jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Min Shen | Ke Liu | Sheng Mao | Chiara Daraio
Source: Science Advances (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Self-supervised AI for decoding and designing disordered metamaterials Disordered microstructures are key to the distinct multifunctional properties of many natural materials. However, understanding the relationship between their microstructures and physical functions remains formidable, hindering engineering applications. Here, we introduce a physics-guided, self-supervised artificial intelligence (AI) framework called generative networks for disordered metamaterials (GNDM), trained on a progressively expanding dataset starting from a few initial samples. We integrate a formula writing module in the training process of neural networks to enforce the identification of the most selective set of hidden geometric invariants that dictate bulk properties. By inversely solving the formulae, GNDM manipulate disordered geometric features to extrapolate property space and design previously unknown structures via its generator module, validated by experiments. GNDM offers an all-in-one AI framework that closes the loop of feature extraction, property prediction, formula writing, and inverse design, unraveling the regulative role of disorder, a critical challenge in the study of metamaterials with complex microstructures. Metamaterial ; Property (philosophy) ; Generator (circuit theory) ; Decoding methods ; Generative grammar ; Function (biology) ; Artificial neural network ; Key (lock) ; Set (abstract data type) Machine Learning in Materials Science ; Metamaterials and Metasurfaces Applications ; Quasicrystal Structures and Properties
Authors: Chenyu Du | Jia Lei Chen-Qiu | Zengying Zhao | Félix Zamora | Hao Liu
Source: Journal of Alloys and Compounds (article, 2026-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in supercapacitors: A critical review of energy storage mechanisms, electrode design strategies and future perspectives Supercapacitor ; Energy storage ; Multiphysics ; Energy density ; Field (mathematics) ; Electrochemical energy storage ; Energy (signal processing) Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Microbial Fuel Cells and Bioremediation
Authors: Yige Liu | Menglong Dai | Rui Li | Hao Rui | Shuchang Wang | Pengpeng Hu | Fengxin Sun | Z. Li | Xinghua Hong
Source: Small (article, 2026-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Humidity‐Triggered Spontaneous Phase Separation Enables Multifunctional 3D Porous Composites for Piezoresistive Mechanosensory and Electromagnetic Interference Shielding Three-dimensional (3D) porous composites have attracted significant attention for piezoresistive sensing and electromagnetic interference (EMI) shielding applications owing to their large specific surface area, excellent electrical conductivity, and remarkable flexibility. However, the fabrication of these composites with minimal complexity and equipment requirements remains challenging. Here, we present a facile humidity-triggered nonsolvent vapor-induced phase separation (NVIPS) approach to fabricate 3D porous composites using a thermoplastic polyurethane solution on reduced graphene oxide-modified carbon fiber felt under ambient conditions. The fabricated composites exhibit exceptional piezoresistive stability with rapid response (60 ms), a broad pressure detection range, high air permeability (358 mm s<sup>-1</sup>), and outstanding X-band EMI shielding (74 dB). Moreover, by integrating a neural network model, an intelligent cognition system capable of real-time recognition of the object material species is constructed via robotic touch under varying pressures (accuracy > 97.8%). Our bioinspired NVIPS strategy offers a versatile solution for multifunctional sensing and EMI protection through the highly flexible, breathable, and conductive porous composites. Piezoresistive effect ; Electromagnetic shielding ; Fabrication ; EMI ; Electromagnetic interference ; Graphene ; Electrical conductor ; Porosity ; Thermoplastic polyurethane Electromagnetic wave absorption materials ; Smart Materials for Construction ; Thermal Radiation and Cooling Technologies
Authors: Hyeonbin Moon | Donggeun Park | Jinwook Yeo | Seunghwa Ryu
Source: International Journal for Numerical Methods in Engineering (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

An Energy‐Based Physics‐Informed Computational Framework for Forward and Inverse Flexoelectric Problems ABSTRACT Flexoelectricity, the coupling between strain gradients and electric polarization, poses significant computational challenges due to its governing fourth‐order partial differential equations that require C 1 ‐continuous solutions. To address these issues, we propose a physics‐informed neural network (PINN) framework grounded in an energy‐based formulation that treats both forward and inverse problems within a unified architecture. The forward problem is recast as a saddle‐point optimization of the total potential energy, solved via the deep energy method (DEM), which circumvents the direct computation of high‐order derivatives. For the inverse problem of identifying unknown flexoelectric coefficients from sparse measurements, we introduce an additional variational loss that enforces stationarity with respect to the electric potential, ensuring robust and stable parameter inference. The framework integrates finite element‐based numerical quadrature for stable energy evaluation and employs hard constraints to rigorously enforce boundary conditions. Numerical results for both direct and converse flexoelectric effects show excellent agreement with mixed‐FEM solutions, and the inverse model accurately recovers material parameters from limited data. This study establishes a unified, mesh‐compatible, and scalable PINN approach for high‐order electromechanical problems, offering a promising alternative to traditional simulation techniques. Converse ; Inverse problem ; Inverse ; Computation ; Boundary value problem ; Finite element method ; Artificial neural network ; Quadrature (astronomy) ; Coupling (piping) Nonlocal and gradient elasticity in micro/nano structures ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Picheswara Rao Polu | Manish Vyas | Malakapogu Ravindra Babu
Source: Critical Reviews in Analytical Chemistry (article, 2026-02-13)
Field: Chemistry — Analytical Chemistry

State-of-the-Art Analytical Technologies for Detection of Food Adulteration: Advances, Validation, and Future Horizons Food adulteration poses an escalating global crisis, threatening public health, economic stability, and consumer trust while costing the food industry billions annually and compromising international trade relationships. We systematically evaluate cutting-edge analytical technologies revolutionizing adulteration detection and authenticity verification across high-risk commodities including dairy products, oils, spices, and protein sources. This review critically examines breakthrough spectroscopic techniques (IR, NIR, Raman, NMR), advanced chromatographic-mass spectrometry platforms (HPLC, GC-MS, LC-MS/MS, metabolomics), and innovative DNA-based molecular authentication tools for ingredient traceability. Particular emphasis is placed on emerging rapid-response technologies, including next-generation biosensors, immunoassays, and AI-integrated portable instrumentation enabling real-time field detection. The transformative impact of artificial intelligence, machine learning algorithms, and advanced chemometrics in enhancing pattern recognition, predictive accuracy, and automated decision-making is comprehensively analyzed. Critical evaluation of validation protocols, standardization challenges, and regulatory acceptance barriers provides practical insights for implementation. The study uniquely integrates technological capabilities with regulatory landscapes, identifying key limitations while proposing actionable future directions including multi-technique fusion approaches, point-of-care testing solutions, and global harmonization frameworks. This comprehensive analysis serves as an indispensable resource for researchers, industry stakeholders, and policymakers committed to fortifying food integrity in our interconnected global marketplace. Harmonization ; Standardization ; Transformative learning ; Instrumentation (computer programming) ; Emerging technologies ; Field (mathematics) ; Resource (disambiguation) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Melamine detection and toxicity
Authors: Tianyang Cui | Yao Zheng | Wei Cai | Liangyuan Qi | Jingwen Wang | Wei Yang | Weiguo Song | Yuan Hu | Jixin Zhu
Source: Advanced Materials (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Quantum to Device AI‐Guided Passivation Paradigm for All‐Weather Ultrastable MXene Based Photothermal Converter Photothermal efficiency in MXenes arises from the complex interplay between electronic structure and lattice dynamics, yet the precise contribution of electron-phonon coupling (EPC) remains poorly understood. By integrating ab initio nonadiabatic carrier-dynamics simulations with state-resolved electron-phonon-coupling analysis, the intrinsic mechanisms governing photothermal conversion in MXene materials are elucidated. Results reveal that MXene photothermal performance is dictated by an intrinsic hierarchy of EPC channels and hot-phonon accumulation, whereas defect-mediated non-radiative recombination serves as a secondary channel and ultimately compromises long-term photothermal stability. Building on this mechanistic insight, a physics-inspired and AI-assisted molecular-screening framework is developed to identify surface passivation chemistries capable of extending hot carrier lifetimes and mitigating phonon bottlenecks. Guided by this paradigm, a composite film endowed with a concave-spherical light-trapping array was fabricated, leading to substantial improvements in photothermal conversion efficiency and operational stability. This quantum-to-device co-design paradigm transcends MXenes, providing a data-driven, systematic design pathway that integrates fundamental theory with surface passivation to accelerate the advancement of durable photothermal devices tailored for sustainable energy applications. Photothermal therapy ; MXenes ; Passivation ; Photothermal effect ; Quantum ; Coupling (piping) ; Ab initio ; Quantum dot MXene and MAX Phase Materials ; 2D Materials and Applications ; Energetic Materials and Combustion
Authors: Tomislav Stolar | Jasna Alić | Lucia Casali | Nikita Y. Gugin | Matěj Baláž | Adam A. L. Michalchuk∞ | Franziska Emmerling
Source: Chem (article, 2026-01-20)
Field: Chemistry — Physical and Theoretical Chemistry

Mechanochemistry: Looking back and ahead Starting with the discovery of fire and the preparation of food in prehistoric times, mechanochemistry is the oldest form of chemistry that humans have controlled.Mechanochemical practices, such as grinding with a mortar and pestle, continued into the Middle Ages until dedicated scientific studies began in the 19 th century.Since then, research in mechanochemistry has shown that many chemical reactions can be performed via mechanical force without or with small amounts of solvent.Besides being time, material, and energy efficient, mechanochemical reactions often yield products that differ from those obtained in solution.Therefore, not only is mechanochemistry greener and more sustainable than conventional solution chemistry, but it also has the added value of providing new reactivity and selectivity.This is especially important today, when chemists need to invent high-performance materials, intermediates, and products with the use of sustainable feedstocks and develop environmental remediation pathways.At the same time, time-resolved in situ monitoring and computational modeling are necessary for addressing fundamental questions about the atomistic, molecular, and electronic nature of mechanochemical reactivity.Integrating digitalization, robotics, and artificial intelligence tools promises to increase the reproducibility and scalability of mechanochemical processes.Further evolution of mechanochemistry is expected to have a transformative effect on the chemical industry. Mechanochemistry ; Transformative learning ; Grinding ; Value (mathematics) ; Yield (engineering) ; Environmental remediation Crystallography and molecular interactions ; Force Microscopy Techniques and Applications ; Mineralogy and Gemology Studies
Authors: Alice Mirone | Luca Cartabia | Irene Martín | Andrea Rubino | Ilka Kriegel | Teresa Gatti
Source: Solar RRL (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compact Photostorage Systems: New Materials and Designs for Integrated Energy Harvesting and Storage The growing demand for flexible and autonomous electronics hasaccelerated the development of compact energy systems capable of both harvesting and storing solar energy. Photobatteries and photocapacitorsrepresent a new generation of self‐charging devices that merge photovoltaic and electrochemical functions within a single structure. These systems overcome the conversion losses and bulkiness of conventional solar‐battery combinations, enabling miniaturized, efficient, and sustainable power sources. This review summarizes recent progress in materials, architectures, and design strategies for compact photostorage systems. This work will focus, in particular, on two‐terminal (2T) monolithic configurations that provide the highest integration level. Advances in inorganic semiconductors such as transition‐metal oxides, sulfides, and lead‐free perovskites, as well as organic materials including conductive polymers, dyes, and carbon nanostructures, have greatly enhanced photo‐charge generation, mobility, and retention. Furthermore, innovations in gel and solid‐state electrolytes have improved flexibility, safety, and long‐term stability. Despite significant progress, major challenges remain in mitigating charge recombination, optimizing energy density and standardizing performance evaluation. By integrating recent results and emerging trends, this review outlines key directions for the rational design of next‐generation self‐powered photostorage systems that could underpin the future of portable, wearable, and sustainable energy technologies. Photovoltaic system ; Energy harvesting ; Sustainable energy ; Energy storage ; Merge (version control) ; Electronics ; Renewable energy ; Supercapacitor ; System integration Supercapacitor Materials and Fabrication ; Solar-Powered Water Purification Methods ; Advanced battery technologies research
Authors: Abdul Ghaffar | Muhammad Ahsan Farooq Qaisar | Jun Liu | Mehwish Hanif | Anand Parkash | Salamat Ali | Ayesha Kalsoom Qaisar | Inaam Ullah | Ayesha Irfan | Sadam Hussain | Ibrahim A. Shaaban | Muhammad Irfan
Source: The Chemical Record (article, 2026-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphitic Carbon Nitride: A Rising Star Electrode Material for Supercapacitors The rising global energy demand requires the development of high-performance supercapacitors (SCs) that synergize high-power density with substantial energy density. The pursuit of such energy storage devices is fundamentally related to the innovation of advanced electrode materials. Two-dimensional graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>) has recently emerged as a compelling candidate, distinguished by its unique nitrogen-rich structure, tunable electronic properties, and facile synthesis. This review provides a comprehensive and critical investigation of g-C<sub>3</sub>N<sub>4</sub>-based materials for SCs. We systematically analyze the crystal structure, physicochemical properties, and synthesis methodologies of g-C<sub>3</sub>N<sub>4</sub>, correlating these characteristics with their electrochemical performance. For the first time, a detailed comparative analysis is presented, categorizing strategies into the engineering of pristine g-C<sub>3</sub>N<sub>4</sub>, heteroatom doping, and the construction of composites. We place particular emphasis on the superior performance of composites formed with conductive polymers, transition metal oxides/sulfides (TMOs/TMSs), graphene, MXenes, and other families, where synergistic effects enhance conductivity, stability, and charge storage capacity. Finally, we provide a critical outlook on the existing challenges and future possible directions, aiming to guide the rational design of next-generation g-C<sub>3</sub>N<sub>4</sub>-based electrode materials to unlock their full potential in SCs. Supercapacitor ; Heteroatom ; Graphitic carbon nitride ; Energy storage ; Electrode ; Carbon fibers ; Electronics ; Electrical conductor Supercapacitor Materials and Fabrication ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Xiaoqing Liu | Kehan Zeng | Zedong Luo | Yangshuai Wang | Teng Zhao | Zhenli Xu
Source: Journal of Applied Physics (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Fine-tuning universal machine-learned interatomic potentials: A Tutorial on methods and applications Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy. While the number of available U-MLIPs and their fine-tuning applications are rapidly expanding, there remains a lack of systematic guidance on how to effectively fine-tune these models. This Tutorial provides a comprehensive, step-by-step guide to fine-tuning U-MLIPs for computational materials modeling. Using the recently released MACE-MP-0 as a representative foundation model, we illustrate the full workflow of data set preparation, hyperparameter selection, model training, and validation. Beyond methodological guidance, we conduct systematic case studies on solid-state electrolytes, stacking fault defects in metals, semiconductors, solid–liquid interfacial interactions in low-dimensional systems, and more complicated heterointerfaces. These examples demonstrate that fine-tuning substantially improves predictive accuracy while maintaining affordable computational cost, accelerates training convergence, enhances out-of-distribution generalization, and achieves superior data efficiency. Remarkably, fine-tuned foundation models can even capture aspects of long-range physics without explicit corrections. Together, these results highlight that fine-tuning not only provides a practical recipe for applying U-MLIPs but also offers new insights into their physical fidelity and potential for advancing large-scale atomistic simulations. To support practical applications, we include code examples that enable researchers, particularly those new to the field, to efficiently incorporate fine-tuned U-MLIPs into their workflows. Workflow ; Hyperparameter ; Set (abstract data type) ; Interatomic potential ; Code (set theory) ; Foundation (evidence) ; Software ; Fidelity Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: C. J. Webb | Terry D. Humphries | Joseph A. Teprovich | Marek Polański | Michael Heere | Hai-Wen Li | Michael Felderhoff | Yaroslav Filinchuk | Martin Dornheim | M. Paskevicius | Craig Buckley | Torben R. Jensen
Source: Chemical Communications (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Diverse hydrogen chemistry with perspectives for energy storage The chemistry of hydrogen and its interaction with matter is remarkably diverse with new discoveries and materials continuously being uncovered. New types of chemical bonding and interactions allow for the preparation of new compounds with unusual compositions and properties. For instance, neutral hydrogen molecules may spontaneously form penta-dihydrogen clusters, (H<sub>2</sub>)<sub>5</sub>, in nanoporous materials with extremely dense packing, similar to metallic hydrogen at high pressure. Hydrides with extreme hydrogen densities - 'superhydrides' - have yielded record critical temperatures under pressure and now guide routes toward low-pressure high-temperature hydride superconductors. A well-known weak interaction identified in biological matter, the hydrogen bond, has an inorganic analogue: the dihydrogen bond. These two interactions have very similar bond lengths and bond strengths that are known to produce flexible and relatively open structures, which often have interesting functionalities. Recently, the di-hydrogen bond has come into focus for development of fast divalent magnesium and calcium cationic conductors. In this review, we highlight key advances in the synthesis and characterisation of novel hydrogen-based materials and illustrate how the compositional and structural versatility of hydrides leads to new functionalities. Hydrides are highly relevant materials with a diversity of energy applications such as solid-state hydrogen storage, solid-state batteries and superconductors, as well as future global hydrogen transportation. Hydrogen storage ; Hydrogen ; Hydride ; Hydrogen bond ; Nanoporous ; Metal ; Molecule ; Chemical bond Crystallography and molecular interactions ; Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies
Authors: Pierre-Paul De Breuck | Hashim A. Piracha | Gian-Marco Rignanese | Miguel A. L. Marques
Source: npj Computational Materials (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

A generative material transformer using Wyckoff representation Abstract Materials play a critical role in various technological applications. Identifying and enumerating stable compounds—those near the convex hull—is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and space groups and can be conditioned on specific properties. We demonstrate its ability to generate stable, novel, and unique crystal structures by conditioning on the distance to the convex hull. Resulting structures are 8 times more likely to be stable than baselines using PyXtal with charge compensation, while maintaining high computational efficiency. We also release a dataset of 3 million unique crystals generated by our method, including 4000 compounds verified by density-functional theory to be within 0.001 eV/atom of the convex hull. Invertible matrix ; Transformer ; Regular polygon ; Generative grammar ; Representation (politics) ; Generative model ; Convex optimization Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Block Copolymer Self-Assembly