jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Masato Nakaya | Shun Yamamoto | Satoshi Ogawa | Toshiaki Nishii | Jun Onoe
Source: Scientific Reports (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Novel strategy for boosting thermoelectric performance of organic materials with low electrical conductivity To realize a wearable power generator, organic thermoelectric materials are desired to exhibit a dimensionless figure-of-merit [zT = α2σT/(κe + κl)] of more than unity, where T, α, and σ denote the temperature, thermopower, and electrical conductivity, whereas κe and κl are the thermal conductivities mediated by carriers and phonons, respectively. Although conventional materials exhibit a maximum power factor (PFmax = α2σ) in the high-σ region of more than 10 Scm− 1, zT is not necessarily maximized at the σ giving PFmax because of a marked increase in κe in the high-σ region, which has been a barrier for boosting the zT of organic materials up to a practical level. Here, we report that an exceedingly large PFmax of 1.1 × 10− 3 Wm−1K− 2 was realized for the composite film consisting of fullerene molecules and molybdenum trioxide nanoclusters by keeping the giant Seebeck effect at a low σ of less than 10− 2 Scm− 1. Since κe is negligibly small in the low-σ region, zT of 0.81 is expected to be achieved at room temperature, which is the highest value among organic materials to the best of our knowledge. Boosting (machine learning) ; Electrical resistivity and conductivity ; Thermoelectric effect ; Thermoelectric materials ; Conductivity Advanced Thermoelectric Materials and Devices ; Organic and Molecular Conductors Research ; Thermal properties of materials
Authors: Melissa T. Manetsch | David W. Kastner | Y. Román-Leshkov | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis We introduce pyEF, a software package for computing molecular electric fields, electrostatic interaction energies, and electrostatic potentials from quantum mechanical (QM) atom-centered multipole expansions with atom-wise decomposable contributions. We demonstrate the computational efficiency and accuracy of this QM-derived electric field evaluation tool through several tests. To assess the influence of the underlying QM method and charge partitioning scheme on these electrostatic quantities, we analyze over 250 configurations of an acetone solute molecule in five solvents of variable polarity. We find that electric field calculations are highly sensitive to the choice of charge partitioning method. Even among real-space charge schemes, acetone Stark tuning rates differ by up to a factor of 2. Benchmarking computed solvent dipole moments against experimental bulk values, we conclude that the CM5, ADCH, and Hirshfeld-I charge schemes most reliably capture solvent electrostatics and therefore provide a more faithful foundation for computing electric fields. When constructed from these real-space charges, electric fields are nearly insensitive to basis set size and monotonically increase in magnitude with higher Fock exchange. We also demonstrate efficient convergence of QM electrostatics when more distant molecules are represented solely by MM point charges, reducing computational overhead. Leveraging these findings, we demonstrate the use of pyEF to deduce environmental effects on a transition metal complex from a Ga<sub>4</sub>L<sub>6</sub><sup>12-</sup> nanocage and quantify the dominant role of organic linkers in orchestrating electrostatic preorganization. Electrostatics ; Electric field ; Dipole ; Multipole expansion ; Static electricity ; Charge (physics) ; Python (programming language) ; Electric charge ; Molecular dynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Synthesis and Properties of Aromatic Compounds
Authors: Shelby Sutton Fields | Christopher David White | Keith E. Knipling | Steven Bennett
Source: ArXiv.org (article, 2026-01-22)
Field: Chemistry — Inorganic Chemistry

Facile Optimization of Combinatorial Sputtering Processes with Arbitrary Numbers of Components for Targeted Compositions Combinatorial sputtering is a physical vapor deposition method that enables the high-throughput synthesis of compositionally varied thin films. Using this technique, the effects of stoichiometry on specific properties of alloy thin films with analog composition gradients can be mapped using high-throughput characterization. To obtain specific stoichiometries, such as those desired for an equiatomic, intermetallic, or doped compounds, the sputter power of each target must be simultaneously tuned to optimize the deposition rate of each component. This optimization problem increases in complexity with the number of components, which commonly leads to iterative guess-and-check processing and can limit the intrinsic high-throughput advantages of this synthesis method. To circumvent this challenge, this work introduces a composition optimization procedure that enables the facile synthesis of sputtered combinatorial films with targeted compositions. This procedure leverages the expeditious mapping of composition using wavelength dispersive x-ray fluorescence and is capable of optimizing processing for an arbitrary number of components. As a demonstration, this method is leveraged to sputter a combinatorial Cr$_{v}$Fe$_{w}$Mo$_{x}$Nb$_{y}$Ta$_{z}$ film with an equiatomic composition near the wafer center. Sputtering ; Combinatorial synthesis ; Combinatorial method ; Deposition (geology) ; Wafer ; Sputter deposition ; Composition (language) ; Stoichiometry ; Thin film Inorganic Chemistry and Materials ; Magnetic properties of thin films ; Machine Learning in Materials Science
Authors: Simon Angstenberger | Emilio Corcione | Tobias Steinle | Cristina Tarín | Harald Gießen
Source: Sensors (article, 2026-01-28)
Field: Chemistry — Spectroscopy

Vibrational Fingerprinting of Gas Mixtures Using COCO-QEPAS Detection and simultaneous monitoring of multiple trace gases is vital in scientific and industrial processes. Here, we use coherent control in quartz-enhanced photoacoustic spectroscopy (COCO-QEPAS) with an in situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at very low concentrations, without prior knowledge of gas composition. We validate this on various mixtures, including CH<sub>4</sub>/C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>/NO<sub>2</sub>/NH<sub>3</sub>. To this end, we demonstrate real-time analysis of mixtures containing up to four trace gases at ppm-level, monitoring changes in seconds using linear regression. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand fingerprinting to 10 ppm with a detection limit of 180 ppb CH<sub>4</sub>, and apply empirical mode decomposition as an adaptive, data-driven filtering method to recover characteristic spectral features at the noise floor. For quantitative analysis in the ppb regime, we employ principal component regression as a calibration model that exploits correlations across the full spectrum. Consequently, our method offers significant potential for sensing applications where speed, accuracy, and simplicity are critical. Calibration ; Trace gas ; Chemometrics ; Principal component analysis ; TRACE (psycholinguistics) ; Detection limit ; Noise (video) Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Advanced Chemical Sensor Technologies
Authors: Yuta Tsuchiya | Yuhei Hirono | Rei Sonobe
Source: AgriEngineering (article, 2026-01-19)
Field: Chemistry — Analytical Chemistry

Comparative Evaluation of Deep Learning Architectures for Non-Destructive Estimation of Carotenoid Content from Visible–Near-Infrared (400–850 nm) Spectral Reflectance Data This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible–near-infrared (VIS–NIR) spectral reflectance data (400–850 nm) acquired non-destructively from tea leaves. Model performance was evaluated using 10-fold cross-validation and analyzed through the mean SHapley Additive exPlanations values to identify key spectral features. The ViT model achieved the highest predictive accuracy (coefficient of determination [R2] = 0.81, root mean square error [RMSE] = 1.04, ratio of performance to deviation [RPD] = 2.32), followed by 1D-CNN (R2 = 0.75, RMSE = 1.21, RPD = 1.99), whereas SSL showed substantially lower predictive performance (R2 = 0.30, RMSE = 2.01, RPD = 1.20). Feature importance analysis revealed that ViT focused strongly on the red-edge region around 720 nm, which corresponds to spectral features associated with carotenoids and chlorophyll. The 1D-CNN relied mainly on blue (450–480 nm) and red (670–700 nm) regions, while SSL exhibited a broadly distributed importance pattern across wavelengths. These results indicate that ViT’s self-attention mechanism captures long-range spectral dependencies more effectively than conventional convolutional or self-supervised models. Overall, the study demonstrates that transformer-based architectures provide a powerful and interpretable framework for non-destructive estimation of carotenoid content from VIS–NIR reflectance spectroscopy. Mean squared error ; Reflectivity ; Carotenoid ; Convolutional neural network ; Deep learning ; Pattern recognition (psychology) ; Content (measure theory) ; Hyperspectral imaging Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Optical Imaging and Spectroscopy Techniques
Authors: Yuta Tsuchiya | Yuhei Hirono | Rei Sonobe
Source: Institutional Repositories DataBase (IRDB) (article, 2026-01-19)
Field: Chemistry — Analytical Chemistry

Comparative Evaluation of Deep Learning Architectures for Non-Destructive Estimation of Carotenoid Content from Visible–Near-Infrared (400–850 nm) Spectral Reflectance Data Reflectivity ; Deep learning ; Hyperspectral imaging ; Content (measure theory) ; Pattern recognition (psychology) ; Carotenoid Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Geochemistry and Geologic Mapping
Authors: Joshua Steier
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Information Routing in Atomistic Foundation Models: How Equivariance Creates Linearly Disentangled Representations What determines whether a molecular property prediction model organizes its representations so that geometric and compositional information can be cleanly separated? We introduce Compositional Probe Decomposition (CPD), which linearly projects out composition signal and measures how much geometric information remains accessible to a Ridge probe. We validate CPD with four independent checks, including a structural isomer benchmark where compositional projections score at chance while geometric residuals reach 94.6\% pairwise classification accuracy. Across ten models from five architectural families on QM9, we find a \emph{linear accessibility gradient}: models differ by $6.6\times$ in geometric information accessible after composition removal ($R^2_{\mathrm{geom}}$ from 0.081 to 0.533 for HOMO-LUMO gap). Three factors explain this gradient. Task alignment dominates: models trained on HOMO-LUMO gap ($R^2_{\mathrm{geom}}$ 0.44--0.53) outscore energy-trained models by $\sim$0.25 $R^2$ regardless of architecture. Within-architecture ablations on two independent architectures confirm this: PaiNN drops from 0.53 to 0.31 when retrained on energy, and MACE drops from 0.44 to 0.08. Data diversity partially compensates for misaligned objectives, with MACE pretrained on MPTraj (0.36) outperforming QM9-only energy models. Inside MACE's representations, information routes by symmetry type: $L{=}1$ (vector) channels preferentially encode dipole moment ($R^2 = 0.59$ vs.\ 0.38 in $L{=}0$), while $L{=}0$ (scalar) channels encode HOMO-LUMO gap ($R^2 = 0.76$ vs.\ 0.34 in $L{=}1$). This pattern is absent in ViSNet. We also show that nonlinear probes produce misleading results on residualized representations, recovering $R^2 = 0.68$--$0.95$ on a purely compositional target, and recommend linear probes for this setting. Equivariant map ; Representation (politics) ; Tree (set theory) ; Projection (relational algebra) ; ENCODE ; Topology (electrical circuits) ; Tensor (intrinsic definition) ; Routing (electronic design automation) ; Scalability Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum many-body systems
Authors: Avinash Mahapatra | Sudipta Majumder | HL Pradeepa | Parul Gupta | Shrikrishna Bhagwat | Shivprasad Patil | Atikur Rahman
Source: Small (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Large‐Area Bi <sub>2</sub> O <sub>2</sub> Se Nanosheets With Enhanced Optoelectronic Performance for Flexible Electronics The rapid progress of flexible electronics demands materials that simultaneously offer outstanding electrical and optoelectronic performance with mechanical durability. Bismuth oxyselenide (Bi<sub>2</sub>O<sub>2</sub>Se) has emerged as a promising candidate due to its high carrier mobility and excellent optoelectronic properties. However, realizing large-area, high-quality Bi<sub>2</sub>O<sub>2</sub>Se nanosheets suitable for device fabrication remains a challenge, as conventional low-pressure chemical vapor deposition (LPCVD) offers limited scalability and narrow growth conditions. This study presents the synthesis of millimeter-scale Bi<sub>2</sub>O<sub>2</sub>Se nanosheets using atmospheric pressure chemical vapor deposition (APCVD), achieving domain sizes up to 0.4 mm. Systematic investigation of the growth parameters and mechanisms accompanied by COMSOL simulations to study the adatom diffusion on the substrate surface reveals the key factors that enable such large-domain formation. The resulting nanosheets exhibit excellent electronic transport properties, with average carrier mobilities of 110 cm<sup>2</sup>V<sup>-1</sup>s<sup>-1</sup> at room temperature and more than 3700 cm<sup>2</sup>V<sup>-1</sup>s<sup>-1</sup> at 2.3 K. Devices fabricated on flexible substrates maintain stable performance under repeated mechanical bending, underscoring their robustness and durability. These results establish APCVD-grown Bi<sub>2</sub>O<sub>2</sub>Se as a scalable platform for next-generation flexible electronic and optoelectronic technologies. Fabrication ; Electronics ; Chemical vapor deposition ; Flexible electronics ; Substrate (aquarium) ; Scalability ; Electron mobility ; Bismuth 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced Photocatalysis Techniques
Authors: Keerti M. Naik | Nobutaka Maeda | Yuji Okuyama | Yoshihiro Yamazaki | Junji Hyodo
Source: ChemRxiv (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Experimental Identification of Oxygen Dissociation Bottleneck in Proton-Conducting Ceramic Fuel Cells Proton-conducting ceramic fuel cells (PCFCs) enable efficient electrochemical energy conversion, yet further performance improvements are limited by sluggish cathodic oxygen reduction kinetics and the lack of direct identification of the rate-determining step under operating conditions. Here, we experimentally identified that oxygen dissociation from superoxide intermediates (O22−) limits cathodic polarization in PCFC devices with a PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) cathode. Modulation-excitation infrared spectroscopy directly observes that superoxide is the major intermediate for the oxygen reduction reaction. We also isolate the intrinsic cathodic kinetics of PBSCF using hydrogen–deuterium isotope effects combined with impedance spectroscopy on working PCFC devices, which achieves peak power densities of 517-1077 mWcm−2 at 550-650 °C under a fuel-oxidant combination of H2-air. The cathodic polarization resistance exhibits an oxygen partial pressure dependence with characteristic exponents above 0.5 and shows no measurable dependence on water vapor pressure. This kinetic signature is uniquely consistent with oxygen dissociation via O⁻ formation from O22− intermediates, assisted by oxygen vacancies at the cathode–gas interface. These results provide experimental evidence that oxygen dissociation constitutes the dominant kinetic bottleneck in the PCFC device, establishing a clear design principle: strong electron-donating character coupled with a high density of surface oxygen vacancies is essential to accelerate cathodic kinetics. Oxygen ; Dissociation (chemistry) ; Dielectric spectroscopy ; Cathodic protection ; Electrochemistry ; Analytical Chemistry (journal) ; Kinetics ; Partial pressure Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Shelby Sutton Fields | Christopher David White | Keith E. Knipling | Steven P. Bennett
Source: Review of Scientific Instruments (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Facile optimization of combinatorial sputtering processes with arbitrary numbers of components for targeted compositions Combinatorial sputtering is a physical vapor deposition method that enables the high-throughput synthesis of compositionally varied thin films. Using this technique, the effects of stoichiometry on specific properties of alloy thin films with analog composition gradients can be mapped using high-throughput characterization. To obtain specific stoichiometries, such as those desired for an equiatomic, intermetallic, or doped compounds, the sputter power of each target must be simultaneously tuned to optimize the deposition rate of each component. This optimization problem increases in complexity with the number of components, which commonly leads to iterative guess-and-check processing and can limit the intrinsic high-throughput advantages of this synthesis method. To circumvent this challenge, this work introduces a composition optimization procedure that enables the facile synthesis of sputtered combinatorial films with targeted compositions. This procedure leverages the expeditious mapping of composition using wavelength dispersive x-ray fluorescence and is capable of optimizing processing for an arbitrary number of components. As a demonstration, this method is leveraged to sputter a combinatorial CrvFewMoxNbyTaz film with an equiatomic composition near the wafer center. Sputtering ; Combinatorial method ; Combinatorial synthesis ; Wafer ; Sputter deposition ; Deposition (geology) ; Thin film ; Composition (language) ; Stoichiometry Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques
Authors: Tynan J. Perez | Rafael Gomez-Bombarelli
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Self-Conditioned Denoising for Atomistic Representation Learning The success of large-scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for the physical sciences. However, pretraining strategies using atomistic data remain underexplored. To date, large-scale supervised pretraining on DFT force-energy labels has provided the strongest performance gains to downstream property prediction, out-performing existing methods of self-supervised learning (SSL) which remain limited to ground-state geometries, and/or single domains of atomistic data. We address these shortcomings with Self-Conditioned Denoising (SCD), a backbone-agnostic reconstruction objective that utilizes self-embeddings for conditional denoising across any domain of atomistic data, including small molecules, proteins, periodic materials, and 'non-equilibrium' geometries. When controlled for backbone architecture and pretraining dataset, SCD significantly outperforms previous SSL methods on downstream benchmarks and matches or exceeds the performance of supervised force-energy pretraining. We show that a small, fast GNN pretrained by SCD can achieve competitive or superior performance to larger models pretrained on significantly larger labeled or unlabeled datasets, across tasks in multiple domains. Our code is available at: https://github.com/TyJPerez/SelfConditionedDenoisingAtoms Code (set theory) ; Representation (politics) ; Noise reduction ; Property (philosophy) ; Domain (mathematical analysis) ; Labeled data ; Supervised learning ; Pattern recognition (psychology) ; Feature learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum many-body systems
Authors: Katarzyna Suchorowiec | Justyna Kasznik | Anastasiia Stepura | Mária Omastová | K. Pielichowska
Source: Molecules (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Polysaccharide Hydrogels Doped with MXenes for Possible Biomedical Applications MXenes, a new family of two-dimensional transition-metal carbides and nitrides, have attracted significant interest in biomedicine because of their tunable surface groups and multifunctional properties. Hydrogels, with their three-dimensional polymeric networks rich in water, provide excellent biocompatibility and structural similarity to those of biological tissues. Although synthetic polymer-based MXene hydrogels are well studied, polysaccharide-based systems remain underexplored despite their biodegradability and biomedical relevance. In this work, MXene nanosheets were incorporated into a sodium alginate (ALG)-gellan gum (GG) polymeric blend to develop polysaccharide/MXene hydrogels. Two dehydration approaches, conventional drying and freeze-drying were used to evaluate their influence on the characteristics of the composite, including microstructure, surface roughness, compressive behavior, water states, and thermal stability. Conventionally dried polysaccharide/MXene nanocomposites with 1.0% wt. MXene have reduced the swelling ratio by ~60% and the volume change by 40% compared to polysaccharide blend. Freeze-dried polysaccharide/MXene nanocomposite hydrogels developed a porous, interconnected network, making them promising for applications requiring high surface area, such as adsorption and tissue engineering. In contrast, conventionally dried samples formed compact, smooth structures with improved barrier and mechanical performance. These results demonstrate that the dehydration strategy strongly governs the polymer network architecture, water states, and material functionality, offering pathways to tailor hydrogel modified with MXene for specific biomedical applications. MXenes ; Self-healing hydrogels ; Biocompatibility ; Nanocomposite ; Polymer ; Swelling ; Adsorption ; Dehydration ; Biodegradation MXene and MAX Phase Materials ; Hydrogels: synthesis, properties, applications ; Graphene and Nanomaterials Applications