarbons based on magnetic driving and deep learning-assisted recognition Detection limit ; Raman spectroscopy ; Pollutant ; Substrate (aquarium) ; Polycyclic aromatic hydrocarbon ; Aquatic ecosystem ; Plasmon ; Spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Oil Spill Detection and Mitigation