jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tarun Kumar Bharati
Source: International Journal of Research in Human Resource Management (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Electronic band alignment and charge storage capacity in organic semiconductor-MXene (Perylene-Ti₃C₂Tx) heterostructures through a theoretical study Organic semiconductor-MXene heterostructures have emerged as versatile materials for energy storage and optoelectronic applications because of their tunable band alignment, high surface area, and superior charge transport properties. Among these, perylene-based organic semiconductors integrated with titanium carbide MXenes (Ti?C?Tx, where Tx represents surface groups -O, -OH, and -F) show particular promise, owing to their stable interfaces that promote efficient charge separation and storage. This work presents a theoretical study of the electronic band alignment and charge storage capacity of perylene-Ti?C?Tx heterostructures using density functional theory (DFT) and electronic structure analysis. The results reveal a type-II band alignment, with the perylene conduction band minimum aligned favorably with the MXene Fermi level, enabling efficient electron transfer. Surface functionalization strongly affects band bending, interface dipole formation, and electronic coupling. Oxygen-terminated Ti?C?Tx provides the most favorable conditions for charge transfer, whereas fluorine terminations suppress conductivity through electron localization. Projected density of states (PDOS) analysis confirms strong hybridization between perylene ? orbitals and Ti-d states, enhancing interfacial communication. Charge density difference maps further show significant redistribution, indicating improved charge storage relative to pristine perylene or Ti?C?Tx. Capacitance values estimated from the density of states near the Fermi level suggest that perylene-Ti?C?Tx heterostructures could function as high-performance electrodes for supercapacitors and hybrid storage systems. Overall, the findings emphasize the importance of surface terminations and molecular orientation in designing optimized organic semiconductor-MXene heterostructures for future technologies Heterojunction ; Fermi level ; Density functional theory ; Work function ; Organic semiconductor ; MXenes ; Wide-bandgap semiconductor ; Band gap ; Dipole MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Xing‐Liang Peng | Weiqing Zhou | Daye Zheng | Zuxin Jin | Yike Huang | Denghui Lu | Zhuoyuan Li | Tongqi Wen | Mohan Chen | Xingliang Peng
Source: Chinese Science Bulletin (Chinese Version) (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Open-source density functional theory software ABACUS: promoting the development of large atomic-scale machine learning pre-trained models 基于机器学习的原子间势函数模型可以兼具量子力学的精度和传统经验力场的效率, 近年来已在分子动力学等领域得到了广泛的应用. 随着机器学习势函数方法在应用领域的拓展和相关数据的持续积累, 正如语言大模型的出现一样, 构建可覆盖元素周期表上大部分元素的通用大原子模型(large atomic model, LAM)也成为可能, 并且正逐渐发展成为理解和预测材料性质的全新原子尺度模拟工具. 开放大原子模型项目(The OpenLAM project)旨在构建适用于复杂材料体系的高精度和高效率预训练大模型, 并且通过进一步的下游微调和知识蒸馏来获得高效准确的原子间势函数模型, 而其中的关键步骤是需要第一性原理软件计算出海量的训练数据. 本文介绍开源密度泛函理论软件原子算筹ABACUS对OpenLAM项目的支撑. 该软件是一款国内自主研发的第一性原理材料计算软件, 支持平面波和数值原子轨道双基组, 构建了方便用户使用的赝势和数值原子轨道库, 已在不同硬件平台上成功移植, 为OpenLAM项目提供了大量密度泛函理论计算数据, 应用领域覆盖了合金、半导体、钙钛矿等多种材料体系. 最后, 本文展望了ABACUS在AI for Science时代下的发展. Abacus (architecture) ; Geography ; Archaeology Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Kenneth L. Kearns | Catherine M. Bishop | Lawrence Judovits | John Rosener | Cathy Stewart | Tina Adams
Source: Polymers (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Abstracts of the 2025 51st Annual NATAS Conference The North American Thermal Analysis Society (NATAS) is pleased to announce its 51st Annual Conference, held jointly with the IX International Baekeland Symposium. This premier event unites scientists, practitioners, and students from academia, industry, and government to explore the forefront of materials science. The NATAS conference provides a dynamic forum for attendees to delve into the latest advancements in thermal analysis, rheology, and materials characterization. The technical program will highlight new developments in instrumentation and software, alongside practical applications across a wide range of industries. Concurrently, the Baekeland Symposium will showcase cutting-edge scientific, technical, and industrial innovations in the field of high-performance thermosetting polymers. The synergy of this joint meeting creates a unique platform for cross-disciplinary collaboration, fostering the exchange of novel ideas and sparking new research opportunities. Featuring technical presentations, poster sessions, and plenary lectures from renowned experts and emerging graduate students, the conference offers an ideal environment for networking and professional development. We invite you to join us to discover state-of-the-art techniques, discuss groundbreaking research, and connect with peers and leaders in the thermal and materials community. Government (linguistics) ; Engineering ethics ; Engineering ; Library science ; Engineering management ; Political science ; Computer science Thermal and Kinetic Analysis ; Heat Transfer and Optimization ; Phase Equilibria and Thermodynamics
Authors: Semin Kim | Hyeonseok Yoon
Source: Communications Materials (article, 2025-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The strategic role of conducting polymers in zinc- and alkali-ion hybrid capacitors The integration of mechanically resilient conducting polymers into zinc- and alkali-ion hybrid capacitors has attracted attention for sustainable, flexible, high-performance, energy storage systems. By exhibiting mixed ionic–electronic conductivity, controllable ion-polymer interactions, electric double-layer, pseudocapacitive, and battery-type behavior, conducting polymers can perform multifunctional roles in both cathodes and anodes, and are capable of supporting all three charge storage mechanisms. As cathodes, conducting polymers provide redox activity and enable versatile integration with carbon or metal oxide hybrids, enhancing both capacity and rate performance. As anodes, conducting polymers serve as ion-selective, conformal coatings that mitigate dendritic growth, buffer structural stress, and stabilize solid–electrolyte interphases. Recent developments in three-dimensional nanostructuring, ion-selective frameworks, and elastomeric composites for wearable applications, along with AI-guided materials discovery and sustainable materials integration, have established conducting polymers as indispensable building blocks for safe, sustainable, and high-performance hybrid capacitors for powering the next generation of electronics and energy storage systems. This Review discusses the use of conducting polymers as anodes and cathodes in zinc- and alkali-ion hybrid capacitors, including their ability to support charge storage mechanisms, as a sustainable way to improve performance of electronics and energy storage devices. Capacitor ; Energy storage ; Conductive polymer ; Supercapacitor ; Electronics ; Cathode Supercapacitor Materials and Fabrication ; Dielectric materials and actuators ; Advanced battery technologies research
Authors: Gyoung S. Na
Source: npj Computational Materials (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Cross-modality material embedding loss for transferring knowledge between heterogeneous material descriptors Despite the remarkable successes of transfer learning in materials science, the practicality of existing transfer learning methods are still limited in real-world applications of materials science because they essentially assume the same material descriptors on source and target materials datasets. In other words, existing transfer learning methods cannot utilize the knowledge extracted from calculated crystal structures when analyzing experimental observations of real-world chemical experiments. We propose a transfer learning criterion, called cross-modality material embedding loss (CroMEL), to build a source feature extractor that can transfer knowledge extracted from calculated crystal structures to prediction models in target applications where only simple chemical compositions are accessible. The prediction models based on transfer learning with CroMEL showed state-of-the-art prediction accuracy on 14 experimental materials datasets in various chemical applications. In particular, the prediction models with CroMEL achieved R2-scores greater than 0.95 in predicting the experimentally measured formation enthalpies and band gaps of the experimentally synthesized materials. Modality (human–computer interaction) ; Embedding ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Image Retrieval and Classification Techniques
Authors: Bing Zhang | Ziqi Wang | Guo Wei-ping | Donghua Xing | Duo Pan | Wenke Yang | Hu Liu
Source: Journal of Composites Science (article, 2025-09-14)
Field: Materials Science — Polymers and Plastics

Magnetoresistive Polyaniline Nanocomposites Incorporating Nickel Ferrite-Modified Carbon Nanotubes In this work, nickel ferrite/carbon nanotubes–polyaniline (NiFe2O4@CNTs-PANI) nanocomposites with positive magnetoresistance (MR) phenomenon was obtained by the hydrothermal method combined with the surface-initiated polymerization (SIP) method. The NiFe2O4@CNTs-PANI nanocomposites’ resistivity decreased as the temperature increased, exhibiting typical semiconductor behavior. This temperature-dependent resistivity revealed a quasi-three-dimensional (3D) variable range hopping (VRH) charge carrier transport mechanism. Based on the wave function shrinkage model, its positive MR was studied. It was found that the localization length (a0) and average hopping distance (Rhop) were decreased with increasing magnetic field strength, indicating the reduction in the hopping probability of the charge carrier, which was beneficial for the positive MR. Meanwhile, the values of a0 and Rhop were decreased with increasing NiFe2O4@CNTs loading, expressing a nanofiller loading-dependent behavior. Magnetoresistance ; Variable-range hopping ; Polyaniline ; Electrical resistivity and conductivity ; Nanocomposite ; Nickel ; Carbon nanotube ; Conductivity Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies
Authors: Dipam Manish Patel | Simiam Ghan | Andreas Lynge Vishart | Georg Kastlunger
Source: ChemRxiv (preprint, 2025-07-09)
Field: Chemistry — Electrochemistry

Improved Hydrogen Evolution Activity Descriptors from First-principles Electrochemical Kinetics Catalyst activity in the Hydrogen Evolution Reaction (HER) is traditionally estimated based on Sabatier's principle, with hydrogen's adsorption strength as its sole variable. This elegant descriptor qualitatively captures HER activity trends but lacks information on electrolyte composition and exhibits distinct outliers like Pt's exceptionally high and Cu's low activity. Numerous studies have added mechanistic insights from first-principles; however, they have generally been limited to analyzing kinetics in acid. Here, we use constant-potential DFT coupled with Gaussian process-based transition state search to compile microkinetic models for alkaline HER. Accounting for both non-Nernstian effects and adsorbate-adsorbate interactions, we reproduce experimental activity trends, as well as outliers in the conventional volcano. Relating activities to descriptors, we find that both refining the traditional descriptor with catalyst surface charge information and applying the H binding strength in the (generally) unfavorable top-site exhibit largely improved predictive power for exchange current densities. This stems from their ability to capture trends in the kinetics of electrochemical hydrogen adsorption, the rate-limiting step on all investigated surfaces, as a consequence of adsorbate-adsorbate interactions. However, these descriptors are not applicable in predicting trends in the kinetics of non-electrochemical H2-desorption, which instead follow the difference in binding strengths between fcc- and top-sites and an effective electronic coupling term. Combining all identified descriptors, we define novel activity volcanoes, which can aid screening for abundant and active HER catalysts. This shows how mechanistic analysis of even the simplest electrocatalytic reactions can still improve our understanding of reactive processes at the electrified solid-liquid interface. Kinetics ; Electrochemistry ; Electrochemical kinetics ; Chemistry ; Physical chemistry ; Physics ; Electrode ; Classical mechanics Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yan Liu | Chuan Zhou | Shuai Zhang | Peng Zhang | Xixun Lin | Shirui Pan
Source: ArXiv.org (preprint, 2025-01-31)
Field: Materials Science — Materials Chemistry

Equivariant Hypergraph Diffusion for Crystal Structure Prediction Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for the development of new materials and the advancement of various scientific disciplines. Recent developments have shown that generative models, particularly diffusion models, hold great promise for CSP. However, traditional graph-based representations, where atomic bonds are modeled as pairwise graph edges, fail to fully capture the intricate high-order interactions essential for accurately representing crystal structures. In this work, we propose a novel approach that utilizes hypergraphs to represent crystal structures, providing a more expressive abstraction for modeling multi-way atomic interactions. By adopting hypergraphs, we can effectively capture complex high-order relationships and symmetries, such as permutation and periodic translation invariance, which are crucial for characterizing crystal structures. In this work, we propose the \textbf{E}quivariant \textbf{H}ypergraph \textbf{Diff}usion Model (\textbf{EH-Diff}), a generative model designed to take advantage of the symmetry-preserving properties of hypergraphs. EH-Diff exploits these features to offer an efficient and accurate method for predicting crystal structures with a strong theoretical justification to preserve invariance properties. Empirically, we conduct extensive experiments on four benchmark datasets, and the results demonstrate that EH-Diff outperforms state-of-the-art CSP methods with only one sample. Equivariant map ; Hypergraph ; Diffusion ; Mathematics ; Computer science ; Pure mathematics ; Combinatorics ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Muhammad Kamran | Sahar Abdalla | S.S. Murtaza | Abdullah Almohammedi | Nuha Y. Elamin | Abida Wareed | Surayya Mukhtar
Source: Energy Technology (article, 2025-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A First Principles Study of Structural, Mechanical, Phonon, Optoelectronic, and Thermoelectric Properties of MnX <sub>2</sub> O <sub>4</sub> (X = Al, Cr) Spinels for Photovoltaic and Thermoelectric Applications This study investigates the physical properties of the spinel oxides MnAl 2 O 4 and MnCr 2 O 4 using the modified Becke–Johnson potential (mBJ). The stability was also evaluated based on tolerance ( τ ) factor and formation energy (Δ H ) calculations. Positive frequencies are observed in phonon dispersion spectra of both spinels confirming their dynamical stability. Mechanical stability is assured by the Born stability criteria. Both spinels exhibit ferromagnetic ordering, as confirmed by the integral values of their magnetic moments, and display semiconducting behavior according to the accurate mBJ calculations. Analysis of optical conductivity exhibits a high conductivity in the ultraviolet (UV) range, thus proving to be appropriate for photovoltaic devices, specifically for UV detection and solar cell devices. MnAl 2 O 4 and MnCr 2 O 4 have thermoelectric (TE) characteristics indicating that their low thermal conductivity alongside optimal Seebeck values forms the basis for excellent figure of merit (ZT) values very close to unity at 300 K. Their equilibrium between thermal and electronic transport characteristics suggests that both would perform well within respective TE temperature ranges. These results establish MnAl 2 O 4 and MnCr 2 O 4 as multifunctional candidates for optoelectronic and energy‐conversion technologies. Thermoelectric effect ; Spinel ; Thermal conductivity ; Figure of merit ; Thermal stability ; Thermoelectric materials ; Dispersion (optics) ; Conductivity ; Seebeck coefficient Heusler alloys: electronic and magnetic properties ; Magnetic Properties and Synthesis of Ferrites ; Advanced Thermoelectric Materials and Devices
Authors: Kai Zhang | Hongying Xia | Gang Cheng | Peng Gan | Yuan Yuan Ju | Bihui Guo | Jingli Yang | C. F. Qiao | Jixiang Lin | Jiachuan Chen
Source: Polymers (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

Catalytic Conversion of Xylo-Oligomers to Furfural in Pulping Pre-Hydrolysis Liquor Using a Hydroxyl-Functionalized Covalent Organic Framework With the rapid development of biorefinery technology, the efficient conversion of lignocellulose into high-value platform chemicals is of great significance for enhancing the value of renewable carbon resources. In this study, a hydroxyl-functionalized covalent organic framework (COF), TAPB-DHPA, was synthesized via an in situ method and innovatively applied to the catalytic conversion of xylo-oligosaccharides (XOS) into furfural. The results demonstrated that TAPB-DHPA possesses a large specific surface area, a well-developed porous structure, and excellent thermal stability, with abundant Brønsted acid (B acid) sites, exhibiting outstanding catalytic activity. Under optimal conditions, including a catalyst loading of 0.16 wt%, a reaction temperature of 180 °C, and a reaction time of 3 h, a furfural yield of up to 65.4% was achieved. The high selectivity was primarily attributed to the p-π conjugation effect between the benzene ring and the phenolic hydroxyl group, which enhanced the ionization ability of hydroxyl hydrogen, thereby effectively promoting the hydrolysis of XOS and subsequent dehydration. Furthermore, TAPB-DHPA exhibited excellent recyclability and stability, maintaining a furfural yield of over 59.9% after six cycles. This study provides new insights into the application of functionalized COF in biomass catalytic conversion and contributes to the green transformation of the pulp and paper industry into a biorefinery-based model. Furfural ; Biorefinery ; Chemistry ; Catalysis ; Organic chemistry ; Hydrolysis ; Benzene ; Raw material Covalent Organic Framework Applications ; Catalysis for Biomass Conversion ; Supercapacitor Materials and Fabrication
Authors: Lin Ye | Jianwen Yang | Dong‐Pyo Kim | Dengrong Sun
Source: Nano Research (article, 2025-09-08)
Field: Chemistry — Inorganic Chemistry

Advances in hydrophobic metal-organic frameworks (MOFs) for photo/electrocatalytic CO <sub>2</sub> reduction Reduction of CO<sub>2</sub> into fuels and chemicals by means of photocatalysis or electrocatalysis offers a sustainable route to mitigate greenhouse emissions. Hydrophobic metal-organic frameworks (MOFs) have emerged as promising photo/electro-catalysts for CO<sub>2</sub> reduction due to their tunable porosity, high surface areas, and ability to repel water for suppressing competing hydrogen evolution. This review summarized the recent advances of hydrophobic MOFs for photo/electrocatalytic CO<sub>2</sub> reduction, catalogged by the principle mechanism of CO<sub>2</sub> reducion, design strategies of hydrophobic MOFs and the applications of hydrophobic MOFs in CO<sub>2</sub> reduction. The personal perspectives are also proposed to inspire more efforts in design diversified hydrophobic MOFs for efficient CO<sub>2</sub> utilization. Photocatalysis ; Electrocatalyst ; Hydrophobic effect ; Reduction (mathematics) ; Metal-organic framework ; Hydrophobe Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Nuaman F. Alheety | Abdulrahman Khalid Mssdf | Rafâa Besbes | Mustafa A. Alheety
Source: Macromolecular Symposia (article, 2025-10-26)
Field: Materials Science — Materials Chemistry

Influence of Ring Size on Hydrogen Storage Performance of cis‐Aziridine Derivatives: A Thermodynamic Investigation ABSTRACT This study investigates the influence of ring size on the hydrogen storage capacity of cis‐aziridine derivatives through a thermodynamic approach. Two N‐cycloalkylated ethyl aziridine‐2‐carboxylates, containing six‐ and seven‐membered rings, were synthesized in a single step via aza‐Michael addition and intramolecular cyclization. Structural characterization was performed using FTIR and 1 H‐NMR. Hydrogen storage performance was evaluated under varying pressures (0–100 bar) at 125°C. Results revealed that the seven‐membered ring derivative achieved 4 wt.% storage capacity, surpassing the six‐membered ring derivative (3.6 wt.%). The findings highlight the structural advantage of larger cyclic systems for enhancing hydrogen uptake, offering valuable insights for the design of efficient hydrogen storage materials. Hydrogen storage ; Ring (chemistry) ; Intramolecular force ; Fourier transform infrared spectroscopy ; Storage ring ; Hydrogen Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Caleb T. Kozuszek | Cole C. Sorensen | Frank A. Leibfarth
Source: ChemRxiv (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

Data Driven Stereoselective Cationic Polymerization Controlling the tacticity of synthetic polymers results in the transformation of simple chemical building blocks into valuable materials with emergent physical properties. Mechanistic insight into stereoselective polymerizations enables hypothesis-driven improvements to catalysts and gives access to polymers with systematic differences in tacticity for structure–property studies. Studying the mechanism of stereoselective polymerization, especially of heteroatom-containing monomers that polymerize through ionic intermediates, is hindered by the challenges of using traditional physical–organic or computational approaches. Here, we use a combination of experiments and computationally derived molecular descriptors to identify quantitative relationships between catalyst structure and stereoselectivity through a data science approach. Stereoselective polymerization of benzyl vinyl ether derivatives with a structurally diverse library of imidodiphosphorimidate (IDPi) catalysts resulted in 40 experimental data points, which were correlated to computationally derived molecular descriptors using multivariate linear regression analysis. The regression model identified the dihedral angle of the 1,1’-binaphthyl-2,2’-diol (BINOL) subunit of the IDPi as a key determinant of isotacticity, which led us to reconsider the long-standing hypothesis for the conformation of the propagating polymer chain-end during cationic vinyl ether polymerization. We anticipate that the specific insights of this study will inform the next-generation of catalysts for stereoselective cationic polymerization, and that the datadriven approach to understand the mechanism for stereoselective polymerizations demonstrated herein will be an invaluable tool in catalyst design and discovery for polymer chemistry broadly. Cationic polymerization ; Stereoselectivity ; Polymerization ; Chemistry ; Polymer chemistry ; Organic chemistry ; Polymer ; Catalysis Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organometallic Complex Synthesis and Catalysis