jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhilash Purohit | S. Sathees Kumar | Pravat Ranjan Pati | Arvind Kumar | Suresh Palanimuthu | Byomakesh Mahapatra
Source: Research Square (preprint, 2026-02-26)
Field: Materials Science — Polymers and Plastics

Machine Learning Approach for Mechanical Property Assessment of Industrial Waste-Filled Epoxy-Jute Composites Ultimate tensile strength ; Flexural strength ; Epoxy ; Fiber ; Composite number ; Inverse ; Random forest ; Decision tree Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Composite Material Mechanics
Authors: Antoine Aerts
Source: The Journal of Chemical Physics (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Systematically improved potential energy surfaces via sinNN models and sparse grid sampling Accurate, global Potential Energy Surfaces (PESs) expressed in sum-of-products (SOP) form are a prerequisite for efficient high-dimensional quantum dynamics simulations using the multi-configuration time-dependent Hartree method. This work introduces a methodology for constructing such surfaces by combining hierarchical sparse grid sampling with a single-layer neural network using sinusoidal activation functions (sinNN). The sparse grid strategy provides a rigorous, unbiased discretization of the configuration space, enabling systematic improvement of the PES fidelity, where accuracy is strictly controlled by the refinement level, while successfully mitigating the curse of dimensionality. The sinNN fitting approach leverages a trigonometric factorization identity to maintain a compact SOP form, offering superior numerical stability compared to "standard" exponential-based networks for the molecular systems investigated. We validate this framework by refitting an analytical PES for nitrous acid (HONO). The flexibility of the sparse grid methodology is demonstrated through a dual-reference strategy, where grids centered on distinct isomers are merged to eliminate topological bias. This optimized sampling yields a global PES that reproduces fundamental vibrational transition energies for both trans- and cis-HONO with spectroscopic precision (<2.5 cm-1) and high data efficiency. Finally, the methodology is applied to fit potential energies computed via the AI-enhanced quantum mechanical method (AIQM2). The resulting AIQM2-based PES for HONO reproduces experimental vibrational frequencies with a root mean square deviation of ∼16 cm-1, a performance comparable to high-level ab initio methods. The robustness of the approach is further confirmed on larger molecules, formic acid (HCOOH) and carbamic acid (H2NCOOH), establishing the combination of sparse grid sampling and sinNN fitting as a powerful, automated tool for generating topologically sound, spectroscopic-quality potential energy surfaces. Grid ; Ab initio ; Sampling (signal processing) ; Potential energy surface ; Robustness (evolution) ; Curse of dimensionality Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Chris E. Mohn | Helmer Fjellvåg | Ponniah Vajeeston | Martin Valldor | Kristin Bergum
Source: Journal of Chemical Theory and Computation (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Benchmarking Density Functional Theory for Accurate Calculation of Nitride Band Gaps We benchmark exchange-correlation functionals for the calculation of fundamental band gaps of inorganic nitrides. These include conventional functionals such as the local density approximation (LDA), the generalized-gradient (Perdew-Burke-Ernzerhof) approximation (PBE), simple Slater exchange functionals (SLOC), specialized LDA/GGA-derived high local exchange (HLE16) and Armiento-Kümmel semilocal (AK13) functionals, meta-GGA functionals including TASK, the modified Becke-Johnson functional (mBJ), and Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional, as well as quasiparticle <i>GW</i> theory. Since inorganic nitrides remain strongly under-represented in previous extensive benchmark studies, the current subdatabase contributes towards building a future large-scale balanced materials compilation of band gaps to benchmark theory. From a literature survey, we carefully collect 25 binary and 11 ternary nitrides with a focus on semiconductors spanning the periodic table, including ionic Li<sub>3</sub>N, antibixbyite-structured X<sub>3</sub>N<sub>2</sub> (X = Be, Mg, Ca), early transition metals and lanthanides (e.g., ScN, YN, and LaN), ultrahard Th<sub>3</sub>P<sub>4</sub>-type structured M<sub>3</sub>N<sub>4</sub> (M = Zr, Hf) compounds, promising photocatalysts Ta<sub>3</sub>N<sub>5</sub>, different polymorphs of III-V reference covalent nitrides (BN, AlN, GaN), and many M<sub>3</sub>N<sub>4</sub> polymorphs (M = C, Si, and Ge) such as spinel-structured phases. Consistent with previous extensive benchmark tests, conventional LDA/PBE unsystematically largely underestimate band gaps with mean absolute errors (MAE) of >1.0 eV and mean absolute percentage errors (MAPE) of about 50%. Simple Slater exchange functional, SLOC, the GGA-derived AK13LDA and HLE16 functionals show improvement over LDA/PBE with MAE of 0.5-0.6 eV (MAPE ∼ 20-25%) with mBJ and HSE06 being the most accurate, with MAE = 0.30 and 0.28 eV (MAPE 12.1% and 11.1%), respectively. Strategies for the development of machine learning and the choice of appropriate exchange-correlation functionals for high-throughput large-scale material screening are discussed in light of these results. Hybrid functional ; Ternary operation ; Benchmark (surveying) ; Density functional theory ; Local-density approximation ; Band gap ; Nitride ; Benchmarking Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: Nachiket Anil Tanksale | Tobias Stauber
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Prediction of the atomistic Hubbard U interaction from moiré system STM-images using image recognition The atomistic Hubbard interaction U, representing the on-site Coulomb repulsion, serves as a pivotal parameter in theoretical models describing of correlated systems, yet its precise experimental determination especially in moiré systems remains challenging. Scanning Tunneling Microscopy(STM) provides real-space images of the local density of states (LDOS), offering rich data sets that reflect the unique electronic structure of the material. Here, we introduce a systematic methodology for extracting the Hubbard U parameter directly from these LDOS images through the application of machine learning (ML) in the case of twisted bilayer graphene in the flat-band regime. The regression of U is highly accurate even though the image-similarity is greater than 99.98%. Subsequent data-analysis further suggest a weak crossover between the weak and strong coupling regime at Uc/t 1 Crossover ; Coupling (piping) ; Hubbard model ; Coulomb ; Quantum tunnelling ; Bilayer graphene Graphene research and applications ; Topological Materials and Phenomena ; Machine Learning in Materials Science
Authors: Gracia Saral Gladison | Lavanya Priyadarshini Ramalingam | M. Udayakumar | Senthilkumar Rathnasamy
Source: International Journal of Biological Macromolecules (article, 2026-02-28)
Field: Materials Science — Biomaterials

Sustainable valorisation of agricultural residues into polyhydroxybutyrate and amylase using XGBoost-optimised extractive co-fermentation and its macromolecular characterization Polyhydroxybutyrate ; Yield (engineering) ; Bran ; Response surface methodology ; Valorisation ; Hydrolysis ; Corn steep liquor ; Amylase biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microplastics and Plastic Pollution
Authors: zhikang xiao | Junyang Chen | Lianghui Fan | Runpu Shen | Yuqian Wang | Sainan Gao | J. Q. Zhang | Xiuli Wang | Chen Qiu | Jianzhong Xu | Gaoxiang Xu
Source: Sensors and Actuators B Chemical (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Interpretable multimodal fusion unlocks enhanced accuracy in colorimetric-fluorometric biosensors: A mechanism-driven approach Robustness (evolution) ; Biosensor ; Random forest ; Pattern recognition (psychology) ; Boosting (machine learning) ; Sensitivity (control systems) ; Support vector machine Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Abigail Park | Shriram Chennakesavalu | Grant M. Rotskoff
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Scaling Transferable Coarse-graining with Mean Force Matching Coarse-grained molecular dynamics often sacrifices accuracy and transferability for computational efficiency, but the use of machine learned potentials is helping coarse-grained models attain performance on par with atomistic molecular dynamics. Nevertheless, developing representations of the coarse-grained potential energy surface faces severe scaling challenges due to the extreme data demands of widely used "bottom-up" coarse-graining objectives. In this work, we show that mean force matching, a strategy for training thermodynamically consistent coarse-grained models, requires 50x fewer training samples and 87% less total atomistic simulation time, while obtaining better accuracy on the potential of mean force for unseen proteins compared to other commonly used objectives. By systematically removing noise from the objective function, we demonstrate that it is possible to scale machine learning architectures for coarse-graining, enabling highly accurate and transferable models. We show the advantages of mean force matching both theoretically and through exhaustive benchmarking using thermodynamic consistency as the primary metric of accuracy. Scaling ; Metric (unit) ; Transferability ; Benchmarking ; Potential of mean force ; Matching (statistics) ; Consistency (knowledge bases) ; Noise (video) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Dmytro Tymoshchuk | Oleh Yasniy | Yuri Lapusta | Iryna Didych | Iaroslav Pasternak | Volodymyr Iasnii
Source: Procedia Structural Integrity (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Modeling the Hysteresis Behavior of SMA by an Ensemble Voting Machine Learning Model Shape memory alloys (SMAs) are characterized by complex hysteresis behavior resulting from phase transformations between martensite and austenite, which is significantly affected by the frequency of cyclic loading. Accurate prediction of these processes is essential to ensure the reliability of structural components in various engineering applications. This paper presents an approach for predicting the hysteresis behavior of SMAs based on an ensemble Voting machine learning model. The ensemble included Random Forest, Gradient Boosting, Extra Trees, Support Vector Regressor, K-Nearest Neighbors, and a Multilayer Perceptron. The model weights were determined as the inverse of the mean squared error, which ensured a balanced contribution of each algorithm to the final prediction. The model performance was evaluated using the MAE, MSE, R², and MAPE metrics. The results demonstrated high prediction accuracy (R 2 > 0.998, MAE < 0.022, MSE < 0.0008, and MAPE < 0.008) and confirmed the ability of the model to generalize across independent cycles, including extrapolated ones (251 and 300). The predicted hysteresis loops showed good agreement with the experimental curves. The obtained results confirm the effectiveness of the ensemble approach for modeling the behavior of SMAs and predicting their functional properties. Hysteresis ; Ensemble learning ; Inverse ; Support vector machine ; Ensemble forecasting ; Reliability (semiconductor) ; Shape-memory alloy Shape Memory Alloy Transformations ; High Temperature Alloys and Creep ; Machine Learning in Materials Science
Authors: Nachiket Anil Tanksale | Tobias Stauber
Source: arXiv (Cornell University) (preprint, 2026-02-21)
Field: Materials Science — Materials Chemistry

Prediction of the atomistic Hubbard U interaction from moiré system STM-images using image recognition The atomistic Hubbard interaction U, representing the on-site Coulomb repulsion, serves as a pivotal parameter in theoretical models describing of correlated systems, yet its precise experimental determination especially in moiré systems remains challenging. Scanning Tunneling Microscopy(STM) provides real-space images of the local density of states (LDOS), offering rich data sets that reflect the unique electronic structure of the material. Here, we introduce a systematic methodology for extracting the Hubbard U parameter directly from these LDOS images through the application of machine learning (ML) in the case of twisted bilayer graphene in the flat-band regime. The regression of U is highly accurate even though the image-similarity is greater than 99.98%. Subsequent data-analysis further suggest a weak crossover between the weak and strong coupling regime at Uc/t 1 Crossover ; Coupling (piping) ; Hubbard model ; Coulomb ; Quantum tunnelling ; Bilayer graphene Graphene research and applications ; Topological Materials and Phenomena ; Machine Learning in Materials Science
Authors: Min-Ji Koo | Yun‐Hae Kim | Sung-Youl Bae
Source: International Journal of Modern Physics B (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Deep learning-based correlation analysis of ceramic fiber composites enabled by an automated manufacturing process This study develops a material design framework for ceramic fiber reinforced composites by implementing an automated manufacturing process capable of generating the large-scale experimental dataset required for AI-based predictive models. A fully integrated continuous production line was constructed, in which key processing parameters were digitally controlled and recorded. Using this system, more than 600 experimental data points were collected and used in a deep-learning-based correlation analysis to quantify the relationships between process variables and mechanical properties. Compared with manual fabrication, the automated process significantly reduced resin content fluctuations and internal porosity, thereby enhancing structural integrity; microstructural observations further confirmed that improved fiber distribution uniformity and the suppression of resin-rich regions contributed to superior mechanical performance. Overall, the proposed framework — combining automated data acquisition with AI-based predictive modeling — provides practical design guidelines for industrial composite manufacturing and offers a foundation for future data-driven materials platforms and process optimization strategies. Ceramic ; Fiber ; Process (computing) ; Manufacturing process ; Composite number ; Data acquisition ; Process optimization ; Process variable Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Composite Material Mechanics
Authors: Bernhard Schmiedmayer | Angela Rittsteuer | Tobias Hilpert | Georg Kresse
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Scalar machine learning of tensorial quantities -- Born effective charges from monopole models Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for learning tensorial quantities based on scalar descriptors. We apply this approach to the Born effective charge tensor, showing that scalar (monopole) kernel models can successfully capture its tensorial nature by exploiting the definition of the Born effective charge tensor as the derivative of the polarisation with respect to atomic displacements. We compare this method with tensorial (dipole) kernel models, as established in our previous work, in which the tensorial structure of the Born effective charge is encoded directly in the kernel and obtained via its derivative. Both approaches are then used for charge partitioning, enabling the separation of monopole and dipole contributions. Finally, we demonstrate the effectiveness of the framework by computing finite-temperature infrared spectra for a range of complex materials. Magnetic monopole ; Scalar (mathematics) ; Charge (physics) ; Kernel (algebra) ; Tensor (intrinsic definition) ; Dipole ; Discrete dipole approximation Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Wenxi Wen | Weiqi Fan | Shiyuan Wang | Yasheng Dai | Tao Jiang | Ci Zhao
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Green in-situ photoreduction synthesis of SERS substrates based on violet phosphorus arrays/silver nanoparticles for ultrasensitive detection and accurate identification of UTI bacteria Enterococcus faecalis ; Detection limit ; Substrate (aquarium) ; Silver nanoparticle ; Raman spectroscopy ; Nanoparticle ; Surface-enhanced Raman spectroscopy ; Crystal violet Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Wajeeha Rahman | Chengquan Zhong | Jingzi Zhang | Xu Zhu | Shahid Hameed Ullah | Riffat Jehan | Jiakai Liu | Kailong Hu | Xi Lin
Source: ACS Applied Materials & Interfaces (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of High-Performance Perovskite Solar Cells via Cluster Analysis and Experimental Validation The optimization of perovskite solar cells (PSCs) is challenged by high-dimensional composition-property relationships in mixed-cation/halide systems. While machine learning (ML) can predict performance, autonomously extracting and validating precise design rules remains difficult. Here, we propose an integrated unsupervised-supervised machine learning framework capable of extraction of microstructural features from scanning electron microscopy (SEM) images to accelerate the discovery of high-efficiency PSCs. The pipeline utilizes Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to design compositional data with morphological descriptors. The predictive model achieves a high predictive accuracy of 97% in distinguishing performance clusters. Guided by this platform, we screened distinct compositional spaces and identified a high-performance "Cluster 0" characterized by FA-dominant compositions (>85%) with low bromine content (<3%). From the 800 AI-generated candidates, we successfully synthesized the top-ranked composition, FA<sub>0.94</sub>Cs<sub>0.03</sub>MA<sub>0.03</sub>Pb(I<sub>0.96</sub>Br<sub>0.04</sub>)<sub>3</sub>, which exhibited a champion efficiency of 22.06%, aligning closely with the predicted performance. Notably, the proposed method reduces the experimental search space by 3 orders of magnitude. Feature importance analysis confirmed that composition (formamidinium, methylammonium, cesium) is the primary performance driver, experimentally validating our clustering method. This work provides a generalizable tool and a significant roadmap for the data-driven design of complex functional materials, effectively bridging the gap between computational discovery and experimental success. Perovskite (structure) ; Cluster (spacecraft) ; Photovoltaic system ; Photovoltaics ; Solar cell Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Khoa Nguyen | Brendt Wohlberg | Oleg Korobkin | Marc Klasky
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Material Identification using Multi-Modal Intrinsic Radiation and Radiography We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composition whose radii are assumed known from radiography. High-purity germanium (HPGe) spectra are reduced to net counts in selected Pu-239 photo-peaks, while neutron multiplicity information is summarized by Feynman variances Y2 and Y3 computed from factorial moments of the neutron counting statistics. Using synthetic data generated with the Gamma Detector Response and Analysis Software (GADRAS) for a range of shielding materials and thicknesses, we cast the material identification problem as a supervised multi-class classification task over all admissible shell-material combinations. We demonstrate that a random forest classifier trained on combined gamma and neutron features achieves almost perfect identification accuracy for single-shell cases, and substantial performance gains for more challenging double-shell configurations relative to gamma-only classification. Alternative statistical and machine-learning formulations for this multi-class problem are examined along with examination of the impact of model-mismatch between the forward model and the test cases as given by variations in the statistical noise. Opportunities for extending the approach to more complex geometries and experimental data are also discussed. Neutron ; Detector ; Beryllium ; Electromagnetic shielding ; Gamma spectroscopy ; SPHERES ; Identification (biology) ; Multiplicity (mathematics) ; Classifier (UML) Radiation Shielding Materials Analysis ; Nuclear Physics and Applications ; Radiation Detection and Scintillator Technologies
Authors: Zohre Eskandari Alughare | Alireza Sanati | Zahra Esfandiari | Parham Joolaei Ahranjani
Source: Microchemical Journal (article, 2026-02-04)
Field: Chemistry — Electrochemistry

Recent advances in design, and applications of electrochemical sensors focused on green screen-printed electrodes to monitor heavy metals in food and beverage Heavy metals ; Electrochemical gas sensor ; Commercialization ; Process (computing) ; Environmental monitoring ; Scope (computer science) Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Dye analysis and toxicity
Authors: Li Xu | Edward | Xiangpu Li | Ran Zhao | Runze Yuan | Cuixia Sun | Yiguo Zhao | Yapeng Fang
Source: Food Research International (article, 2026-03-24)
Field: Materials Science — Biomaterials

Unraveling the stability of fish collagen: A review of assembly mechanisms, decisive factors, and regulatory strategies from natural tissues to in vitro gels Fish <Actinopterygii> ; In vitro ; Natural (archaeology) ; Heat stability Collagen: Extraction and Characterization ; Proteoglycans and glycosaminoglycans research ; Seaweed-derived Bioactive Compounds