jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tuğbey Kocabaş | Murat Keçeli | Tanju Gürel | M. V. Miloševıć | Cem Sevik
Source: ArXiv.org (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Limits of MoS$_2$ and MoSe$_2$: Revisiting High-Order Anharmonic Lattice Dynamics with Machine Learning Potentials Group-VI transition metal dichalcogenides (TMDs), MoS$_2$ and MoSe$_2$, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. Yet, their reported lattice thermal conductivities ($κ$) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs) including recently developed foundation models. %MACE-OMAT-0, UMA, and NEP89. We train and benchmark GAP, MACE, NEP, and \textsc{HIPHIVE} against density functional theory (DFT) and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on $κ$. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS$_2$ and MoSe$_2$. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials. Anharmonicity ; Phonon ; Thermal conductivity ; Molecular dynamics ; Bridging (networking) ; Lattice (music) ; Non-equilibrium thermodynamics ; Thermal ; Scattering Thermal properties of materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Dr.Nagabhushan SV
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Bridging Privacy and Utility: An In-Depth Survey of Synthetic Data Generation Methods Generating synthetic data has become an important technique in data science that provides solutions to many challenges such as private data, rare data, and rich information. This research explores the diversity of computing techniques, from artificial intelligence techniques such as artificial neural networks (GANs), generalized instruction tuning and variable auto-encoders (VAEs) to legal rendering, live cloning and data protection technology. An overview of each method is provided and its content, advantages, limitations, and practical applications in various fields are discussed. Through comparative analysis, this article evaluates the advantages and disadvantages of each method and provides insight into their suitability for various applications. It also discusses the challenges and future directions in the development of synthetic materials and provides recommendations to researchers and professionals. This research is important for understanding the state of the art in synthetic materials design and informs future research in this rapidly changing field. Bridging (networking) ; Synthetic data ; Big data ; Data sharing ; Data collection ; Information privacy Machine Learning in Materials Science ; Big Data and Digital Economy ; Internet of Things and AI
Authors: Daisuke Ogawa | Ryotaro Akagi | Keitaro Sodeyama | Y. K. Takahashi
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intrinsic magnetic properties for SmFe&nbsp;12−xTx thin films via high-throughput experiments and machine learning techniques The development of next-generation permanent magnets has become critical due to the limited performance improvements in Nd-Fe-B magnets and concerns over rare earth supply. ThMn12-type rare-earth intermetallic compounds have emerged as promising alternatives, offering superior performance and reduced rare earth content. This study systematically investigates the magnetic properties of Sm(Fe12−xTx)-based thin films synthesized via combinatorial sputtering. Various stabilizing elements (e.g., Ti, V, Co, Cr) were analyzed to explore their effects on phase stability, saturation magnetization (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn12 phase stability for unexplored compositions, enhancing the active learning framework for material discovery. This work highlights the synergy of combinatorial deposition, high-throughput data collection, and ML-assisted prediction in accelerating the exploration of magnetic materials. Future extensions to multi-element systems and other magnetic phases are expected to expedite the discovery of high-performance magnets for motors and energy applications. Thin film ; Support vector machine ; Pattern recognition (psychology) ; Feature (linguistics) ; Process (computing) Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Chemical and Physical Properties of Materials
Authors: Zhenyao Fang | Ting-Wei Hsu | Qimin Yan
Source: ArXiv.org (preprint, 2025-04-01)
Field: Materials Science — Materials Chemistry

Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method The discovery and design of functional materials for energy harvesting and electronic applications require accurate predictions of their optical and transport properties. While several existing databases contain the first-order optical properties and the electron transport properties calculated from high-throughput first-principles calculations, the amount of material entries is often limited and those functional properties are often reported in scalar form. Comprehensive databases for the tensorial properties still remain inadequate, which prevents from capturing the anisotropic effect in materials and the development of advanced machine learning models that incorporate the space group symmetry of materials. Therefore, in this work we present the largest-to-date database of tensorial optical properties (optical conductivity, shift current) and the database of tensorial transport properties (electrical conductivity, thermal conductivity, Seebeck coefficient, thermoelectric figure of merit zT) for 7301 materials, calculated from the Wannier function method. The quality of the Wannier functions were validated by the maximal spread of the Wannier functions and by the comparison with the band structures from first-principles calculations, ensuring the accuracy of the calculated properties. These results contribute to the systematic study the functional properties for diverse materials and can benefit future data-driven discovery of candidate materials for optoelectronic and thermoelectric applications. Wannier function ; Scalar (mathematics) ; Anisotropy ; Thermoelectric effect ; Work (physics) ; Function (biology) ; Seebeck coefficient ; Figure of merit ; Symmetry (geometry) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Pablo
R. Arantes (6263345) | Souvik Sinha (4788888) | Giulia Palermo (760316)
Source: Figshare (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

ParametrizANI:Fast and Accessible Dihedral Parametrizationfor Small Molecules In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing detailed protocols for dihedral parametrization using GAFF and OpenFF force fields. The robust PyTorch-based program, TorchANI, forms the backbone of ParametrizANI, functioning as a benchmark to uphold precision in parametrization tasks. TorchANI plays a pivotal role in enabling ANI (ANAKIN-ME) deep learning models, which are trained to predict potential energy surfaces and related molecular properties with high accuracy. Our work on ParametrizANI goes beyond just creating a tool; it is about building a research-friendly environment, free from the constraints of limited resources. We are committed to democratizing research, enabling teams of all sizes to perform dihedral parametrization with DFT-level accuracy. This tool opens up new possibilities in molecular dynamics and related fields. It marks a significant step forward in improving the scientific community’s ability to parametrize small molecules. For a detailed look at its features, we invite you to check out ParametrizANI on GitHub (https://github.com/palermolab/ParametrizANI). Parametrization (atmospheric modeling) ; Benchmark (surveying) ; Dihedral angle ; Molecular dynamics ; Small molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallography and molecular interactions
Authors: C. Wilson | Montserrat Calvo | Stamatia Zavitsanou | James Somper | Ewa Wieczorek | Tom Watts | Jason Crain | Fernanda Duarte
Source: ChemRxiv (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Kinetic predictions for SN2 reactions using the BERT architecture: Comparison and interpretation. The accurate prediction of reaction rates is an integral step in elucidating reaction mechanisms and designing synthetic pathways. Traditionally, kinetic parameters have been derived from activation energies obtained from quantum mechanical (QM) methods and, more recently, machine learning (ML) approaches. Among ML methods, Bidirectional Encoder Representations from Transformers (BERT), a type of transformer-based model, is the state-of-the-art method for both reaction classification and yield prediction. Despite its success, it has yet to be applied to kinetic prediction. In this work, we developed a BERT model to predict experimental log k values of bimolecular nucleophilic substitution (SN2) reactions and compared its performance to the top-performing Random Forest (RF) literature model in terms of accuracy, training time, and interpretability. Both BERT and RF models exhibit near-experimental accuracy (RMSE ≈ 1.1 log k) on similarity-split test data. Interpretation of the predictions from both models reveals that they successfully identify key reaction centres and reproduce known electronic and steric trends. This analysis also highlights the distinct limitations of each; RF outperformed BERT in identifying aromatic allylic effects, while BERT showed stronger extrapolation capabilities. Interpretation (philosophy) ; Architecture ; SN2 reaction ; Kinetic energy ; Computer science ; History ; Physics ; Chemistry ; Programming language ; Archaeology ; Stereochemistry Machine Learning in Materials Science
Authors: Okechukwu Dominic Onukwuli | Ifeanyi John Obibuenyi | Linda Nnodi | Uchenna Luvia Ezeamaku | Joseph Okechukwu Ezeugo | Monday Omotioma
Source: International Journal of Advanced Engineering and Management Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Investigating Inhibitory Actions of Expired Omeprazole in Sulphuric Acid as Corrosion Resistance of Mild-steel It is proposed to investigate the inhibitory actions of omeprazole in acid environments as corrosion resistance of mild-steel. This omeprazole was characterized for its functional groups and chemical constituents using Fourier transform infra-red spectroscopy and gas chromatography. Experimental techniques and gravimetric methods were also employed. The inhibitory effects of the drug were assessed and efficiencies optimized using response surface methodology RSM, and artificial neural network ANN, models. The predominant functional groups found were C-H, C=H, C=O, C- F, and C=C stretches; C=N and N=O stretching; and NH deformation, and contains theophylline, n-hexa-decanoic acid, di-n-octyl phthalate and methyl tetra-triacontyl ether. The Gibbs free energy results were negative, indicating the flow of heat from the inhibitor-mild-steel interface at a seemingly higher temperature to the surroundings at a lower temperature. The adsorption of the inhibitor molecules was physical and not chemisorption. The Frumkin isotherm provided the best fit and optimum efficiency of 93.71% was attained. The ANN yielded better optimization results because it had a higher value of R2 and lower values of root-mean-square-error RMSE and standard error of prediction SEP. The impedance method displayed a capacitive loop, signifying charge-transfer process-controlled, and polarization measurements showed that the drug was a mixed-type inhibitor. Available studies have not investigated inhibitory actions of Omeprazole in acidic environments. Hence, this study aims to fill this gap and has proved Omeprazole as an excellent inhibitor for controlling mild-steel corrosion in sulphuric acid media. Omeprazole ; Corrosion ; Inhibitory postsynaptic potential ; Chemistry ; Metallurgy ; Pharmacology ; Medicine ; Materials science ; Internal medicine Corrosion Behavior and Inhibition
Authors: Sungwoo Kang
Source: ArXiv.org (preprint, 2025-05-02)
Field: Materials Science — Materials Chemistry

Coarse-grained graph architectures for all-atom force predictions We introduce a machine-learning framework termed coarse-grained all-atom force field (CGAA-FF), which incorporates coarse-grained message passing within an all-atom force field using equivariant nature of graph models. The CGAA-FF model employs grain embedding to encode atomistic coordinates into nodes representing grains rather than individual atoms, enabling predictions of both grain-level energies and atom-level forces. Tested on organic electrolytes, CGAA-FF achieves root-mean-square errors of 4.96 meV atom-1 for energy and 0.201 eV A-1 for force predictions. CGAA-FF significantly reduces computational costs, achieving about 22- and 14-fold improvements in simulation speed and memory efficiency, respectively, compared to the all-atom potential (SevenNet-0). Since this CGAA framework can be integrated into any equivariant architecture, we believe this work opens the door to efficient all-atom simulations of soft-matter systems. Embedding ; Force field (fiction) ; ENCODE ; Equivariant map ; Graph ; Work (physics) ; Field (mathematics) ; Energy (signal processing) Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Boron and Carbon Nanomaterials Research
Authors: Sirui Guo | Jinchang Li | Wei Jiang | Jun Yang | Yingying Du | Tao Luo | A.M. Shahed Anwar | Limei Qi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Chirality Recognition of Amino Acid by Combining Machine Learning Method and Sliding Window Technique Sliding window protocol ; Chirality (physics) ; Artificial intelligence ; Window (computing) ; Pattern recognition (psychology) ; Computer science ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Mohammad Rizehbandi | Hadise Nobakht | Neda Jannesar | Nariman Yousefi
Source: Computational and Theoretical Chemistry (article, 2025-12-20)
Field: Materials Science — Biomaterials

Computational investigation of curcumin–polymer interactions: a DFT–ML integrated framework for electronic structure and biodegradation analysis of PGA and PLA complexes Biocompatibility ; Biodegradation ; Polymer ; Rational design ; Electronic structure ; Density functional theory ; Degradation (telecommunications) ; Range (aeronautics) biodegradable polymer synthesis and properties ; Biopolymer Synthesis and Applications ; Advanced Cellulose Research Studies
Authors: Nikolay Tatus’ | Olga Volkova
Source: Springer Link (Chiba Institute of Technology) (article, 2025-11-14)
Field: Materials Science — General Materials Science

Comparative evaluation of approaches to determining biofibres characteristics: From mechanical tests to machine learning This article is devoted to a comparative analysis of methods for determining the characteristics of biofibers, a key component of modern environmentally friendly composites. The aim of the work is to systematize and compare approaches to the study of biofibers: from mechanical testing and microstructural analysis to digital image processing and machine learning methods. Particular attention is paid to the relationship between fiber characteristics and composite properties. As a result, a classification of methods has been developed, recommendations for their selection for different types of biofibers have been formulated, and promising areas of research have been identified. The analysis has made it possible to identify the optimal combinations of methods for effectively predicting the properties of composite materials. Component (thermodynamics) ; Key (lock) ; Selection (genetic algorithm) ; Feature selection ; Work (physics) Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Engineering and Agricultural Innovations
Authors: Gururaj Pejavara Narayana | Dhananjay Kumar Singh | Mehul Hirabhai Chudasama | Shweta Deotale | Nalla Bhanu Prakash Reddy | Perumal Thivya
Source: Bioresource Technology Reports (article, 2025-11-29)
Field: Materials Science — Biomaterials

Intelligent packaging of functional foods: A comprehensive review of its advances, recyclability, and regulatory frameworks Active packaging ; Supply chain ; Transparency (behavior) ; Quality (philosophy) ; Sustainability ; Functional requirement ; Product (mathematics) ; Food spoilage Nanocomposite Films for Food Packaging ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Carlos Deleon | Dmitri Gavrilov | Peggy A. O’Day | Ajith Pattammattel
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Autonomous X-ray Fluorescence Mapping of Chemically Heterogeneous Systems via a Correlative Feature Detection Framework We present X-AutoMap, a modular framework for autonomous X-ray fluorescence (XRF) mapping that enables chemically informed targeting of regions of interest through a correlative feature detection strategy. The system integrates classical computer vision and rule-based logic to identify features based on spatial relationships across multiple elemental maps, rather than relying solely on intensity or morphology. Tight integration with the Bluesky control infrastructure at the NSLS-II Hard X-ray Nanoprobe (HXN) beamline enables real-time, closed-loop scan orchestration. Applied to a chemically heterogeneous urban PM2.5 sample, X-AutoMap reduced high-resolution acquisition time from over 44 hours to approximately 10 hours by targeting compositionally significant features identified from coarse scans. High-resolution results revealed diverse particle types, including fully mixed, partially overlapping, and spatially distinct multi-element structures, demonstrating the ability of the framework to isolate chemically relevant features with minimal user intervention. The framework supports interactive and autonomous modes, operates within hardware constraints via grid-based scanning, and is robust across varying sample conditions. Future extensions will incorporate machine learning and probabilistic sampling to further improve detection sensitivity and scan efficiency. X-AutoMap is currently in active use at HXN and provides a flexible foundation for scalable, intelligent imaging workflows at synchrotron beamlines. Workflow ; Modular design ; Correlative ; Feature (linguistics) ; Probabilistic logic ; Pipeline (software) ; Pattern recognition (psychology) Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Grenioux, Louis | Galliano, Leonardo | Berthier, Ludovic | Biroli, Giulio | Gabrié, Marylou
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Riemannian Stochastic Interpolants for Amorphous Particle Systems Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials (glasses), which are disordered particle systems lacking atomic periodicity. Sampling equilibrium configurations of glass-forming materials is a notoriously slow and difficult task. This obstacle could be overcome by developing a generative framework capable of producing equilibrium configurations with well-defined likelihoods. In this work, we address this challenge by leveraging an equivariant Riemannian stochastic interpolation framework which combines Riemannian stochastic interpolant and equivariant flow matching. Our method rigorously incorporates periodic boundary conditions and the symmetries of multi-component particle systems, adapting an equivariant graph neural network to operate directly on the torus. Our numerical experiments on model amorphous systems demonstrate that enforcing geometric and symmetry constraints significantly improves generative performance. Equivariant map ; Boundary (topology) ; Interpolation (computer graphics) ; Homogeneous space ; Amorphous solid ; Particle system ; Boundary value problem ; Graph Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Mack Cleveland | Andrew Nelson | Ericmoore Jossou
Source: Nuclear Engineering and Design (review, 2025-12-01)
Field: Materials Science — Materials Chemistry

Chromium-doped uranium dioxide fuels: A review Uranium dioxide ; Eutectic system ; Uranium ; Microstructure ; Sintering ; Nuclear fuel ; Adsorption ; Doping ; Grain growth ; Grain size Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing