jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lingshuang Kong | Fengrui Zhao | Jing Li | Fanlun Meng | Wenlong Xu
Source: Advanced Science (article, 2025-07-13)
Field: Materials Science — Materials Chemistry

Solvent Replacement‐Driven Ionic Liquid Thermoelectric Gel for Self‐Powered Morse Code Communication Assisted by Machine Learning The development of ionic liquid gels (IL gels) with both high thermoelectric performance and mechanical flexibility is essential for advancing low-grade heat energy harvesting in next-generation flexible electronics and self-powered systems. Herein, a poly(methacrylic acid) (PMAA)-based IL gel is reported, fabricated via a solvent replacement strategy. By tailoring the synergistic coordination between Fe<sup>2+</sup>/Fe<sup>3+</sup> redox couples and carboxyl (─COOH) groups in the polymer network, the gel functions as a thermogalvanic electrolyte, and its voltage generation is driven by temperature-dependent redox reactions of Fe<sup>2+</sup>/Fe<sup>3+</sup>. The resulting gel demonstrates excellent thermoelectric stability over a broad temperature range, achieving a high ionic conductivity (σ) of 13.45 S·m<sup>-1</sup> and the Seebeck coefficient (S<sub>i</sub>) of -4.67 mV·K<sup>-1</sup>. In situ Raman spectroscopy and low-field solid-state nuclear magnetic resonance (NMR) analysis reveal the directional migration behavior of Fe<sup>2+</sup>/Fe<sup>3+</sup> ions under a thermal gradient and their dynamic coupling with polymer chain motion. Furthermore, a self-powered Morse-code communication system is developed using a machine learning (ML)-assisted framework. A logistic regression model achieved 100% accuracy on an independent test set, indicating a strict monotonic mapping between voltage signals and encoded characters. This work provides new insights into the molecular design and thermoelectric regulation mechanisms of flexible thermoelectric gel, paving the way for their practical application in wearable self-powered communication devices. Thermoelectric effect ; Materials science ; Ionic liquid ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Nishchal Bharadwaj | Diptendu Roy | A. Das | Biswarup Pathak
Source: ACS Materials Letters (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Screening of Atomically Precise Pure Metal Nanoclusters for Oxygen Reduction Developing efficient catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells is challenging due to high power density and durability requirements. Subnanometer clusters (SNCs) show promise, but their fluxional behavior and complex structure–activity relationships hinder catalyst design. We combine density functional theory (DFT) and machine learning (ML) to study transition metal-based subnanometer nanoclusters (TMSNCs) ranging from 3 to 30 atoms, aiming to establish structure activity relationship (SAR) for ORR. Subdividing the data set based on size and periodic groups significantly improves the accuracy of our ML models. Importantly, the ML model predicting the ORR catalytic performance is validated through DFT calculations, identifying 12 promising catalysts. Late group TMSNCs exhibit enhanced ORR activity, reflected in a noticeable shift toward Au/Ag metals on the volcano plot. This underscores the importance of investigating late group TMSNCs alongside Pt for the ORR. ML accelerates TMSNC design, surpassing computational screening and advancing catalyst development. Nanoclusters ; Oxygen reduction ; Reduction (mathematics) ; Materials science ; Metal ; Oxygen ; Nanotechnology ; Metallurgy ; Chemistry ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Olatunji Oladimeji Ojo | Emmanuel Olatunji Olutomilola | Anthony Yinka Oyerinde | Afolawemi Afolabi | T.E. Abioye | Isiaka Oluwole Oladele | Kenneth Kanayo Alaneme
Source: Materials Today Communications (article, 2025-01-20)
Field: Chemistry — Organic Chemistry

Advancements in microwave welding: Susceptor-assisted microwave hybrid-heating technique for metals and polymers Susceptor ; Materials science ; Microwave ; Microwave heating ; Welding ; Polymer ; Metallurgy ; Composite material ; Optoelectronics ; Telecommunications Microwave-Assisted Synthesis and Applications ; Synthesis and properties of polymers ; Epoxy Resin Curing Processes
Authors: Zhongli Zhang | Zhensheng Wu | Shuai Zheng | Haitao Yang | Fuqiang Tian
Source: Progress in Organic Coatings (article, 2025-01-08)
Field: Materials Science — Polymers and Plastics

Multiscale analysis of breakdown strength in polyimide nanocomposite films: Simulations, experiments, and machine learning Materials science ; Polyimide ; Nanocomposite ; Composite material Synthesis and properties of polymers ; Dielectric materials and actuators ; High voltage insulation and dielectric phenomena
Authors: Hongwei Liu | Zhenming Cui | Yan Sun | Zhennan Qiao | Yanlan Zhang | Qiang Bai | Yongzhen Wang
Source: Energy Materials (article, 2025-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergistic design and synthesis of O, N Co-doped hierarchical porous carbon for enhanced supercapacitor performance Carbon-based supercapacitors have emerged as promising energy storage components for renewable energy applications due to the unique combination of various physicochemical characteristics in porous carbon materials (PCMs) that can improve specific capacitance (SC) properties. It is essential to develop a methodical approach that exploits the synergy of these effects in PCMs to achieve superior capacitance performance. In this study, machine learning (ML) provided a clear direction for experiments in the screening of key physicochemical features; SHapley Additive exPlanations analysis on ML indicated that specific surface area and specific doping species had a significant synergistic impact on SC enhancement. Utilizing these insights, an O, N co-doped hierarchical porous carbon (ONPC-900) was synthesized using a synergistic pyrolysis strategy through K2CO3-assisted in-situ thermal exfoliation and nanopore generation. This method leverages the role of carbon nitride (graphite-phase carbon nitride) as an in-situ layer-stacked template and the oxygen (O)-rich properties of the pre-treated lignite, enabling controlled synthesis of graphene-like folded and amorphous hybrid structures engineered for the efficient N and O doping sites and high specific surface area, resulting in an electrode material with enhanced structural adaptability, rapid charge transfer, and diffusion mass transfer capacity. Density functional theory (DFT) calculations further confirmed that pyrrole nitrogen (N-5), carboxyl (-COOH) active sites, and the defect structure formed by pores synergically enhanced the adsorption of electrolyte ions (K+) and electron transfer, improving the SC performance. The optimized ONPC-900 electrode exhibited impressive SC properties of 440 F g-1 (0.5 A g-1), outperforming most coal-based PCMs. This study provides a methodology for designing and synthesizing high SC electrode materials by optimizing the key characteristic parameters of synergism from complex structure-activity relationships through the combination of ML screening, experimental synthesis, and density functional theory validation. Supercapacitor ; Materials science ; Chemical engineering ; Porosity ; Doping ; Carbon fibers ; Capacitance ; Chemistry ; Composite material ; Electrode ; Composite number ; Optoelectronics ; Engineering Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Catalytic Processes in Materials Science
Authors: Yehu Shen | Jiahao Du | Yifan He | Hezhe Wang | Ting Hao | Xue‐Mei Niu | Q.K. Jiang
Source: Materials Today Communications (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

AAKAN-WGAN: Predicting mechanical properties of magnesium alloys based on generative design and adaptive activation of Kolmogorov-Arnold Networks Materials science ; Magnesium ; Generative grammar ; Metallurgy ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced Materials Characterization Techniques
Authors: Yaolong Zhang | Bin Jiang | Hua Guo
Source: Journal of Chemical Theory and Computation (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

SchrödingerNet: A Universal Neural Network Solver for the Schrödinger Equation Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schrödinger equation (SE) by integrating various neural network (NN)-based wave function ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born-Oppenheimer approximation (BOA) and require computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, SchrödingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system. This approach is based on a translationally, rotationally and permutationally symmetry-adapted total wave function ansatz that includes both nuclear and electronic coordinates. This strategy not only allows for an efficient and accurate generation of a continuous potential energy surface at any geometry within the well-sampled nuclear configuration space, but also incorporates non-BOA corrections, through a single training process. Comparison with benchmarks of atomic and small molecular systems demonstrates its accuracy and efficiency. Ansatz ; Wave function ; Artificial neural network ; Computer science ; Solver ; Function (biology) ; Configuration space ; Schrödinger equation ; Statistical physics ; Applied mathematics ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Michael Parzer | Alexander Riss | Fabian Garmroudi | Johannes de Boor | Takao Mori | E. Bauer
Source: npj Computational Materials (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

SeeBand: a highly efficient, interactive tool for analyzing electronic transport data SeeBand is an interactive tool for extracting microscopic material parameters by fitting temperature-dependent thermoelectric transport properties using Boltzmann transport theory. With real-time comparison between electronic band structures and transport data, it analyzes the Seebeck coefficient, resistivity, and Hall coefficient. Neural-network-assisted guesses and efficient fitting routines enable high-throughput processing of large datasets. SeeBand accelerates material design by allowing electronic band structure models to be derived directly from a single sample’s transport measurements. Computer science ; Human–computer interaction Advanced Thermoelectric Materials and Devices ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: W.L. Wang | Punit Kumar | David H. Cook | Flynn Walsh | Buyu Zhang | Pedro P. P. O. Borges | Diana Farkas | Robert O. Ritchie | Mark Asta
Source: npj Computational Materials (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Ductility mechanisms in complex concentrated refractory alloys from atomistic fracture simulations Abstract The striking variation in damage tolerance among refractory complex concentrated alloys is examined through the analysis of atomistic fracture simulations, contrasting behavior in elemental Nb with that in brittle NbMoTaW and ductile Nb 45 Ta 25 Ti 15 Hf 15 . We employ machine-learning interatomic potentials (MLIPs), including a new MLIP developed for NbTaTiHf, in atomistic simulations of crack tip extension mechanisms based on analyses of atomistic fracture resistance curves. While the initial behavior of sharp cracks shows good correspondence with the Rice theory, fracture resistance curves reveal marked changes in fracture modes for the complex alloys as crack extension proceeds. In NbMoTaW, compositional complexity appears to promote dislocation nucleation relative to pure Nb, despite theoretical predictions that the alloy should be relatively more brittle. In Nb 45 Ta 25 Ti 15 Hf 15 , alloying alters the fracture mode compared to elemental Nb, promoting crack tip blunting and enhancing resistance to crack propagation. Ductility (Earth science) ; Fracture (geology) ; Nucleation ; Dislocation ; Brittleness ; Alloy ; Refractory metals ; Deformation (meteorology) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Charlie Meisel | Jake Huang | You‐Dong Kim | Sophia Stockburger | Ryan O’Hayre | Neal P. Sullivan
Source: Journal of the American Ceramic Society (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Insights on proton‐conducting ceramic electrochemical cell fabrication Abstract This study investigates the key factors influencing sintering behavior and grain growth in (BCZYYb4411)–NiO negatrodes and BCZYYb electrolytes for protonic ceramic electrochemical cells (PCECs). Elastic net machine learning models are applied to a dataset of nearly 200 individual PCEC button cells fabricated over the course of more than 3 years to identify the key processing parameters that significantly affect negatrode shrinkage and electrolyte grain growth. The shrinkage rate of the BCZYYb4411–NiO negatrode is primarily governed by the solid‐state sintering behavior. Higher sintering temperatures, longer dwell times, and smaller NiO particle size are the primary determinants that lead to greater shrinkage. New or lightly‐used setters and more compact negatrodes are also found to increase shrinkage. Electrolyte grain growth is chiefly controlled by the liquid‐phase sintering of the BCZYYb phase. Increased cerium content on the B‐site leads to the largest enhancement in grain size, followed by increasing maximum sintering temperature. We find that the parameters used to tune the spray deposition of the electrolyte layer are also critical, with wetter and more uniform sprays promoting grain enlargement. Finally, we find that the sintering environment (e.g. presence/absence of sintering neighbors or sacrificial powders and the ambient humidity level) also substantially impacts both shrinkage and grain growth. This work comprehensively analyzes data from nearly 200 PCECs without “success bias,” meaning that poor performers and fabrication failures were included in the analysis. By doing so, the study provides valuable insight into the critical factors controlling shrinkage and grain growth in BCZYYb‐based PCECs. The findings offer foundational guidance for processing optimization that could lead to better repeatability, increased yields, and higher performance. Fabrication ; Ceramic ; Materials science ; Electrochemistry ; Proton ; Nanotechnology ; Electrochemical cell ; Composite material ; Chemistry ; Electrode ; Physical chemistry ; Physics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Gas Sensing Nanomaterials and Sensors
Authors: Irene Lepori | Zichen Liu | Nelson Iyore Evbarunegbe | Shasha Feng | Turner Brown | Kishor Mane | Shivangi Shivangi | Mitchell D. Wong | Amir George | Taijie Guo | Jiajia Dong | Joel S. Freundlich | Wonpil Im | Anna G. Green | Marcos M. Pires | M. Sloan Siegrist
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-27)
Field: Chemistry — Organic Chemistry

Identification of chemical features that influence mycomembrane permeation and antitubercular activity Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (Mtb), is the deadliest single-agent infection worldwide. Current antibiotic treatment for TB takes a minimum of four months, underscoring the need for better therapeutics. The unique mycobacterial cell envelope, particularly the outermost mycomembrane, has long been thought to promote intrinsic antibiotic resistance by limiting compound entry into Mtb. Understanding chemical features that influence permeation across the mycomembrane may enable more accurate predictions of whole cell anti-Mtb activity, leading to development of more effective antibacterials. Here we query the mycomembrane permeation of over 1500 azide-tagged compounds in live Mtb with the bioorthogonal click chemistry-based assay PAC-MAN. We use cheminformatics and machine learning to identify chemical features associated with mycomembrane permeation and show that they have predictive value via systematic modification of two small molecule series. Additionally, we find that chemical features that influence mycomembrane permeation correlate with anti-Mtb activity in large compound libraries. These findings suggest that the mycomembrane is indeed a significant barrier to whole cell activity in Mtb and provide a rational framework for designing or modifying compounds to overcome this barrier. Mycobacterium tuberculosis ; Permeation ; Cheminformatics ; Bioorthogonal chemistry ; Tuberculosis ; Chemistry ; Limiting ; Combinatorial chemistry ; Computational biology ; Biochemical engineering ; Membrane ; Biology ; Click chemistry ; Biochemistry ; Medicine ; Computational chemistry ; Engineering Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Bacteriophages and microbial interactions
Authors: Yuan Fang | Jiang Zhou | Yusuke Yamauchi | Yiqiang Wu | Caichao Wan
Source: Coordination Chemistry Reviews (article, 2025-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Supercapacitor dynamics: Mechanisms, architectures, and advanced in-situ characterizations for next-generation energy storage Supercapacitor ; Characterization (materials science) ; Energy storage ; Bridging (networking) ; Pseudocapacitance ; Renewable energy ; Pseudocapacitor ; Electrochemical energy storage Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Ömer Tayfuroğlu | Seda Keskın
Source: The Journal of Chemical Physics (article, 2025-08-01)
Field: Chemistry — Inorganic Chemistry

Modeling CO2 adsorption in flexible MOFs with open metal sites via fragment-based neural network potentials Metal-organic frameworks (MOFs) with open metal sites (OMS) are among the most promising porous materials for gas adsorption and separation, owing to their strong and selective interactions with guest molecules. However, simulating adsorption in such systems with high accuracy and efficiency remains a key challenge due to the need to model complex guest-MOF interactions and framework flexibility. Classical force fields often lack the precision to capture these effects, while ab initio methods are computationally prohibitive for large-scale, long-timescale simulations. In this work, we developed a neural network potential (NNP) trained on highly accurate density functional theory (PBE-D4/def2-TZVP) level data derived from a single representative fragment of the Mg-MOF-74 framework, a prototypical OMS-containing MOF, with CO2 molecules. Despite the limited training domain, the NNP accurately captured both intra- and inter-molecular interactions in the CO2-Mg-MOF-74 system, including those involving the open metal sites. We integrated this NNP into a hybrid molecular dynamic and grand canonical Monte Carlo simulation workflow, enabling accurate modeling of CO2 adsorption in flexible MOFs. This approach allows accounting for both framework dynamics and complex host-guest interactions with chemical accuracy and computational efficiency. Our results highlight the crucial role of framework flexibility in adsorption behavior and demonstrate that fragment-based NNP, when combined with advanced simulation techniques, offer a powerful and efficient approach for realistically modeling adsorption processes in MOFs with open metal sites. Flexibility (engineering) ; Workflow ; Metal-organic framework ; Adsorption ; Molecular dynamics ; Computer science ; Artificial neural network ; Complex system ; Multiscale modeling ; Molecule ; Fragment (logic) ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Artificial intelligence ; Algorithm ; Physical chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Rituraj Mahanta | Pawan Chetri | Nishant Shukla | Santosh Kumar Tamang
Source: Applied Surface Science (article, 2025-07-31)
Field: Materials Science — Biomaterials

A combined experimental, DFT, and machine learning study of cellulose nanocrystal/SnO2 composites for efficient sunlight-assisted wastewater remediation Nanocrystal ; Environmental remediation ; Cellulose ; Sunlight ; Wastewater ; Composite material ; Materials science ; Chemical engineering ; Waste management ; Nanotechnology ; Engineering ; Optics ; Physics ; Contamination ; Ecology Advanced Cellulose Research Studies ; TiO2 Photocatalysis and Solar Cells
Authors: Zhenglu Yang | Jiyu Cui | Lifeng Yang | Xiaofei Lü | Xian Suo | Xili Cui | Huajun Chen | Huabin Xing
Source: Separation and Purification Technology (article, 2025-05-08)
Field: Chemistry — Inorganic Chemistry

Powder X-ray diffraction patterns based deep learning for the prediction of adsorption in metal-organic frameworks Adsorption ; Metal-organic framework ; Diffraction ; Metal ; Powder diffraction ; Materials science ; Deep learning ; X-ray ; X-ray crystallography ; Chemical engineering ; Chemistry ; Crystallography ; Computer science ; Artificial intelligence ; Metallurgy ; Organic chemistry ; Engineering ; Physics ; Optics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Xing He | Mayanak K. Gupta | D. L. Abernathy | G. E. Granroth | Feng Ye | Barry Winn | L. A. Boatner | Olivier Delaire
Source: Proceedings of the National Academy of Sciences (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Resolving the dynamic correlated disorder in KTa <sub> 1− <i>x</i> </sub> Nb <sub> <i>x</i> </sub> O <sub>3</sub> Understanding the complex temporal and spatial correlations of ions in disordered perovskite oxides is critical to rationalize their functional properties. Here, we provide insights into the longstanding controversy regarding the off-centering of transition metal (TM) ions in the archetypal ferroelectric alloy KTa[Formula: see text]Nb[Formula: see text]O[Formula: see text] (KTN). By mapping the full energy ([Formula: see text]) and wavevector ([Formula: see text]) dependence of the dynamical structure factor [Formula: see text] using neutron scattering, and rationalizing our observations with atomistic simulations leveraging machine learning, we fully resolve the static <i>v</i>s dynamic nature of diffuse scattering sheets, as well as their composition ([Formula: see text]) and temperature dependence. Our first-principles simulations, extended with machine-learning molecular dynamics, reproduce both inelastic neutron spectra and diffuse features, and establish how dynamically correlated TM off-centerings couple to phonons, unifying local and collective viewpoints. This study sheds light into an exemplary ferroelectric system and shows the importance of mapping the full [Formula: see text] to reveal critical spatiotemporal correlations of atomic disorder from which functional properties emerge. Algorithm ; Artificial intelligence ; Materials science ; Computer science Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Perovskite Materials and Applications