jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Ju | Usama S. Altimari | Raman Kumar | Ashutosh Pattanaik | Hrushikesh Sarangi | Deepak K. Gupta | Vikram Rao | Dilsora Abduvalieva | Vikasdeep Singh Mann | Heyder Mhohamdi | Aseel Smerat | Ahmad Abumalek
Source: Chemical Papers (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Robust classification frameworks for oxide nanomaterials assisted by machine learning Nanomaterials ; SAFER ; Oxide ; Surface modification ; Core (optical fiber) ; Surface (topology) Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Computational Drug Discovery Methods
Authors: Longfei Cui | Wen‐Li Chang | Qingchao Liu | Tao Wang | Xiao-Ping Wei | Xiaoma Tao
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A theoretical study of the thermoelectric properties of two-dimensional surface-functionalized MXene materials ScYCTT′ (T = OH, H; T′ = H, F) MXenes are a novel family of two-dimensional layered materials consisting of transition metal carbides, nitrides, or carbon-nitrides, and great interest has been shown in these materials because of their distinctive structural configurations. In this study, the electronic structures of functionalized ScYC(OH)H, ScYC(OH)F, and ScYCHF were initially evaluated through density-functional theory (DFT) to judge their stability in the two-dimensional state. Subsequently, based on the Boltzmann transport theory, we conducted an in-depth analysis of the key thermoelectric parameters of these materials, including the Seebeck coefficient (<i>S</i>), electrical conductivity (<i>σ</i>), power factor (PF) and thermal conductivity (<i>κ</i>), with lattice thermal conductivity calculations accelerated using machine learning (MTP) methods. The results show that the n-type ScYCTT' (T = OH, H; T' = H, F) exhibits superior power factor performance compared to its p-type counterpart. Within the thermal range of 300 to 900 K, the thermoelectric figure of merit of the three materials (<i>ZT</i>) increases with increasing temperature, particularly under 900 K conditions, where the n-type ScYCTT' manifests remarkable <i>ZT</i> peaks of 0.97, 1.11, and 0.59, respectively, better than those of the p-type. Therefore, ScYCTT' exhibits promising thermoelectric performance across the 300-900 K temperature interval. MXenes ; Thermoelectric effect ; Thermal conductivity ; Seebeck coefficient ; Thermoelectric materials ; Figure of merit ; Electrical resistivity and conductivity ; Atmospheric temperature range MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Shengjun Liu (3635473) | Jizhe Song (21178670) | Sujuan Feng (3942527) | Jiacheng Li (826240) | Xiaolong Wang (20235) | Xinyue Wang (286629) | Yu Wang (12152) | Guangqiang Liu (1787232)
Source: Figshare (article, 2025-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Composite NanotubeArrays for Pesticide DetectionAssisted with Machine Learning Based on SERS Effect This study presents a streamlined approach for the large-scale fabrication of composite nanotube arrays (CNA) with a diameter of approximately 330 nm and a wall thickness of ∼60 nm via reactive ion etching. By conducting a thorough investigation into the entire formation process and various influencing factors, we have identified the critical role played by the reaction products involving the silica layer, the aluminum sample stage, and fluoride ions in the nanotube formation process. Moreover, surface-enhanced Raman scattering (SERS) analyses were performed on the resulting substrates, revealing outstanding SERS performance. Exploiting this SERS capability, we successfully detected Raman signals from four distinct pesticides: carbendazim, carbaryl, thiram, and thiabendazole. Additionally, we integrated the SERS technique with machine learning algorithms to classify and identify these four pesticide molecules, achieving an accuracy rate exceeding 95% for each algorithm. Consequently, this platform holds promise for applications in the detection of pesticide contamination and other related fields. Raman scattering ; Nanotube ; Raman spectroscopy ; Fabrication ; Composite number ; Process (computing) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanowire Synthesis and Applications
Authors: Yahong Pu (19978297) | Jie Zhang (64655) | Peixuan Li (9721518) | Guolin Wan (17940596) | Jinbo Pan (1671757) | Yan-Fang Zhang (3688987) | Shixuan Du (1511089)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Unveiling theMechanism of Negative Poisson’sRatio in Phosphorus-like 2D MX Materials Driven by Geometric and ElectronicStructures Two-dimensional black-phosphorus-like materials with a re-entrant structure have been reported exhibiting positive or negative Poisson’s ratio (NPR). However, uncovering the underlying geometric–electronic interplay and identifying design principles for NPR materials remain challenging. Using first-principles calculations, we investigate 26 two-dimensional black-phosphorus-like MX monolayers with 10 valence electrons (M = cation, X = anion). Among them, PN, AsN, SbN, AsP, and GeSe exhibit out-of-plane NPR. Geometric structure analysis using machine learning links NPR to the variation of the X–M–X bond angle (θ<sub>M</sub>) and M–X–X–X dihedral angle (φ). Under zigzag (<i>y</i>)-direction strain, a larger reduction in θ<sub>M</sub> and a greater increase in φ are more favorable for NPR formation. Electronic structure analysis attributes the out-of-plane NPR under <i>y</i>-axis strain to the cation–anion <i>p</i><sub><i>z</i></sub> orbital interaction. For NPR materials with the same X element, a higher atomic number of M corresponds to a more negative Poisson’s ratio. This work advances the development of novel materials with unique mechanical behaviors. Zigzag ; Dihedral angle ; Electronic structure ; Valence (chemistry) ; Work (physics) ; Monolayer ; Valence electron 2D Materials and Applications ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: Hajime Kimizuka | Shigenobu Ogata | Bo Thomsen | Motoyuki Shiga
Source: Institutional Repositories DataBase (IRDB) (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Ab initio path-integral simulations of hydrogen-isotope diffusion in face-centred cubic metals Lighter isotopes typically diffuse faster than heavier isotopes; however, the case is not necessarily true for H. Predicting the kinetics of H isotope transport and reactions in substances remains a fundamental challenge in material and condensed matter physics. The peculiar experimentally observed isotope effect on H diffusivities in face-centred cubic (fcc) metals has long been an unresolved problem. Using an ab initio path-integral approach to explore the quantum mechanical nature of both electrons and nuclei, this study successfully predicts H isotope diffusivities in fcc Pd over a wide temperature range. The temperature dependence of the diffusivities follows an unusual ‘reversed-S’ shape on Arrhenius plots. This irregular behaviour, arising from the competition between different nuclear quantum effects (NQEs) with different temperature dependencies, reveals the mechanism of anomalous crossovers between normal and reversed isotope effects. The results illustrate that this phenomenon is common in other fcc metals (e.g. Cu and Ag), where H atoms prefer to occupy octahedral (O) sites. Conversely, in Al, where H atoms prefer to occupy tetrahedral (T) sites, the dependence of H diffusivities on temperature exhibits a familiar ‘C’ shape. A lattice expansion of approximately 1%–2% causes the stable position of H atoms dissolved in Pd to shift from the O to T sites, and H diffusion in expanded Pd is no longer suppressed by NQEs, as observed in Al. This finding has important implications for interpreting kinetic processes involving the crossover from classical to quantum behaviour of H atoms moving between different interstitial sites. Path-integral simulation results describing the approximate quantum dynamics of the Pd–H system, using a machine-learning-based interatomic potential with accuracy similar to the density functional theory calculations, are presented. This computational approach paves the way for elucidating the quantum behaviour of H isotopes in various materials. Kinetic isotope effect ; Ab initio ; Isotope ; Diffusion ; Quantum ; Arrhenius equation ; Kinetic energy ; Atomic diffusion Hydrogen Storage and Materials ; Quantum, superfluid, helium dynamics ; Cold Fusion and Nuclear Reactions
Authors: Weiwei Gao | Fan Zhang | Li Fu | Jijun Zhao | Peihong Zhang
Source: Research Square (preprint, 2025-03-26)
Field: Materials Science — Materials Chemistry

Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information Vacancy defect ; Space (punctuation) ; Computer science ; Physics ; Nanotechnology ; Materials science ; Condensed matter physics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Optical Imaging and Spectroscopy Techniques
Authors: Trevor Bohl | Minasadat Attari | Matthew R. Maschmann | Filiz Bunyak
Source:  (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

A Transformer-Based GAN Architecture for Simulating Carbon Nanotube Structures in the Frequency Domain The design and discovery of new materials with specific properties is a costly and time-consuming process because of the vast search space and the high cost of experimental testing. Simulation and machine learning offer promising solutions to address these challenges. Carbon nanotubes (CNTs) are highly promising nanoscale materials renowned for their exceptional mechanical, electrical, and thermal properties, and have a wide range of potential applications. The properties of CNTs are related to their structural characteristics, which are determined by the growth parameters. In this paper, we present CNT-Former, a generative adversarial network (GAN) using a transformer-based architecture and frequency domain encoding to simulate CNT structures based on given growth parameters. Our ultimate objective is to identify the optimal growth parameters for the desired structures and properties. This approach provides a more scalable alternative to traditional finite element simulators while maintaining high accuracy in simulating CNT structures. Additionally, it allows for the integration of real, multi-modal experimental data, grounding the simulations in actual experimental results. Experimental results demonstrate promising structural fidelity and strong class discrimination capabilities for CNT-Former. Carbon nanotube ; Scalability ; High fidelity ; Process (computing) ; Finite element method ; Encoding (memory) ; Thermal Carbon Nanotubes in Composites ; Electromagnetic Simulation and Numerical Methods ; Electromagnetic Scattering and Analysis
Authors: Monirul Islam
Source: Journal of Sustainable Engineering & Renewable Energy (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Advancing Electronic Structure Modeling for Next Generation Quantum Materials This study presents a reliable and efficient electronic structure modeling framework for next-generation quantum materials, which is directly applicable to practical applications. The proposed hybrid model uses Density Functional Theory, many-body correction, and physics-informed machine learning to accurately predict the band gap, quasiparticle energy, and density of states. The model’s average accuracy compared with experimental data was 0.89, significantly higher than 0.78 for conventional DFT and 0.84 for DFT plus GW. This precision simplifies the selection of suitable materials for quantum computing components, spintronic devices, and topological electronic systems. The study shows that electron correlation contributes about 34 percent, spin-orbit coupling contributes 26 percent, and lattice distortion contributes 21 percent, all of which directly affect device performance. It is possible to reduce the time and cost of experimental research by approximately 40-50% through virtual screening. As a result, this work can serve as a powerful decision-support tool for industrial researchers and policymakers, making a real and sustainable contribution to future quantum technology development. Electronic structure ; Density functional theory ; Quantum ; Quasiparticle ; Coupling (piping) ; Electronic band structure ; Spintronics Machine Learning in Materials Science ; Topological Materials and Phenomena ; Advanced Physical and Chemical Molecular Interactions
Authors: Corin Wagen
Source: ChemRxiv (preprint, 2025-01-24)
Field: Chemistry — Physical and Theoretical Chemistry

Efficient Black-Box Prediction of Hydrogen-Bond-Acceptor Strength Hydrogen-bond-acceptor strength is a critical determinant of physicochemical properties and binding affinity in drug discovery, but computationally predicting the strength of different hydrogen-bond acceptors remains challenging and inaccessible to non-experts. Here, we report a robust black-box workflow for predicting site-specific pKBHX values in organic molecules with minimal computational cost. Our approach begins with rapid conformer generation and optimization with neural network potentials, followed by a single density-functional-theory calculation of the electrostatic potential. We then calibrate these results against an extensive reference set of experimentally determined pKBHX data, achieving sub-0.2 pKBHX-unit accuracy. We illustrate the power of this tool in multiple published drug discovery programs, highlighting how per-site pKBHX tuning can improve bioavailability, minimize efflux, and enhance selectivity Acceptor ; Black box ; Bond strength ; Hydrogen bond ; Bond ; Hydrogen ; Materials science ; Business ; Physics ; Nanotechnology ; Computer science ; Condensed matter physics ; Artificial intelligence ; Finance ; Molecule ; Quantum mechanics Crystallography and molecular interactions ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods
Authors: Fang-Cheng Wang | Qi-Jun Ye | Yucheng Zhu | Xin-Zheng Li
Source: Physical review. B./Physical review. B (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Classification and enumeration of solid-solid phase transition mechanisms A crystal-structure match (CSM), the atom-to-atom correspondence between two crystalline phases, is used extensively to describe solid-solid phase transition (SSPT) mechanisms. However, existing computational methods cannot account for all possible CSMs. Here, we propose a formalism to classify all CSMs into a tree structure, which is independent of the choices of unit cell and supercell. We rigorously proved that only a finite number of noncongruent CSMs are of practical interest. By representing CSMs as integer matrices, we introduce the crystmatch method to exhaustively enumerate them, which uncontroversially solves the CSM optimization problem under any geometric criterion. For most SSPTs, crystmatch can reproduce all known deformation mechanisms and CSMs within 10 CPU minutes, while also revealing thousands of new candidates. The resulting database can be further used for comparing experimental phenomena, high-throughput energy barrier calculations, or machine learning. Enumeration ; Formalism (music) ; Phase transition ; Integer (computer science) ; Tree (set theory) ; Unit circle Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Syamantak Roy | Pritam Dey | Debdeep Pati | Bani K. Mallick
Source: ArXiv.org (preprint, 2025-09-24)
Field: Materials Science — Materials Chemistry

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central to this progress is uncovering scientific laws from experimental data through symbolic regression. However, existing approaches are dominated by heuristic algorithms or data-hungry black-box methods, which often demand low-noise settings and lack principled uncertainty quantification. Motivated by interpretable Statistical Artificial Intelligence, we develop a hierarchical Bayesian framework for symbolic regression that represents scientific laws as ensembles of tree-structured symbolic expressions endowed with a regularized tree prior. This coherent probabilistic formulation enables full posterior inference via an efficient Markov chain Monte Carlo algorithm, yielding a balance between predictive accuracy and structural parsimony. To guide symbolic model selection, we develop a marginal posterior-based criterion adhering to the Occam's window principle and further quantify structural fidelity to ground truth through a tailored expression-distance metric. On the theoretical front, we establish near-minimax rate of Bayesian posterior concentration, providing the first rigorous guarantee in context of symbolic regression. Empirical evaluation demonstrates robust performance of our proposed methodology against state-of-the-art competing modules on a simulated example, a suite of canonical Feynman equations, and single-atom catalysis dataset. Symbolic data analysis ; Probabilistic logic ; Bayesian probability ; Context (archaeology) ; Heuristic ; Bayesian inference ; Inference ; Markov chain Monte Carlo ; Markov chain Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Quantum many-body systems
Authors: Dong Hyun Yoo | Young-Jin Kang | Yoojeong Noh | Daekil In | Jun-su Park | Jung-Hoon Cho
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Estimation of Multivariate Spin-Drying Vibration Distribution Using Synthetic Data with Temporal Correlation Modeling Multivariate statistics ; Vibration ; Correlation ; Distribution (mathematics) ; Estimation ; Statistics ; Biological system ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Acoustics ; Engineering ; Mathematical analysis ; Geometry Spectroscopy and Chemometric Analyses
Authors: Chin‐Kai Chang | Cheng-Yu Tsai
Source: Optical Materials (article, 2025-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling optical spectra of disordered metallic nanostructures with feature-based machine learning Optical spectra ; Feature (linguistics) ; Nanostructure ; Metal ; Spectral line ; Materials science ; Nanotechnology ; Physics ; Artificial intelligence ; Computer science ; Metallurgy ; Quantum mechanics ; Philosophy Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nonlinear Optical Materials Studies
Authors: Prof. Kushal K. Dubey | Devi Amrita Ray | Soumyadarshi Rath | Kajari Chatterjee | Akhilesh Kumar Singh
Source: Coordination Chemistry Reviews (article, 2025-12-31)
Field: Chemistry — Inorganic Chemistry

Challenges and strategies for hydrogen storage and carbon dioxide capture using metal-organic framework: Synergizing experimental and machine learning approach Fossil fuel ; Carbon capture and storage (timeline) ; Energy storage ; Hydrogen storage ; Combustion ; Carbon fibers ; Carbon dioxide ; Greenhouse gas Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Carbon Dioxide Capture Technologies
Authors: Luis F. V. Thomazini | Alexandre Luiz Affonso Fonseca
Source: arXiv (Cornell University) (preprint, 2025-12-28)
Field: Materials Science — Materials Chemistry

A Simple and Efficient Non-DFT-Based Machine Learning Interatomic Potential to Simulate Titanium MXenes Titanium MXenes are two-dimensional inorganic structures composed of titanium and carbon or nitrogen elements, with distinctive electronic, thermal and mechanical properties. Despite the extensive experimental investigation, there is a paucity of computational studies at the level of classical molecular dynamics (MD). As demonstrated in a preceding study, known MD potentials are not capable of fully reproducing the structure and elastic properties of every titanium MXene. In this study, we present a simply trained, but yet efficient, non-density functional theory-based machine learning interatomic potential (MLIP) capable of simulating the structure and elastic properties of titanium MXenes and bulk titanium carbide and nitride with precision comparable to DFT calculations. The training process for the MLIP is delineated herein, in conjunction with a series of dynamical tests. Limitations of the MLIP and steps towards improving its efficacy to simulate titanium MXenes are discussed. MXenes ; Titanium ; Titanium carbide ; Interatomic potential ; Molecular dynamics ; Carbide ; Titanium alloy Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials
Authors: Jyun-Jie Chen | Yisheng Chen | M. Xue | Chia‐Chi Chang | Jeih-weih Hung | Ming-Hsien Li | Ming‐Yu Kuo | Chun-Hung Lin | Yung‐Hui Li | Hsiang Chen | Jung Han
Source: IEEE Sensors Journal (article, 2025-09-16)
Field: Chemistry — Organic Chemistry

Nanoimprinted ZnO Nanograss Grating Structures on Polyethylene Terephthalate Flexible Substrates for Enhanced Broadband Photosensing and Intelligent Light Source Recognition Flexible electronic devices are rapidly advancing, bringing innovative applications to optoelectronic technology. However, traditional optoelectronic devices are limited by rigid substrates, making them unsuitable for flexible applications. Due to its excellent optoelectronic properties and low processing temperature, zinc oxide (ZnO) has become an ideal material for flexible photodetectors (PDs). This study aims to integrate grating-patterned ZnO nanograss onto a flexible polyethylene terephthalate (PET) substrate using nanoimprint lithography (NIL) to enhance the broadband photosensing capabilities of ZnO-based PDs. Experimental results show that NIL technology successfully fabricates uniform and well-structured ZnO nanorod gratings on PET substrates, with a linewidth of approximately 600 nm and a pitch of about 1080 nm. Material analyses indicate that the NIL process, assisted by oxygen plasma etching, increases surface oxygen adsorption on ZnO nanorod surfaces, thereby improving device sensitivity. Notably, photosensing measurements demonstrate that, compared to conventional nonpatterned ZnO nanograss PDs, grating-patterned ZnO nanograss PDs exhibit significantly enhanced photoresponse in the visible light region. Absorption spectrum analysis further confirms that the grating structure effectively improves light absorption in the visible wavelength range. This enhanced photoresponse is attributed to the grating structure, which extends the optical path and increases light absorption by ZnO nanorods. Additionally, this study employs artificial intelligence (AI) deep learning techniques to analyze photosensing data, successfully identifying the corresponding light sources. This work demonstrates the potential of NIL technology in improving the broadband photosensing performance of ZnO-nanograss-based PDs and, in combination with AI deep learning, creates a broadband PDs capable of distinguishing different light sources without requiring optical filters. This research paves the way for future development of flexible optoelectronic devices, with ZnO nanograss PDs showing great potential for advanced light sensing applications. Polyethylene terephthalate ; Nanorod ; Photodetector ; Nanoimprint lithography ; Grating ; Visible spectrum ; Absorption (acoustics) ; Light-emitting diode Polydiacetylene-based materials and applications ; Gas Sensing Nanomaterials and Sensors ; Optical Coatings and Gratings