jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao‐wen Zhang | Xiang Xu | Hao Chen | Yang‐sheng Wang | Ying Zhi | Zhe‐sheng Feng | Yan Wang
Source: Advanced Functional Materials (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Ag Nanocrystalline Decorated Hollow MXene Enabling Dynamic Heterojunction Network: An Interfacial Molecular Engineering for Self‐Decoupling Thermoelectric‐Piezoresistive Dual‐Mode Sensing ABSTRACT The advancement of flexible sensing technology is pivotal for driving next‐generation innovations in human–machine interactions, biomimetic skins, and intelligent physiological supervision. Whereas, realizing multi‐modal monitoring systems capable of simultaneous sensing to pressure stimuli and thermal stimuli remains a formidable challenge. Herein, a flexible dual‐mode chemical sensing system is developed based on the thermobaric‐field‐modulated dynamic heterojunction network, which is functionalized through molecular‐scale chemical engineering of Ag nanocrystalline decorated activated hollow MXene (22, 26, and 32 ms for the response of pressure and high/low‐temperature stimuli). The self‐decoupling mechanism of the system is established by methodically analyzing pressure‐induced quantum tunneling with interface chemistry evolution and temperature‐driven modulation of electronic structure/potential barrier at heterojunction interfaces, arising from the competitive separation of charge carrier scattering and transporting. Meanwhile, the unique interfacial molecular engineering of the covalently crosslinked PEI/GA activation layer endows the sensing system with exceptional structural stability (below 4% attenuation of the response value after 500 bending cycles) and operational durability (over 5000 cycles of periodic testing). In combination with the machine learning algorithm, the monitoring accuracy and self‐decoupling capability of the integrated dual‐mode sensing device are substantially enhanced, transcending the development boundaries in the progression of intelligent robotics, flexible sensing, and physiological monitoring. Heterojunction ; Nanocrystalline material ; Quantum tunnelling ; Flexible electronics ; Nanocrystal ; Charge carrier ; Quantum dot ; Interface (matter) MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Juan Jesús Reyes Valdez | Eugenio Rodríguez González | Edna Carina De la Cruz Terrazas | Deyanira Del Ángel López | Ana Bertha López Oyama
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Thermoelectric materials: Current challenges and prospects Thermoelectric (TE) materials make it possible to convert temperature differences directly into electrical energy, or vice versa, through the Seebeck and Peltier effects. Their applications range from waste heat recovery to cooling and thereby contribute to reducing energy losses and environmental impact, making them attractive for the energy transition. Currently, strategies such as nanostructuring, the employment of new non.toxic and abundant materials, as well as the use of artificial intelligence and machine learning to predict new materials and optimize parameters before synthesis are driving the development of more sustainable and efficient alternatives, consolidating TE materials as a technology with great potential in the energy and industrial fields. Thermoelectric effect ; Thermoelectric cooling ; Thermoelectric materials ; Current (fluid) ; Waste heat recovery unit ; Waste heat ; Energy (signal processing) ; Range (aeronautics) Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Yunzhi Li | Zibo Wu | Bohan Zhang | Wei-Hai Fang | Zhendong Li
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-adapted neural network backflow for strongly correlated electrons Accurately describing strongly correlated electrons in systems such as transition metal complexes requires strict adherence to spin symmetry, a feature largely absent in modern neural-network-based variational wavefunctions. This deficiency can lead to severe spin contamination in simulating systems with near-degenerate spin states. To resolve this limitation, we present a spin-adapted neural network backflow (SA-NNBF) ansatz, formulated in second quantization for fermionic lattice models and ab initio quantum chemistry. Our approach constructs a fully antisymmetric wavefunction by combining a neural-network backflow spatial component with a spin eigenfunction expressed in a sum-of-products form. To address the computational complexity of spin adaptation, we introduce a tensor compression algorithm for spin eigenfunctions, and a more compact wavefunction representation based on the particle-hole duality in second quantization. These advancements enable variational Monte Carlo calculations using SA-NNBF for challenging molecular systems with more than one hundred electrons, including the FeMo-cofactor (FeMoco) in nitrogenase. Applications to prototypical strongly correlated molecules demonstrate that SA-NNBF consistently outperforms standard NNBF with a similar number of parameters. Furthermore, it surpasses the accuracy of the state-of-the-art spin-adapted density matrix renormalization group (SA-DMRG) algorithm for FeMoco with a significantly reduced computational resource. Our work establishes a foundational framework for exploring fully symmetry-preserving neural-network quantum states for interacting fermion problems. Wave function ; Antisymmetric relation ; Quantum ; Density matrix ; Spin (aerodynamics) ; Backflow ; Electron ; Density matrix renormalization group ; Tensor (intrinsic definition) Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Tim Tetenberg | Hagen Neugebauer | Christoph Plett | Nakul Santhosh | Markus Bursch | Christoph Riplinger
Source: Journal of Chemical Theory and Computation (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

ORCA Meets Python─The ORCA Python Interface OPI The ORCA program suite is one of the most widely used quantum chemistry software packages. It features a wide range of electronic structure methods and algorithms for the prediction of molecular chemical properties, reactivity, and spectroscopy. In this paper, we present a fully featured ORCA Python Interface termed OPI to drastically increase the accessibility of ORCA's method portfolio and enable efficient automation of quantum chemical workflows. OPI is an open-source Python library that provides straightforward low-level access to ORCA's input, execution, and output with a few lines of Python code. In the following, we introduce OPI version 2.0 and its key features, also outlining its general architecture. In addition, we demonstrate its application through several diverse examples of quantum chemical workflows. These examples include a system-dependent optimal-tuning procedure for range-separated hybrid functionals, generation of training data for machine learning purposes, orbital localization and visualization for chemical education, and calculations with density functional ensembles. Finally, we outline the current status of OPI and future plans for its development. OPI is compatible with ORCA ≥ 6.1.1 and Python ≥ 3.11. The project, its code, and its including documentation are available at https://github.com/faccts/opi. OPI is also available through PyPI (https://pypi.org/project/orca-pi). Python (programming language) ; Suite ; Visualization ; Documentation ; Software ; Quantum chemical ; Usability Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies
Authors: Qing-Xiao Wang | Lei Chen | Xue-Ling Zhang | Xiao-Xin Li
Source: Machine Learning (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

ABANet: An Atom-Bond Attention-Enhanced Neural Network for End-to-End Retrosynthesis GRASP ; Context (archaeology) ; Artificial neural network ; Feature (linguistics) ; Retrosynthetic analysis ; Representation (politics) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: L. Thomas | Sampath Parasuram | Arun Sam Varghese | Jerrin Joy Varughese | S. Manobalan | Suryasarathi Bose | T.P. Sumangala | M.S. Sreekanth
Source: Composites Part C Open Access (article, 2026-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Epoxy vitrimer based nanocomposites for EMI shielding: Integrating reprocessability, self-healing, and machine learning predictions • Epoxy vitrimer composite with MWCNTs and CoFe 2 O 4 developed for EMI shielding • Disulfide bond of vitrimer demonstrated self-healing and reprocessability • Epoxy vitrimer showed electrical percolation threshold at 0.2 wt% MWCNTs • Optimal EMI shielding of −18.4 dB with 15 wt% CoFe 2 O 4 and 0.2 wt% MWCNTs • Machine and deep learning models predict EMI shielding and mechanical Properties The rapid evolution of electronic technologies necessitates multifunctional polymeric materials capable of offering robust electromagnetic interference (EMI) shielding, with mechanical durability, reprocessability and self-healing. The investigation utilized, epoxy vitrimer nanocomposites reinforced with conductive multi-walled carbon nanotubes (MWCNTs) and magnetic cobalt ferrite (CoFe) nanoparticles to engineer high-performance, reprocessable EMI shielding materials. The vitrimer matrix, synthesized using diglycidyl ether of bisphenol A (DGEBA) and 4-aminophenyl disulfide (4-AFD), was optimized to incorporate dynamic covalent disulfide bonds enabling thermal reprocessability, self-healing, and solvent resistance. A fixed MWCNTs content of 0.2 wt% established as the electrical percolation threshold was used to ensure a conductive network, while CoFe content was systematically varied from 0 to 20 wt%. The synergistic interplay between the conductive and magnetic fillers resulted in an optimal shielding effectiveness (SE T ) of -18.4 dB at 0.2 wt% MWCNTs and 15 wt% CoFe, primarily governed by absorption losses due to interfacial polarisation and multiple scattering. Mechanical and thermal analyses demonstrated a balanced trade-off between stiffness and toughness, with enhanced thermal stability, and reprocessability. Furthermore, machine learning (Random Forest) and deep learning (DNN with Adam optimiser) models were deployed to accurately predict the tensile properties and shielding effectiveness, offering a data-driven framework for design optimisation. The developed vitrimer composites also exhibited excellent solvent resistance and self-healing capability, underscoring their potential for sustainable, high-performance EMI shielding applications in aerospace, electronics, and defence industries. Epoxy ; Nanocomposite ; Electromagnetic shielding ; Electrical conductor ; Percolation threshold ; Carbon nanotube ; Elastomer ; Composite number ; Ultimate tensile strength Electromagnetic wave absorption materials ; Smart Materials for Construction ; Flame retardant materials and properties
Authors: Lejla S. Biberic | Nisarg Joshi | Jim Pfaendtner
Source: Research Square (preprint, 2026-01-14)
Field: Materials Science — Materials Chemistry

Are Energy and Forces Really Enough? Using Structure to Evaluate the Accuracy and Transferability of Machine Learning Potentials of Biomolecules Transferability ; Metric (unit) ; Fidelity ; Experimental data ; Energy (signal processing) ; Scaling ; Work (physics) ; High fidelity Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Naim Ben Ali | Raj Agravat | Habib Kraiem | Aymen Flah | Ashish Baldania | Yogesh Sharma | Mahaveer Jain | Shobhit K. Patel
Source: Materials Today Advances (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Next-generation segmented Halo-T metamaterial ultra-wideband design using MXene-TiO2-Cu materials performance prediction and optimization with machine learning assisted solar absorber for industrial heating systems Performance prediction ; Solar energy ; Performance enhancement ; Metamaterial MXene and MAX Phase Materials ; Electromagnetic wave absorption materials ; Solar-Powered Water Purification Methods
Authors: Austin Rodriguez | Justin Smith | Sakib Matin | Nicholas Lubbers | Kipton Barros | Jose L. Mendoza-Cortes
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials The Hessian matrix (second derivatives) encodes far richer local curvature of the potential energy surface than energies and forces alone. However, training machine-learning interatomic potentials (MLIPs) with full Hessians is often impractical because explicitly forming and storing Hessian matrices scales quadratically in cost and memory. We introduce Projected Hessian Learning (PHL), a scalable second-order training framework that injects curvature information using only Hessian-vector products (HVPs). Rather than constructing the Hessian, PHL projects curvature along stochastic probe directions and uses an unbiased stochastic trace-based loss with favorable system-size scaling, enabling curvature-informed training without quadratic memory growth. We benchmark PHL on a chemically diverse dataset of reactants, products, transition states, intrinsic reaction coordinates, and normal-mode sampled geometries computed at omegaB97XD/6-31G(d). We compare energy-force training (E-F), two HVP-based schemes (E-F-HVP with one-hot or randomized probes), and full energy-force-Hessian training (E-F-H). With randomized probes per minibatch, both HVP schemes match full-Hessian training in energy, force, and Hessian accuracy while delivering >24x epoch speedups for the small molecular systems studied. In a fixed-probe regime with one HVP per molecule, randomized projections consistently outperform one-column probing, especially for far-from-equilibrium geometries. Overall, PHL replaces explicit Hessian supervision with force-complexity curvature training, retaining most second-order accuracy gains while scaling to larger, more complex molecular systems. Hessian matrix ; Curvature ; Quadratic equation ; Benchmark (surveying) ; Scaling ; Scalability ; Quadratic growth Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Bingqian Liu | Anguo Xie | Han Wu | Yunhong Liu
Source: Journal of Food Process Engineering (article, 2026-01-27)
Field: Chemistry — Analytical Chemistry

Development of a Generalizable IRIV‐ANN Model for Rapid, Non‐Destructive Polysaccharides Detection in <i>Polygonatum Sibiricum</i> ABSTRACT Polysaccharides, as important bioactive components in medicinal and functional food materials, are key indicators for quality evaluation. Conventional analytical methods for polysaccharides quantification often require laborious sample preparation and may cause irreversible damage to samples, which limits their suitability for routine quality control. To overcome these limitations, we developed a rapid and non‐destructive detection approach by integrating hyperspectral imaging with an iteratively retained information variables‐artificial neural network (IRIV‐ANN) model. Spectral data (400–1000 nm) were processed using the IRIV algorithm, which classified variables into four categories‐strong information, weak information, no information, and interference. The optimal subset was then used to construct an ANN model, and its performance was compared with models based on shuffled frog leaping algorithm (SFLA), successive projections algorithm (SPA), one‐dimension convolutional neural network (1D‐CNN), full‐spectrum ANN, and multiple linear regression (MLR). The IRIV‐ANN model successfully eliminated redundant variables, reduced model complexity, and improved predictive performance, achieving a coefficient of determination (R 2 ) of 0.9301 and a root mean square error (RMSE) of 0.7348 for the prediction set. The characteristic wavelengths selected were closely associated with vibrational changes in O‐H and C‐H functional groups, providing chemical interpretability of the spectral features. This method provides an efficient and reliable analytical tool for rapid quantification of polysaccharides and has considerable potential for broader application in the quality control of functional foods, traditional medicines, and other plant‐derived materials. Interpretability ; Hyperspectral imaging ; Convolutional neural network ; Artificial neural network ; Pattern recognition (psychology) ; Regression ; Partial least squares regression ; Linear regression Spectroscopy and Chemometric Analyses ; Polysaccharides and Plant Cell Walls ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Thomas P Ilett | Thomas A Hazlehurst | Chen Jiang | Cai Y Ma | David C Hogg | Kevin J Roberts
Source: PNAS Nexus (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Reconstruction of 3D crystal growth from transmission optical microscopy images Abstract Variations in the 3D shapes of crystalline materials can greatly affect their physical chemical properties, influencing the downstream processes required for their formulation, precision manufacturing and transportation. Under different growth conditions, identical compounds can produce a wide variety of particle shapes and sizes, yet a comprehensive understanding of the relationship between crystallisation conditions and final product properties is severely lacking. In part, this reflects the technical challenges associated with accurately recovering 3D crystal shape information during their growth. Here, we present a novel method for reconstructing the evolving 3D polyhedral shape of a single crystal in a growth cell from a sequence of transmission optical microscopy images. Our approach combines deep learning with synthetic image generation based on theoretically grounded crystallography together with accurate simulation of the refraction of light at the crystal faces, yielding robust estimates of the dynamics of the crystal shape evolution during the crystallisation process. We demonstrate our approach by tracking the 3D growth and shape development of the polyhedral α form of l-glutamic acid and validate our results against manual measurements collected using a purpose-built interactive software tool. We observe changes in the relative areas of crystal faces, characterising not just the size but also the shape changes during growth. This approach offers a new framework for in situ monitoring of crystal growth and may support future advances in the precision manufacturing of crystalline particles through the improved digital design and control of crystallisation processes. Crystallization ; Crystal (programming language) ; Crystal growth ; Optical microscope ; Microscopy ; Tracking (education) ; Software ; Video microscopy Crystallization and Solubility Studies ; Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications
Authors: Xuan Liu | Xuerui Song | Yusuf Sulub | David Zoller | Zhenyu Kong | Blake N. Johnson
Source: The Analyst (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Classification of recycled plastics using sparse and imbalanced spectral data and data augmentation by the generative adversarial network Accurate identification of post-consumer plastics is essential to establishing high-performance recycling processes and enabling a circular and sustainable economy and environment through effective recycling and remanufacturing. However, Fourier transform infrared (FTIR) spectra of recycled materials often exhibit noise, baseline shifts, and overlapping signatures from additives or contaminants, resulting in datasets that are both sparse and severely imbalanced. This data complexity, sparsity, and class imbalance can degrade conventional machine-learning classifiers, resulting in higher rates of misclassifying plastics. To address these challenges, we investigated if data augmentation using generative adversarial networks could enhance polymer classification performance. We implemented a Generative Adversarial Network (GAN) framework that integrates adversarial training with a classifier-guided feedback loop to synthesize realistic, class-discriminative FTIR spectra for six commonly recycled polymers, polyethylene (PE), polypropylene (PP), polystyrene (PS), polycarbonate (PC), polyethylene terephthalate (PET), and acrylonitrile butadiene styrene (ABS), and trained multilayer perceptron classifiers on datasets with varying ratios of synthetic data. The optimal balanced accuracy of 96.2% was achieved when synthetic spectra accounted for 50% of the training set, whereas including more than 90% synthetic data degraded generalization. Synthetic data augmentation using a GAN with the optimal augmentation ratio improved ABS classification accuracy, precision, and recall by 43%, 50%, and 33%, respectively, compared with no augmentation and replicate experimental measurements. These results demonstrate that GAN-based data augmentation can effectively mitigate data sparsity and class imbalance in spectral classification of common plastics, providing a practical foundation for creating robust online polymer classification systems. Adversarial system ; Sorting ; Generative grammar ; Pattern recognition (psychology) ; Generative adversarial network ; Training set Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Selim Demirci | Durmuş Özkan Şahin | Sercan Demirci | Mehmet Masum Tünçay | Moataz M. Attallah
Source: PLoS ONE (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties Understanding and controlling grain growth kinetics in steels is crucial for optimizing mechanical properties during thermomechanical processing. However, traditional empirical models often fail to account for the complex, nonlinear interactions between alloying elements and processing parameters. In this study, we introduce a novel machine learning (ML) based framework that predicts austenitic grain growth behaviour directly from chemical composition and process conditions, utilizing a comprehensive dataset of 1039 experimentally validated samples. Among various algorithms tested, the XGBoost model demonstrated exceptional predictive capability, achieving an R2 value of 0.9728 after hyperparameter optimization. Feature selection methods (Pearson correlation, CfsSubset, ReliefF) and SHAP-based explainable AI analyses were employed to identify the most influential parameters, revealing temperature, initial grain size, and holding time as dominant factors. Experimental validation was conducted on 316L stainless steel samples annealed at 1100 °C. The predicted grain sizes showed strong agreement with experimental measurements, and the observed hardness variations followed the expected Hall-Petch behaviour. This study demonstrates the first integrated ML and experimental approach for predicting grain growth kinetics in steels, offering a powerful tool for alloy design and process optimization. Future work will extend this framework to additional process variables and alloy systems. Hyperparameter ; Grain growth ; Alloy ; Work (physics) ; Feature selection ; Grain size ; Austenite ; Process (computing) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep