jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nitant Gupta | Sri V.V.R. Akepati | Simona Bianco | Jilan Shah | Dave J. Adams | Arthi Jayaraman
Source: ArXiv.org (preprint, 2025-04-04)
Field: Materials Science — Biomaterials

CREASE-2D Analysis of Small Angle X-ray Scattering Data from Supramolecular Dipeptide Systems In this paper, we extend a recently developed machine-learning (ML) based CREASE-2D method to analyze the entire two-dimensional (2D) scattering pattern obtained from small angle X-ray scattering measurements of supramolecular dipeptide micellar systems. Traditional analysis of such scattering data would involve use of approximate or incorrect analytical models to fit to azimuthally-averaged 1D scattering patterns that can miss the anisotropic arrangements. Analysis of the 2D scattering profiles of such micellar solutions using CREASE-2D allows us to understand both isotropic and anisotropic structural arrangements that are present in these systems of assembled dipeptides in water and in the presence of added solvents/salts. CREASE-2D outputs distributions of relevant structural features including ones that cannot be identified with existing analytical models (e.g., assembled tubes, cross-sectional eccentricity, tortuosity, orientational order). The representative three-dimensional (3D) real-space structures for the optimized values of these structural features further facilitate visualization of the structures. Through this detailed interpretation of these 2D SAXS profiles we are able to characterize the shapes of the assembled tube structures as a function of dipeptide chemistry, solution conditions with varying salts and solvents, and relative concentrations of all components. This paper demonstrates how CREASE-2D analysis of entire SAXS profiles can provide an unprecedented level of understanding of structural arrangements which has not been possible through traditional analytical model fits to the 1D SAXS data. Small-angle X-ray scattering ; Scattering ; Small-angle scattering ; Isotropy ; Dipeptide ; Anisotropy Supramolecular Self-Assembly in Materials ; Protein Structure and Dynamics ; Chemical Synthesis and Analysis
Authors: Linfei Li | Jie Zhou | Rong Yu | Xiumin Chen | Yunmin Chen | Kangming Wu | Zhicheng Gao | Guihua Zhu | Wei Jiang
Source: Journal of Molecular Liquids (article, 2025-11-30)
Field: Chemistry — Inorganic Chemistry

Study on the forms and volatilization behavior of hydrogen fluoride in phosphoric acid with deep potential molecular dynamics (DPMD) Phosphoric acid ; Hydrogen fluoride ; Hydrogen bond ; Hydrogen ; Fluoride ; Molecular dynamics Inorganic Fluorides and Related Compounds ; Fluoride Effects and Removal ; Crystallography and molecular interactions
Authors: Xianyong Shen (22193038) | Shirui Ma (22193041) | Chuncan Song (22193044) | Yuezhen He (3108567) | Maoguo Li (4482988)
Source: Figshare (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Kinetic andMechanistic Discrepancies of Single/Dual-AtomNanozymes Drive a Triple-Channel Sensing Array for Machine Learning-AssistedAntioxidant Discrimination Current colorimetric sensing arrays for antioxidant detection often struggle with discrimination due to cross-reactive signals from individual nanozymes. These signals are typically modulated by external factors such as pH or chromogenic substrates, offering limited kinetic and mechanistic diversity. To overcome this, we present a novel triple-channel colorimetric sensing array utilizing two distinct single-atom nanozymes (Cu SA and Fe SA) and one dual-atom nanozyme (CuFe DA). Our approach leverages the inherent kinetic and mechanistic differences between these atomically dispersed catalysts. We observed measurably distinct oxidase-like activities through variations in their Michaelis–Menten constants (Km<i>Km</i>) and specific activities. Furthermore, detailed mechanistic studies revealed differences in their active metal sites, leading to varied reactive oxygen species (ROS) production. This intrinsic functional divergence creates unique “fingerprint” responses for improved antioxidant differentiation. When integrated with machine learning algorithms (Principal Component Analysis and Hierarchical Cluster Analysis), our array successfully identified and quantified six common antioxidants: ascorbic acid, glutathione, cysteine, tea polyphenol, gallic acid, and tannin. The array exhibited excellent sensitivity, with a low detection limit of 2.01 μM for cysteine. This research offers a robust strategy for developing high-performance sensing arrays by exploiting fundamental atomic-scale kinetic and mechanistic variations, holding significant promise for food safety and health monitoring applications. Ascorbic acid ; Antioxidant ; Chromogenic ; Substrate (aquarium) ; Kinetic energy ; Gallic acid ; Detection limit Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Mohammad Moradi | Azadeh Ebrahimian Pirbazari | Fatemeh Esmaeili Khalil Saraei | Amin Esmaeili | Ali Ebrahimian Pirbazari | Ali Derakhshesh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Agbr Decorated Tio2@Ceo2 Core-Shell Structure for Efficient Degradation of Pharmaceutical Pollutant: Performance, Degradation Pathway, and Machine Learning Estimation Degradation (telecommunications) ; Pollutant ; Shell (structure) ; Computer science ; Chemistry ; Chemical engineering ; Materials science ; Engineering ; Composite material ; Organic chemistry ; Telecommunications Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Gas Sensing Nanomaterials and Sensors
Authors: Jonathan E. Moussa (22177786)
Source: Figshare (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

The Enduring Relevance of Semiempirical Quantum Mechanics The development of semiempirical models to simplify quantum mechanical descriptions of atomistic systems is a practice that started soon after the discovery of quantum mechanics and continues to the present day. There are now many methods for atomistic simulation with many software implementations and many users on a scale large enough to be considered as a software market. Semiempirical models occupied a large share of this market in its early days, but research activity in atomistic simulation has steadily polarized over the last three decades toward general-purpose but expensive <i>ab initio</i> quantum mechanics methods and fast but special-purpose molecular mechanics methods. I offer a perspective on recent trends in atomistic simulation from the middle ground of semiempirical modeling, to learn from its past success and consider its possible paths to future growth. In particular, there is a lot of ongoing research activity in combining semiempirical quantum mechanics with machine learning models and some unrealized possibilities for tighter integration between <i>ab initio</i> and semiempirical quantum mechanics with more flexible theoretical frameworks and more modular software components. Relevance (law) ; Quantum ; Perspective (graphical) ; Modular design ; Software Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Manas Kumar Nayak | Barun Haldar | Hillol Joardar | T. Badapanda | Subhashree Sahoo | Nashmi H. Alrasheedi | Ahmed Abdala
Source: Ceramics International (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Machine learning assisted electrocaloric and pyroelectric performance of Ba0.85Ca0.15Ti0.9Zr0.1O3 ceramic for solid state refrigeration Materials science ; Ceramic ; Pyroelectricity ; Refrigeration ; Solid-state ; Chemical engineering ; Composite material ; Process engineering ; Mechanical engineering ; Engineering physics ; Optoelectronics ; Ferroelectricity ; Dielectric Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials
Authors: Qi Li | J. Zhao | Na Lin | Xiufeng Cheng | Xian Zhao | Zhaojun Liu | Zhitai Jia | Mengyuan Hua
Source: ArXiv.org (preprint, 2025-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Edge-Dependent Step-Flow Growth Mechanism in $β$-Ga$_{2}$O$_{3}$ (100) Facet at the Atomic Level Homoepitaxial step-flow growth of high-quality $β$-Ga$_{2}$O$_{3}$ thin films is essential for the advancement of high-performance Ga$_{2}$O$_{3}$-based devices. In this work, the step-flow growth mechanism of $β$-Ga$_{2}$O$_{3}$ (100) facet is explored by machine-learning molecular dynamics simulations and density functional theory calculations. Our results reveal that Ga adatoms and Ga-O adatom pairs, with their high mobility, are the primary atomic species responsible for efficient surface migration on the (100) facet. The asymmetric monoclinic structure of $β$-Ga$_{2}$O$_{3}$ induces a distinct two-stage Ehrlich-Schwoebel barrier for Ga adatoms at the [00$\overline{1}$] step edge, contributing to the suppression of double-step and hillock formation. Furthermore, a miscut towards [00$\overline{1}$] does not induce the nucleation of stable twin boundaries, whereas a miscut towards [001] leads to the spontaneous formation of twin boundaries. This research provides meaningful insights not only for high-quality $β$-Ga$_{2}$O$_{3}$ homoepitaxy but also the step-flow growth mechanism of other similar systems. Enhanced Data Rates for GSM Evolution ; Facet (psychology) ; Mechanism (biology) ; BETA (programming language) ; Flow (mathematics) ; Chemistry ; Crystallography ; Physics ; Geometry ; Mathematics ; Computer science ; Psychology ; Social psychology ; Quantum mechanics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Chenxin Zhang (3374429) | Yanyan Chen (527430) | Qian Wang (32718) | Puru Jena (1282011)
Source: Figshare (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Phonon Localizationand a Boson-Peak-Like Anomalyin Twisted Penta-PdSe<sub>2</sub> Bilayer With the emergence of twistronics, twisting has been employed to manipulate phonon–phonon interactions in 2D-layered systems. However, due to the complexity of twisted structures, twisting-induced phonon localization and Boson-peak-like (BP-like) anomalies have not yet been reported. In this work, using machine-learning-supported molecular dynamics, we study the impact of interlayer twisting on the phonon properties in bilayer pentagonal PdSe<sub>2</sub> (penta-PdSe<sub>2</sub>) with multiple twist angles. We find that twisting reduces the in-plane thermal conductivity by up to 78.62%, while significantly enhancing the phonon coherent thermal conductivity contribution from 21.75% to 85.40%. Furthermore, by analyzing the vibrational density of states and localized shear strain distribution, we demonstrate that twisting introduces the inhomogeneous shear modulus distribution, causing phonon localization and a BP-like anomaly in twisted penta-PdSe<sub>2</sub> bilayers. These findings show that twisting becomes a new variable for producing a BP-like anomaly, different from the conventional strategies by using disorder, defects, or doping. Phonon ; Bilayer ; Thermal conductivity ; Twist ; Anomaly (physics) ; Shear modulus ; Shear (geology) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Seokhwan Min | Jonghwa Shin
Source: Computer Physics Communications (article, 2025-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Eschallot: A topology nucleation algorithm for designing stratified, spherically symmetric systems that exhibit complex angular scattering of electromagnetic waves Topology (electrical circuits) ; Topology optimization ; Scattering ; Mie scattering ; Square (algebra) ; Matrix (chemical analysis) ; Scattering theory ; Network topology Metamaterials and Metasurfaces Applications ; Topology Optimization in Engineering ; Orbital Angular Momentum in Optics
Authors: Desai, Mahek | Quinn, Rowena | Asadinia, Marjan
Source: arXiv (Cornell University) (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

ML-PCM : Machine Learning Technique for Write Optimization in Phase Change Memory (PCM) As transistor-based memory technologies like dynamic random access memory (DRAM) approach their scalability limits, the need to explore alternative storage solutions becomes increasingly urgent. Phase-change memory (PCM) has gained attention as a promising option due to its scalability, fast access speeds, and zero leakage power compared to conventional memory systems. However, despite these advantages, PCM faces several challenges that impede its broader adoption, particularly its limited lifespan due to material degradation during write operations, as well as the high energy demands of these processes. For PCM to become a viable storage alternative, enhancing its endurance and reducing the energy required for write operations are essential. This paper proposes the use of a neural network (NN) model to predict critical parameters such as write latency, energy consumption, and endurance by monitoring real-time operating conditions and device characteristics. These predictions are key to improving PCM performance and identifying optimal write settings, making PCM a more practical and efficient option for data storage in applications with frequent write operations. Our approach leads to significant improvements, with NN predictions achieving a Mean Absolute Percentage Error (MAPE) of 0.0073% for endurance, 0.23% for total write latency, and 4.92% for total write energy. Phase-change memory ; Scalability ; Artificial neural network ; Key (lock) ; Dynamic random-access memory ; Power (physics) ; Energy (signal processing) ; Efficient energy use Phase-change materials and chalcogenides ; Parallel Computing and Optimization Techniques ; Advanced Data Storage Technologies
Authors: Huan Li (120482) | Junjie Leng (22172056) | Cailong Zhou (3727750) | Hao Wu (65943) | Lichun Dong (466119) | Zemin Feng (8862197)
Source: Figshare (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Machine Learning-AssistedHigh-Throughput Screeningof Covalent Organic Frameworks for Propane/Propylene Adsorption-Separation A high-throughput computational screening (HTCS) approach for identifying covalent organic frameworks (COFs) for C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> adsorption-separation was developed by combining machine learning (ML) and grand canonical Monte Carlo (GCMC) simulation. COFs from two experimental data sets were first evaluated using GCMC simulation to measure the performance metrics of C<sub>3</sub>H<sub>8</sub> adsorption capacity and C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> selectivity and to extract 28 geometric and chemical feature descriptors. The obtained values for the feature descriptors and performance metrics were used to train various ML models. The best-performing interpretable ML model was determined by using the AutoML method. This selected ML model was used to screen potential COF candidates from a hypothetical data set. The most promising COFs were then validated and analyzed using GCMC simulation, revealing that gravimetric surface area and void fraction (ϕ) are the two primary factors affecting the C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> adsorption-separation performance of COFs, and that the extra trees regressor model exhibits the highest accuracy in predicting COF performance. By using the developed HTCS approach, 20 top-performing COFs with strong adsorption-separation efficiency and regenerability were identified, and most of them are 3D COFs with qdl, clh, and qzl topologies. Finally, 4 of the top-performing COFs were experimentally synthesized, with the best achieving a C<sub>3</sub>H<sub>8</sub> uptake of 60.5 mL g<sup>–1</sup> and a separation factor of 3.67. This study demonstrates that the ML-assisted HTCS approach can enable the efficient identification of high-performance COFs for C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> separation and establish a generalizable framework for high-throughput screening of functional materials. Adsorption ; Covalent bond ; Measure (data warehouse) ; Feature (linguistics) ; Gravimetric analysis ; Selectivity ; Pattern recognition (psychology) ; Identification (biology) Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Pan Zhang | Fang Lyu | Shiwei Chen | Yong Liu | Ziyu Wang | Zhihong Lu | Rui Xiong | Shiheng Liang
Source: Physical review. B./Physical review. B (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Anomalous suppression of thermal conductivity in SnSe induced by aliovalent doping As a universally employed strategy for suppressing phonon propagation, element doping plays a critical role in reducing lattice thermal conductivity ${\ensuremath{\kappa}}_{L}$ of thermoelectric materials. Traditionally, heavy-element doping with a substantial atomic mass/radius difference leads to a pronounced ${\ensuremath{\kappa}}_{L}$ reduction through the phonon velocity attenuation and phonon scattering enhancement. In this study, using accurate neural network potentials combined with the phonon Boltzmann transport equation, we systematically investigate the comparative impacts of isoelectronic (Ge, Pb) and aliovalent (Ag) doping on phonon transport in SnSe. Counterintuitively, despite the fact that Pb has a heavier atomic mass and exhibits a greater mismatch in both mass and radius compared to Sn, aliovalent Ag doping achieves superior ${\ensuremath{\kappa}}_{L}$ suppression than isoelectronic Pb doping. While the softening of acoustic and optical phonons, resulting, respectively, from weakened bond strength and enhanced electron screening, contributes to some reduction in phonon group velocity of Ag-doped SnSe, it is not the main mechanism. The anomalous suppression of ${\ensuremath{\kappa}}_{L}$ is primarily attributed to the enhanced coupling between the acoustic and optical phonon branches. Further results reveal that the p-d antibonding interaction between $\mathrm{Se}\text{\ensuremath{-}}4p$ and $\mathrm{Ag}\text{\ensuremath{-}}4d$ orbitals induces the weakening of bond strength, thereby causing the downward shift of low-frequency optical branches and thus enhancing the coupling effect between acoustic and optical branches. This work deepens our understanding of the phonon transport mechanism in aliovalent-doped materials and provides theoretical guidance for experimentally reducing the ${\ensuremath{\kappa}}_{L}$ through doping strategies. Phonon ; Doping ; Thermal conductivity ; Effective mass (spring–mass system) ; Antibonding molecular orbital ; Scattering ; Atomic mass ; Phonon scattering ; Coupling (piping) Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Phase-change materials and chalcogenides
Authors: Vaishnavi Bhardwaj
Source:  (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Hybrid Quantum Kernel Support Vector Regression for Bandgap Prediction in Halide Perovskites Predicting the electronic bandgap (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{g}$</tex>) of hybrid organic-inorganic halide perovskites (HOIPs) remain a fundamental challenge in the design of high-efficiency solar cells and light-emitting diodes. While classical machine learning (ML) methods have achieved competitive accuracy, their reliance on fixed kernel spaces limits their ability to capture the intricate quantum correlations underlying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{g}$</tex> variations. To address this limitation, we propose a Hybrid Quantum Kernel Support Vector Regression (QKSVR) framework that integrates quantum feature encoding with classical regression. The model employs an optimized 3-qubit, single-layer quantum feature map constructed using RY rotations, T-gates for enhanced nonlinearity, and CZ entanglement for quantum correlation mapping. This design embeds compositional descriptors into a high-dimensional Hilbert space, enabling the model to learn electronic structure dependencies inaccessible to classical kernels. Feature selection was guided through the Kernel-Target Alignment (KTA) metric, a quantum-native criterion that identified the optimal descriptor subset (Cs, I, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex>) yielding a peak KTA of 0.8337. The resulting QKSVR achieved an outstanding predictive performance with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.9806$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{RMSE}=0.0618 \text{eV}$</tex> on the benchmark perovskite dataset (10.1007/s40033-023-00553-Z)[5]. Remarkably, this accuracy was achieved using only three chemical descriptors, underscoring the efficiency of quantum feature representations. The proposed KTA-driven hybrid modelling paradigm demonstrates the potential of quantum kernels in advancing materials informatics by revealing complex structure-property relationships beyond the reach of conventional ML models. Support vector machine ; Kernel (algebra) ; Quantum ; Feature (linguistics) ; Quantum entanglement ; Benchmark (surveying) ; Feature selection ; Band gap ; Hilbert space Machine Learning in Materials Science ; Perovskite Materials and Applications ; Organic Electronics and Photovoltaics