jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lingzhi Cao | Yateng Wang | Zhonghao Xia | Jiangang He
Source: ArXiv.org (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Microscopic Origin of the Ultralow Lattice Thermal Conductivity in Vacancy-Ordered Halide Double Perovskites Cs$_2BX_6$ ($B$ = Zr, Pd, Sn, Te, Hf, and Pt; $X$= Cl, Br, and I) Vacancy-ordered halide double perovskites Cs$_2BX_6$ have recently attracted significant attention due to their intrinsically ultralow lattice thermal conductivity ($κ_{\mathrm{L}}$), which is highly desirable for thermal insulation and thermoelectric applications. In this work, we systematically investigate the anharmonic lattice dynamics and thermal transport properties of Cs$_2BX_6$ ($B$ = Zr, Pd, Sn, Te, Hf, and Pt; $X$ = Cl, Br, and I) using state-of-the-art first-principles calculations, based on a unified theory of thermal transport for crystals and glasses. All studied compounds are found to exhibit ultralow $κ_{\mathrm{L}}$ below 1.0~W\,m$^{-1}$\,K$^{-1}$ at room temperature and large derivation from the conventional $T^{-1}$ temperature dependence. Our analysis combining with machine-learning approach show that low sound velocities (1100 -- 1600~m\,s$^{-1}$), which originates from the intrinsically weak chemical bonding, play a crucial role in suppressing heat transport of the most compounds, instead of the strong scattering of rattling phonon modes expected from the large void in the structure. Furthermore, the influence of $B$ and $X$-site elements on phonon dispersion, anharmonicity, and scattering phase space is clarified. Our results provide microscopic insights into the origin of ultralow $κ_{\mathrm{L}}$ in Cs$_2BX_6$ and offer guiding principles for the rational design of halide-based materials with tailored thermal transport properties. Anharmonicity ; Thermal conductivity ; Phonon ; Scattering ; Halide ; Thermal ; Phonon scattering Thermal Expansion and Ionic Conductivity ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Zhen Li | Linfeng Chen | Junjie Wei | Junhua Zhao | Yanhua Cheng | Ning Wei | Meifang Zhu
Source: Advanced Fiber Materials (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Programmable Woven Graphene Nanoribbons for Advanced Thermal Rectification Devices Rectification ; Thermal ; Graphene nanoribbons ; Thermal management of electronic devices and systems ; Efficient energy use ; Dissipation ; Coupling (piping) ; Thermal conduction ; Thermal conductivity Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Carbon Nanotubes in Composites
Authors: Mohammad Mostafanejad | Paul Saxe | T. Daniel Crawford
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

BERTology of Molecular Property Prediction Chemical language models (CLMs) have emerged as promising competitors to popular classical machine learning models for molecular property prediction (MPP) tasks. However, an increasing number of studies have reported inconsistent and contradictory results for the performance of CLMs across various MPP benchmark tasks. In this study, we conduct and analyze hundreds of meticulously controlled experiments to systematically investigate the effects of various factors, such as dataset size, model size, and standardization, on the pre-training and fine-tuning performance of CLMs for MPP. In the absence of well-established scaling laws for encoder-only masked language models, our aim is to provide comprehensive numerical evidence and a deeper understanding of the underlying mechanisms affecting the performance of CLMs for MPP tasks, some of which appear to be entirely overlooked in the literature. Property (philosophy) ; Benchmark (surveying) ; Competitor analysis ; Scaling law ; Scaling ; Series (stratigraphy) ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Vishnu Priya Kancharla | John Sushma Nannepaga | Mannur Ismail Shaik | Mhd Ikhwanuddin Abdullah | Norizah Mhd Sarbon
Source: Research Square (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Targeted Therapy for Glioblastoma Multiforme using Biofunctionalized Iron Oxide Nanoparticles and Deep Learning-Enhanced Method Glioblastoma ; Targeted drug delivery ; Biocompatible material ; Iron oxide nanoparticles ; Nanoparticle ; Drug delivery ; Targeted therapy ; Cytotoxicity Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications
Authors: Austin Rodriguez | Justin Smith | Sakib Matin | Nicholas Lubbers | Kipton Barros | Jose L. Mendoza-Cortes
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials The Hessian matrix (second derivatives) encodes far richer local curvature of the potential energy surface than energies and forces alone. However, training machine-learning interatomic potentials (MLIPs) with full Hessians is often impractical because explicitly forming and storing Hessian matrices scales quadratically in cost and memory. We introduce Projected Hessian Learning (PHL), a scalable second-order training framework that injects curvature information using only Hessian-vector products (HVPs). Rather than constructing the Hessian, PHL projects curvature along stochastic probe directions and uses an unbiased stochastic trace-based loss with favorable system-size scaling, enabling curvature-informed training without quadratic memory growth. We benchmark PHL on a chemically diverse dataset of reactants, products, transition states, intrinsic reaction coordinates, and normal-mode sampled geometries computed at omegaB97XD/6-31G(d). We compare energy-force training (E-F), two HVP-based schemes (E-F-HVP with one-hot or randomized probes), and full energy-force-Hessian training (E-F-H). With randomized probes per minibatch, both HVP schemes match full-Hessian training in energy, force, and Hessian accuracy while delivering &gt;24x epoch speedups for the small molecular systems studied. In a fixed-probe regime with one HVP per molecule, randomized projections consistently outperform one-column probing, especially for far-from-equilibrium geometries. Overall, PHL replaces explicit Hessian supervision with force-complexity curvature training, retaining most second-order accuracy gains while scaling to larger, more complex molecular systems. Hessian matrix ; Curvature ; Quadratic equation ; Benchmark (surveying) ; Scaling ; Scalability ; Quadratic growth Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Lingzhi Cao | Yateng Wang | Zhonghao Xia | Jiangang He
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Microscopic Origin of the Ultralow Lattice Thermal Conductivity in Vacancy-Ordered Halide Double Perovskites Cs$_2BX_6$ ($B$ = Zr, Pd, Sn, Te, Hf, and Pt; $X$= Cl, Br, and I) Vacancy-ordered halide double perovskites Cs$_2BX_6$ have recently attracted significant attention due to their intrinsically ultralow lattice thermal conductivity ($κ_{\mathrm{L}}$), which is highly desirable for thermal insulation and thermoelectric applications. In this work, we systematically investigate the anharmonic lattice dynamics and thermal transport properties of Cs$_2BX_6$ ($B$ = Zr, Pd, Sn, Te, Hf, and Pt; $X$ = Cl, Br, and I) using state-of-the-art first-principles calculations, based on a unified theory of thermal transport for crystals and glasses. All studied compounds are found to exhibit ultralow $κ_{\mathrm{L}}$ below 1.0~W\,m$^{-1}$\,K$^{-1}$ at room temperature and large derivation from the conventional $T^{-1}$ temperature dependence. Our analysis combining with machine-learning approach show that low sound velocities (1100 -- 1600~m\,s$^{-1}$), which originates from the intrinsically weak chemical bonding, play a crucial role in suppressing heat transport of the most compounds, instead of the strong scattering of rattling phonon modes expected from the large void in the structure. Furthermore, the influence of $B$ and $X$-site elements on phonon dispersion, anharmonicity, and scattering phase space is clarified. Our results provide microscopic insights into the origin of ultralow $κ_{\mathrm{L}}$ in Cs$_2BX_6$ and offer guiding principles for the rational design of halide-based materials with tailored thermal transport properties. Anharmonicity ; Thermal conductivity ; Phonon ; Scattering ; Halide ; Thermal ; Phonon scattering Thermal Expansion and Ionic Conductivity ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Ashmita Das | Debaditya Gupta | Ramagopal V.S. Uppaluri | Sudip K Mitra
Source: Biomass and Bioenergy (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Application of RSM-DF and RSM-ANN-TLBO optimization techniques for enhancing the performance of nanozyme derived from spent mushroom substrate biochar Biochar ; Pyrolysis ; Catalysis ; Response surface methodology ; Peroxidase ; Point of delivery Advanced Nanomaterials in Catalysis ; Enzyme-mediated dye degradation ; Nanoplatforms for cancer theranostics
Authors: A. Ulugöl | J. I. Bückmann | R. Yang | L. D. Hoitink | A. van Blaaderen | Frank Smallenburg | Laura Filion
Source: The Journal of Chemical Physics (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Comparing dimensionality reduction methods for local structural identification in colloidal systems Quantifying local structures in self-assembled systems is a central challenge in soft matter and materials science. When no a priori knowledge of the relevant structures is available, traditional order parameters often fall short. Unsupervised machine learning provides a convenient route to autonomously uncover structural motifs directly from particle configurations. In this work, we systematically compare three popular dimensionality reduction techniques, principal component analysis, autoencoders, and uniform manifold approximation and projection (UMAP), for classifying local environments in self-assembled systems. We first apply these methods to fluid and crystal configurations of hard and charged spheres. Thereafter, we apply it to an icosahedral arrangement of spheres that self-assembled in spherical confinement, both from simulations and from experiments. We demonstrate that UMAP consistently outperforms the other methods in capturing complex structural features, offering a robust tool for structural classification without supervision. Dimensionality reduction ; A priori and a posteriori ; Principal component analysis ; Projection (relational algebra) ; Curse of dimensionality ; Nonlinear dimensionality reduction ; Icosahedral symmetry ; Reduction (mathematics) ; Pattern recognition (psychology) Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Hanmei Chen | Pengfei Yu | Qiulin Bi | Jialiang Liu | Junting Li | Mingxu Xia | Xiujun Han
Source: Microstructures (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Research progress on liquid-solid transition under synchrotron radiation X-ray and simulation The structures of metallic melts are of utmost significance for understanding liquid properties, atomic dynamics, and solidification behaviors, including the formation of solidified microstructures. In recent years, with the continuous advancement of experimental techniques and numerical simulation methods, researchers have obtained deeper insights into the microscopic details of the liquid structure, the structure evolution, and the correlations between the liquid structure and the solidified microstructure. This article reviews the main experimental techniques and simulation methods employed in the study of metallic melt structures, as well as the pioneering findings in the field. High-energy synchrotron X-ray diffraction and in situ X-ray imaging techniques and their applications in elucidating the liquid structure and its evolution during solidification are introduced. Special attention is given to the development of synchrotron equipment. Simulation methods for analyzing melt structures include classical molecular dynamics (MD), ab initio molecular dynamics (AIMD), reversed Monte Carlo (RMC), and machine learning (ML), all of which have been applied to predict the atomic structures of metallic melts. The most recent progress in machine learning potentials or force fields is also introduced. The article also discusses future research directions, including the integration of high-resolution imaging with high-energy X-ray diffraction techniques, the application of artificial intelligence-assisted simulations to reduce computational costs, and the investigation of external factors such as pressure and cooling rates on solidification behavior. By combining advanced experimental and computational approaches, research on metallic melt structures is expected to move toward a more comprehensive and in-depth understanding, opening new opportunities for breakthroughs in metallurgy and materials science. Synchrotron ; Synchrotron radiation ; Molecular dynamics ; Diffraction ; Liquid metal ; Ab initio ; Monte Carlo method Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Bahareh Kheilnezhad | Maziar Montazerian | Seong H. Kim | John C. Mauro
Source: International Journal of Ceramic Engineering & Science (article, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Viscosity Measurement Techniques for High‐Temperature Oxide Glass‐Forming Systems ABSTRACT Viscosity is a fundamental physical property that governs the flow and processing behavior of glass‐forming liquids. During glass manufacturing processes, it is thus important to know how rheological properties are influenced by variables such as temperature, chemical composition, and the presence of any inclusions within the melt. There is extensive literature describing theoretical models for calculating the viscosity of oxide glass‐forming systems; resources dedicated to experimental methodologies for measuring oxide glass viscosity are comparatively scarce. This review therefore focuses on direct viscometry techniques as well as indirect approaches for measuring viscosity including differential scanning calorimetry (DSC) and dilatometry combined with the Mauro–Yue–Ellison–Gupta–Allan (MYEGA) model. In addition, we highlight emerging machine‐learning approaches, which offer complementary insights into glass viscosity, particularly in complex multicomponent systems. Such machine learning‐based methods require large and high‐quality datasets for training and validation, underscoring the vital importance of experimental measurements in establishing reliable viscosity values. Overall, the primary goal of this review is to fill a gap in the literature regarding experimental methodologies for viscosity measurement of oxide glasses, explaining their underlying principles, highlighting the challenges faced by researchers, and emphasizing the continued necessity of experimental viscosity measurements alongside theoretical and computational approaches. Viscometer ; Viscosity ; Rheology ; Oxide ; Differential scanning calorimetry ; Experimental data ; Measure (data warehouse) ; Flow properties Glass properties and applications ; Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys
Authors: K. Praveen Kumar Reddy | Suresh Gudala | Rajasekhar Thota | Lava Kumar P | Dhiraj Raj | Ramesh Ganugapenta
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-07)
Field: Materials Science — Ceramics and Composites

Predictive modelling of compressive strength in silicon nitride-reinforced aluminium composites using supervised machine learning: A comparative study of random forest and artificial neural networks The integration of machine learning and material science is now changing the way property predictions are made, accelerating the process of discovery and optimization of advanced materials. For this research, we employed a supervised ML to estimate the compressive strength of AMCs reinforced by silicon nitride. To sharpen predictive accuracy, hyperparameter tuning using GridSearch CV was performed. We employed two different algorithms-RF and ANN-to unravel the complex links between the inputs, such as compaction pressure, reinforcement content, sintering temperature, and sintering time with the target strength. Regularization was used to guard against overfitting, and training versus testing performance was compared rigorously. The results indicated that the RF outperformed the ANN model, giving an R² of 0.88, while the ANN reached an R² of only 0.80. These results suggested that the RF model had tighter residuals and higher accuracy since it emphasizes the parameters that drive most of the variance in the dataset. This work illustrates the potential of ML, especially RF, in reliably predicting properties of materials and informing the design of high-performance composites. Random forest ; Hyperparameter ; Artificial neural network ; Compressive strength ; Compaction ; Regularization (linguistics) ; Support vector machine ; Aluminium ; Trimming Advanced ceramic materials synthesis ; Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science
Authors: Carlos Cobas
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Spectroscopy

Physics-Informed Neural Networks for Automated Extraction of Scalar Coupling Constants from Strongly Coupled NMR Spectra: ABC and ABCD Spin Systems Coupling constant ; Artificial neural network ; Curse of dimensionality ; Scalar (mathematics) ; Coupling (piping) ; Spectral line ; Algebraic number ; Inverse Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Molecular spectroscopy and chirality
Authors: Sergey V. Doronin | Yury A. Budkov
Source: Current Opinion in Electrochemistry (article, 2026-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Electric Double Layer: From Classical Stern-like Models to Advanced Continuum Theories Polarizability ; Density functional theory ; Ab initio ; Electronic structure ; Charge (physics) ; Ion ; Dielectric ; Quantum ; Charge density ; Capacitance Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Hannes Kneiding | David Balcells
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

∆-Machine Learning for the Prediction of Metal Complex Properties ∆-machine learning was applied to predict electronic properties of TMCs, improving accuracy and chemical transferability compared to traditional models. Additionally, it was demonstrated how minimizing the cost of the low-fidelity method substantially improves computational efficiency, while only slightly affecting predictive performance. Transferability ; Key (lock) ; Mathematical model ; Predictive modelling ; Statistical learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Memory and Neural Computing
Authors: Huiya Zhang | Xuelian Zou | Zhiqiang Yang | Jiamin Zhao | Qiguan Luo | Wenxia Zhai | Ziyu Dang | Xiaochen Pan | Wenjing Zhang | Shuangfei Wang | Shuangxi Nie | Zhiwei Wang
Source: Advanced Functional Materials (article, 2026-01-07)
Field: Materials Science — Polymers and Plastics

Mussel‐Inspired Robust Self‐Healing Triboelectric Materials Enabled by Supramolecular Interactions and Nanoconfinement ABSTRACT Biomass‐based self‐healing triboelectric materials are recognized for meeting the sustainability and reliability demands of flexible electronic sensors, playing an essential role in enhancing device durability and operational longevity. However, existing self‐healing biomass systems still face challenges in achieving synergistic enhancement of both mechanical properties and self‐healing efficiency. Inspired by the strong adhesion of mussel foot proteins, a robust, room‐temperature self‐healing, and recyclable biomass‐based triboelectric material has been developed through the synergistic coordination of supramolecular interactions (such as Fe 3+ ─catechol coordination bonds, hydrogen bonds, and π─π stacking) and nanoconfinement. The constructed nanonetwork was found to facilitate enhanced energy dissipation and accelerated dynamic bonds reorganization, resulting in a combination of superior mechanical properties and efficient self‐healing. The obtained material demonstrated a tensile strength of 30.95 MPa, a toughness of 100.77 MJ m −3 , and a remarkable self‐healing efficiency of 96.22%. A triboelectric tactile sensor fabricated from this material exhibited a rapid response of 14 ms, and when integrated with deep learning algorithms, achieved highly accurate identification of material types and surface textures. This work offers a promising approach for the development of bionic perception systems in complex environments. Triboelectric effect ; Toughness ; Supramolecular polymers ; Durability ; Supramolecular chemistry ; Ultimate tensile strength ; Polymer ; Reliability (semiconductor) ; Artificial muscle Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Hydrogels: synthesis, properties, applications
Authors: Connie M. Wang | Roberta M. Sabino | Aditya Garg | Ahmed E. Salih | Loza F. Tadesse | Elazer R. Edelman
Source: ACS Applied Materials & Interfaces (article, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoengineered Surface-Enhanced Raman Spectroscopy Substrates for Probing Tissue–Material Interactions Innovation in biomaterials has brought both breakthroughs and challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue-implant interactions. Implant biology remains difficult to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nanocolumns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue-implant interface, providing the ability to sense tissue-material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs (360 ± 40 nm in length and 50 ± 16 nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated a biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via <i>in vitro</i> cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be 1.8 × 10<sup>5</sup>, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated by using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning techniques, promising multiplexed characterization of tissue-material interactions and subsequently enabling tissue state determination and noninvasive monitoring of implant-tissue interaction. Raman spectroscopy ; Biocompatibility ; Characterization (materials science) ; Biomolecule ; Substrate (aquarium) ; Surface-enhanced Raman spectroscopy ; X-ray photoelectron spectroscopy ; Multiplexing ; Microscopy ; Chemical imaging Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser Material Processing Techniques