jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pawan Whig | Imran Ahmed Khan | Amrita Rai | Owais Ahmad Shah | M. Nasim Faruque | Jaishanker Prasad Keshari | Mudit Wadhwa
Source: International Journal of Computational Vision and Robotics (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Predictive modelling of carbon nanotube structures using machine learning techniques This paper explores predictive modelling techniques applied to the structural analysis of carbon nanotubes (CNTs) using a dataset encompassing 10,721 initial and calculated atomic coordinates, alongside their intricate chiral networks. Derived from simulation software, BIOVIA materials studio CASTEP, this dataset serves as the foundation for employing advanced machine learning methodologies. Our research aims to decode the nuanced complexities inherent in CNT structures. By leveraging cutting-edge machine learning approaches, we seek to revolutionise the understanding and predictive capabilities regarding CNT architectures. The significance of our findings resonates deeply within materials science and nanotechnology, promising to streamline the comprehension and utilisation of CNTs across diverse domains, spanning electronics to materials engineering. This study marks a pivotal stride towards automating and expediting the development of nanomaterials, fostering innovation in a field where precision and efficiency are paramount. Our work showcases the potential for transformative advancements in harnessing CNTs for practical applications, propelling the integration of these nanostructures into real-world technologies. Transformative learning ; Field (mathematics) ; Expediting ; Carbon nanotube ; Bespoke ; Terabyte ; Deep learning ; Materials informatics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; X-ray Diffraction in Crystallography
Authors: Armaghan Shalbaftabar | Kristen Rhinehardt | D. Kumar
Source: American Journal of Engineering and Applied Sciences (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Analyzing Temperature-Dependent Thermal Properties of Biomaterials Using Machine Learning Methods This research utilizes machine learning models ANN, Random Forest, and Decision Tree to predict material properties of Ti-based biomaterials, including Young&rsquo;s modulus, density, thermal conductivity, and specific heat at various temperatures. Data was sourced using web scraping and Plot Digitizer, validated against literature, and analyzed in Excel. The ANN model achieved strong performance, with R&sup2; = 0.980874 for TiAl and R&sup2; = 0.997607 for TiCu, effectively predicting density and Young&rsquo;s modulus but showing deviations in band gap. For TiO2, the ANN model demonstrated solid predictions but struggled with band gap and specific heat accuracy. Random Forest yielded high accuracy for TiAl (R&sup2; = 0.998168) and TiO2 (R&sup2; = 0.9994) and its ability to generalize well and capture complex relationships in the data makes it the most reliable method for this study. The Decision Tree model accurately predicted specific heat and Young&rsquo;s modulus for TiAl (R&sup2; = 0.993841) and captured trends in TiCu but showed deviations in band gap and thermal conductivity. These results underline the predictive potential of these models while highlighting areas for refinement. Decision tree ; Random forest ; Thermal ; Specific heat ; Tree (set theory) ; Modulus ; Predictive modelling ; Plot (graphics) Thermal properties of materials ; Machine Learning in Materials Science ; Infrared Thermography in Medicine
Authors: Leng Ze Tang | Trupti Mohanty | Sterling G. Baird | Leonard W. T. Ng | Taylor D. Sparks
Source: ChemRxiv (preprint, 2025-09-03)
Field: Materials Science — Materials Chemistry

Multi-task Attention for Doped Thermoelectric Properties Prediction Improving the performance of thermoelectric (TE) materials is essential for their wider adoption in sustainable energy and cooling applications. Impurity doping is a common strategy for enhancing transport properties, yet synthesizing every possible TE composition is infeasible, and high-fidelity ab initio simulations remains computationally expensive. Machine learning (ML) offers a viable alternative by learning a cheaper surrogate model, but due to the compositional similarity between doped and undoped materials, conventional approaches often struggle to capture the diverse doping effects. Addressing this challenge, we adapt the existing Compositionally restricted attention-based Network (CrabNet) into a multi-task variant, MT CrabNet, which uses self-attention to implicitly learn composition-specific dopant effects. Multi-task learning enables simultaneous predictions of seven TE transport properties, leveraging their interdependence to enhance performance. We also explore different strategies for encoding external variables such as the temperature. Trained on an experimental dataset covering over 1,400 unique compositions, MT CrabNet outperforms baseline models by up to 1.5 times in terms of R2. This establishes MT CrabNet as a useful tool for accelerating the screening and discovery of high-performance TE materials, with a design extensible to doped systems beyond the TE domain. Thermoelectric effect ; Task (project management) ; Doping ; Materials science ; Thermoelectric materials ; Computer science ; Engineering physics ; Optoelectronics ; Physics ; Engineering ; Systems engineering ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Armin Aminimajd | João M. Maia | Abhinendra Singh
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Robust Prediction of Frictional Contact Network in Near-Jamming Suspensions Employing Deep Graph Neural Networks The viscosity of the suspension consisting of fine particles dispersed in a Newtonian liquid diverges close to the jamming packing fraction. The contact microstructure in suspensions governs this macroscopic behavior in the vicinity of jamming through a frictional contact network (FCN). FCN is composed of mechanical load-bearing contacts that lead to the emergence of rigidity near the jamming transition. The stress transmission and network topology, in turn, depend sensitively on constraints on the relative motion of the particles. Despite their significance, predicting the FCN, especially close to jamming conditions, remains challenging due to experimental and computational impediments. This study introduces a cost-effective machine learning approach to predict the FCN using a graph neural network (GNN), which inherently captures hidden features and underlying patterns in dense suspension by mapping interparticle interactions. Employing a variation of GNN called the Deep Graph Convolutional Network (DeepGCN) trained on data-driven simulations, this study demonstrates robust generalization and extrapolation capabilities, accurately predicting FCNs in systems with divergent flow parameters and phase spaces, despite each being trained exclusively on a single condition. The study covers a wide range of phase space, from semi-dilute to jammed states, spanning transient to steady states, while systematically varying parameters such as shear stress ($σ_{xy}$), packing fraction($ϕ$) and sliding and rolling friction (${μ_s, μ_r}$). The results of this research pave the way for innovative transferable techniques in predicting the properties of particulate systems, offering new avenues for advancement in material science and related fields. Jamming ; Rheology ; Contact dynamics ; Extrapolation ; Artificial neural network ; Viscosity ; Phase (matter) ; Graph ; Convolutional neural network Material Dynamics and Properties ; Model Reduction and Neural Networks ; Advanced Sensor and Energy Harvesting Materials
Authors: Jyoti Prakash Das | Vempuluru Navakoteswara Rao | Sang-Jae Kim
Source: Progress in Materials Science (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Advances in structural engineering and electrochemical insights of MXene-based derivates for next generation micro-supercapacitor with tuneable ink, microelectrode design, and scalable manufacturing strategies Characterization (materials science) ; Electrode ; Electrochemical energy storage ; Electrochemistry ; Capacitance ; Raman spectroscopy ; Synchrotron ; Scalability MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Varad Daoo | Jayant K. Singh
Source: Advanced Intelligent Discovery (article, 2025-09-02)
Field: Chemistry — Inorganic Chemistry

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation Supervised machine learning (SML) has woven itself into the very fabric of material discovery, as it offers computationally cheaper ways of correlating the structure of a material with its properties, which, in SML's absence, would require high fidelity, resource intensive first principles calculations. The performance of SML models is strongly influenced by the quantity of available training data. In general, an increase in the amount of training data leads to an improvement in model accuracy. When adequately trained, these SML models act as effective low fidelity surrogate models for accelerating materials discovery, aligning with the broader objective of computational materials science, which is the identification of high‐performing materials for a variety of target applications. In this work, we recognize the importance of data driven model accuracy and introduce a novel framework for constructing SML models aimed at identifying top performing materials for gas separation applications. Our approach embraces the challenge of data scarcity, seeking to discover as many high performing candidates as possible while relying on minimal training data. We demonstrate that our iterative framework for building SML models reduces the required training dataset to only 5%–10% of the total data, while successfully identifying up to 97 of the top 100 best performing materials. Furthermore, we show that this framework is weakly SML model dependent, exhibiting minimal dependence on the specific target property under investigation. Leveraging this innovative approach, we identify top performing candidates for three industry relevant gas separations in multiple metal organic framework databases, thereby highlighting the robustness and general applicability of our workflow. Separation (statistics) ; Computer science ; Data science ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Raheel Hammad | Soumya Ghosh
Source: ChemRxiv (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Predicting the binding sites of alkali atoms on transition metal dichalcogenides with a machine learning model based on electrostatics Finding stable binding sites of alkali metal ions on two-dimensional transition metal dichalcogenides (TMDs) is crucial for predicting and engineering the usage of these materials in batteries and optoelectronic devices. However, conventional approaches using density functional theory (DFT), where the energies of the intercalated layers are analyzed upon sequential addition of each ion, face significant challenges due to the substantial number of calculations involved. Alternatively, one can employ point charge analysis to predict the most favorable binding sites. In this study, we first show that such analyses cannot be extended to arbitrary concentration of intercalated ions. Furthermore, we compare the DFT derived energies and electrostatic energies based on nearest neighbors and show that while it improves upon the direct point charge predictions, the correlation is still limited to certain concentrations. Finally, we develop a machine learning-based ranking model employing the electrostatic energies as predictive features. This model demonstrates high accuracy in predicting the ordering of the energies of different binding sites across a diverse range of alkali metal ion concentrations and types, as well as various sizes and types of TMDs. Electrostatics ; Alkali metal ; Transition metal ; Materials science ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Konstantin Stracke | Connor Edwards | Jack D. Evans
Source: ArXiv.org (preprint, 2025-11-28)
Field: Materials Science — Materials Chemistry

Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simulations. Accuracy is evaluated across 13 diverse materials (nine metal-organic frameworks and four inorganic compounds), computing bulk modulus, thermal expansion, and thermal decomposition. These MLIPs employ three different architectures (graph neural networks, graph network simulators, and graph transformers) with varying training datasets. We observe qualitative accuracy across these predictions but systematic underestimation of bulk modulus and overestimation of thermal expansion across all models, consistent with potential energy surface softening. From all tested models, three top performers arise; `MACE-MP-0a', `fairchem_OMAT', and `Orb-v3', with average error across metrics and materials of 41%, 44%, and 47%, respectively. Despite strong overall performance, questions arise about the limits of model transferability: dataset homogeneity and structural representation dominate model accuracy. Our results show that certain architectures can compensate for biases, a step closer to truly universal MLIPs. Molecular dynamics ; Interatomic potential ; Thermal ; Homogeneity (statistics) ; Graph ; Representation (politics) ; Property (philosophy) ; Material properties Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity
Authors: Xinyu Wu (187974) | Liuhong Zhang (4508530) | Deying Kong (6668243) | Ping He (33281) | Tong Zhang (103827) | Chao Sun (139686)
Source: Figshare (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

A Smart Workflowfor Performing the ElectrochemicalEvaluation of Fuel Cell Catalysts in a Precise Way with Ultra-HighReproducibility The reproducibility of the rotating disk electrode (RDE) tests has been decisively limited by environmental and process variability in conventional manual sample preparation. To address this challenge, we present autoRDE-film, an automated platform that integrates force-controlled robotic polishing and process-controlled drop-coating to facilitate highly consistent RDE sample preparation. This mechanism reduces the standard deviation of the oxygen reduction reaction (ORR) limiting current from 0.7 mA cm<sup>–2</sup> (traditional scheme) to 0.07 mA cm<sup>–2</sup> and the standard deviation of the ORR half-wave potential (<i>E</i><sub><i>1/2</i></sub>) from 0.038 to 0.004 V, ensuring exceptional batch-to-batch consistency. With the help of 480 RDE sample datasets generated by autoRDE-film, we have developed a ResNet-18-based deep learning model that correlates the film morphology with their electrochemical performance, enabling rapid quality classification with an accuracy of 96.3%. The combined workflow achieves a success rate of 90% for uniform coatings in batch preparations, far surpassing those of traditional approaches. By quantitatively controlling all preparation parameters, autoRDE-film establishes a universal and high-efficiency framework for evaluating ORR catalysts, which can be extended to the evaluation of fuel cell and water electrolysis catalysts and offers a transformative tool for AI-aided electrocatalyst optimization. Reproducibility ; Workflow ; Polishing ; Electrocatalyst ; Electrolysis ; Sample (material) ; Fuel cells ; Rotating disk electrode ; Electrochemical cell Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Zacharie Waysenson (22022027) | Arthur France-Lanord (5908697) | Alessandra Serva (1484947) | Patrice Simon (1280361) | Mathieu Salanne (1291470) | A. Marco Saitta (2311618)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrode FlexibilityEnhances Electrolyte Dynamicsduring Supercapacitor Charging Supercapacitors are energy storage devices with high power density and long cycle life. Combined with spectroscopy and electrochemistry, molecular simulations and theory have allowed to characterize their charging mechanisms, and we now have an excellent understanding of the effect of properties such as nanopore size or structural disorder on the supercapacitor performances. However, the influence of electrode flexibility remains to be addressed as state-of-the-art models focused on the polarization effects of the electrode, but enforced its structural rigidity, as an approximation. Here we overcome this limitation by integrating a constant-potential molecular dynamics scheme with a state-of-the-art machine-learning potential for carbon, while controlling the applied potential. Using nanoporous sp<sup>2</sup>/sp<sup>3</sup> carbon electrodes filled with an ionic liquid electrolyte, we compare the behavior of the rigidified and flexible frameworks; the latter allowing for local atomic relaxation, breathing modes, etc. We demonstrate that flexibility significantly enhances in-pore ionic diffusivity, thus shortening the characteristic charging time by a factor of 3 relative to the rigid analogue, while the specific capacitance remains in the experimental range (≈140 F·g<sup>–1</sup>) for both cases. More specifically, our analyses demonstrate that flexibility accelerates co-ion expulsion, mitigates pore overcrowding, and promotes a homogeneous induced-charge profile that penetrates deeply from the very start of the applied voltage onset, explaining the improved kinetics. Supercapacitor ; Electrolyte ; Nanopore ; Capacitance ; Electrode ; Ionic liquid ; Flexibility (engineering) ; Energy storage Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Mohamad Hossein Nargesi | Kamran Kheiralipour | Jalal Khodaei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Determining Potassium Oxide in Potassium Sulfate Fertilizers, a Survey on Driving Visible-Near Infrared Hyperspectral Imaging and Artificial Intelligence Hyperspectral imaging ; Potassium ; Infrared ; Visible spectrum ; Sulfate ; Potassium sulfate ; Environmental science ; Remote sensing ; Chemistry ; Materials science ; Optics ; Optoelectronics ; Metallurgy ; Geology ; Physics Spectroscopy and Chemometric Analyses
Authors: Mohamad Hossein Nargesi | Kamran Kheiralipour | Jalal Khodaei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Determining Potassium Oxide in Potassium Sulfate Fertilizers, a Survey on Driving Visible-Near Infrared Hyperspectral Imaging and Artificial Intelligence Hyperspectral imaging ; Potassium ; Infrared ; Sulfate ; Chemical imaging ; Potassium sulfate ; Visible spectrum ; Remote sensing ; Chemistry ; Environmental science ; Materials science ; Optics ; Geology ; Optoelectronics ; Physics Spectroscopy and Chemometric Analyses
Authors: Angela F. Harper | S. Kocher | Karsten Reuter | Christoph Scheurer
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Performance metrics for tensorial learning: prediction of Li4Ti5O12 nuclear magnetic resonance observables at experimental accuracy Predicting observable quantities from first principles calculations is the next frontier within the field of machine learning (ML) for materials modelling. While ML models have shown success for the prediction of scalar properties such as energetics or band gaps, models and performance metrics for the learning of higher order tensor-based observables have not yet been formalized. ML models for experimental observables, including tensorial quantities, are essential for exploiting the full potential of the paradigm shift enabled by machine learned interatomic potentials by mapping the structure–property relationship in an equally efficient way. In this work, we establish performance metrics for accurately predicting the electric field gradient tensor (EFG) underlying nuclear magnetic resonance (NMR) spectroscopy. We further demonstrate the superiority of a tensorial learning approach that fully encodes the corresponding symmetries over a separate scalar learning of individual tensor-derived observables. To this end we establish an extensive EFG dataset representative of real experimental applications and develop performance metrics for model evaluation which directly focus on the targeted NMR observables. Finally, by leveraging the computational efficiency of the ML method employed, we predict quadrupolar observables for 1512 atom models of Li4Ti5O12, a high performance Li-ion battery anode material, which is capable of accurately distinguishing local atomic environments via their NMR observables. This workflow and dataset sets the standard for the next generation of tensorial based learning for spectroscopic observables. Observable ; Scalar (mathematics) ; Tensor (intrinsic definition) ; Experimental data ; Workflow ; Homogeneous space ; Estimator Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Battery Materials and Technologies
Authors: Ebiapia, Epiya J
Source: Civil War Book Review (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Designing a data collection and visualization toolkit for scalable tensor algebra in quantum chemistry applications Large-scale quantum chemistry computations, such as those executed with the Tensor Algebra for Many-body Methods (TAMM) framework, require careful configuration of runtime parameters to achieve high performance and cost efficiency in high-performance computing (HPC) and cloud environments. Without effective performance analysis tools, researchers risk inefficient use of computational resources, leading to longer runtimes and higher costs. To address this challenge, this thesis presents the design and implementation of a performance profiling and visualization toolkit for TAMM, developed as part of the DOE TEC4 project in collaboration with Pacific Northwest National Laboratory, Microsoft, and Louisiana State University. The toolkit collects detailed runtime data from TAMM-based quantum chemistry computations, enabling the analysis of tensor contraction performance, visualization of dependency graphs, and estimation of compute costs through machine learning models. A user-facing dashboard provides interactive performance views and supports optimization of configuration parameters such as tile size and node allocation. The toolkit is evaluated using Coupled Cluster Singles and Double (CCSD) workloads on both LSU HPC systems and Microsoft Azure HPC cloud nodes. Results show that the machine learning models achieve accurate runtime prediction (Mean Absolute Percentage Error as low as 2.3% in certain test cases) and that the visual analytics effectively reveal performance bottlenecks. These capabilities make exascale-level simulations more accessible,interpretable, and resource-efficient for chemists and computational scientists. Visualization ; Scalability ; Cloud computing ; Supercomputer ; Big data ; Profiling (computer programming) ; Tensor (intrinsic definition) ; Data visualization ; Abstraction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Quantum many-body systems