jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kryštof Kolář | Kang Yang | Felix von Oppen | Christophe Mora
Source: arXiv (Cornell University) (preprint, 2024-06-10)
Field: Materials Science — Materials Chemistry

Hofstadter spectrum of Chern bands in twisted transition metal dichalcogenides We study the topological bands in twisted bilayer transition metal dichalcogenides in an external magnetic field. We first focus on a paradigmatic model of WSe$_2$, which can be described in an adiabatic approximation as particles moving in a periodic potential and an emergent periodic magnetic field with nonzero average. We understand the magnetic-field dependent spectra of WSe$_2$ based on the point net zero flux, at which the external field cancels the average emergent field. At this point, the band structure interpolates between the tightly-bound and nearly-free (weak periodic potential) paradigms as the twist angle increases. For small twist angles, the energy levels in a magnetic field mirror the Hofstadter butterfly of the Haldane model. For larger twist angles, the isolated Chern band at zero flux evolves from nearly-free bands at the point of net zero flux. We also apply our framework to a realistic model of twisted bilayer MoTe$_2$, which has recently been suggested to feature higher Landau level analogs. We show that at negative unit flux per unit cell, the bands exhibit remarkable similarity to a backfolded parabolic dispersion, even though the adiabatic approximation is inapplicable. This backfolded parabolic dispersion naturally explains the similarity of the Chern bands at zero applied flux to the two lowest Landau levels, offering a simple picture supporting the emergence of non-Abelian states in twisted bilayer MoTe$_2$. We propose the study of magnetic field dependent band structures as a versatile method to investigate the nature of topological bands and identify Landau level analogs. Transition metal ; Spectrum (functional analysis) ; Condensed matter physics ; Physics ; Materials science ; Chemistry ; Quantum mechanics ; Catalysis 2D Materials and Applications ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: Sylwia Pasieczna‐Patkowska | Marcin Cichy | Jolanta Flieger
Source: Molecules (review, 2025-02-04)
Field: Materials Science — Materials Chemistry

Application of Fourier Transform Infrared (FTIR) Spectroscopy in Characterization of Green Synthesized Nanoparticles The fundamental principle of Fourier Transform Infrared (FTIR) spectroscopy is based on the vibration and rotation of atoms, and it has become a universal and widely used spectral methodology for the detection of internal molecular structures in a diverse range of fields. A considerable number of review articles pertaining to the applications of FTIR spectroscopy have been published in recent years. Nevertheless, a comprehensive summary of the application of FTIR spectroscopy in nanoparticles' (NPs') green synthesis has yet to be presented. In the present paper, we propose a series of case studies that demonstrate the application of FTIR spectroscopy in the analysis of metal and metal oxide NPs that have been synthesized using green synthesis processes. Furthermore, a summary is presented of the position of functional group bands in FTIR spectra that are responsible for the reduction, capping and stabilization of NPs. In this review, we explore the advantages and limitations of FTIR and propose methodologies for overcoming these challenges. We also present potential solutions for the analysis of complex FTIR spectra. The present summary is intended to serve as a compendium of information for researchers engaged in the field of green synthesis of NPs, utilizing FTIR spectroscopy as a research tool. Fourier transform infrared spectroscopy ; Spectroscopy ; Infrared spectroscopy ; Fourier transform ; Materials science ; Characterization (materials science) ; Infrared ; Fourier transform spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Chemical engineering ; Physics ; Organic chemistry ; Optics ; Engineering Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Claudio Zeni | Robert Pinsler | Daniel Zügner | Andrew T. Fowler | Matthew K. Horton | Xiang Fu | Zilong Wang | Aliaksandra Shysheya | Jonathan Crabbé | Shoko Ueda | Roberto Sordillo | Lixin Sun | Jake A. Smith | Bichlien H. Nguyen | Hannes Schulz | Sarah Lewis | Chin‐Wei Huang | Ziheng Lu | Yichi Zhou | Han Yang | Hongxia Hao | Jielan Li | Chunlei Yang | Wenjie Li | Ryota Tomioka | Tian Xie
Source: Nature (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

A generative model for inorganic materials design The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture<sup>1-3</sup>. Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints<sup>4-11</sup>. Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models<sup>4,12</sup>, structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design. Generative grammar ; Computer science ; Environmental science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Yujang Cho | Jong Won Beak | Mingyu Sagong | Seongcheol Ahn | Jong Seok Nam | Il‐Doo Kim
Source: Advanced Materials (review, 2025-04-07)
Field: Materials Science — Biomaterials

Electrospinning and Nanofiber Technology: Fundamentals, Innovations, and Applications Electrospinning has emerged as a transformative technique for fabricating nanofibers (NFs), offering precise control over their morphology, composition, and functionality. This versatile process facilitates the production of fibers ranging from nanoscale to microscale with customized properties, integrating diverse materials and architectures for advanced research and industrial applications. This review presents recent advancements in electrospinning, addressing its fundamental principles, nanomaterial synthesis methods, and examples from a wide range of applications. The significant progress that are made in fabricating polymer, metal oxide, carbon, and composite NFs with diverse architectures such as porous, core-shell, hollow, and aligned structures is highlighted. Advanced electrospinning techniques, including coaxial electrospinning, aligned electrospinning, yarn electrospinning, and roll-to-roll processes, demonstrate the scalability and adaptability of electrospinning for the development of next-generation nanomaterials. Electrospun NFs are being actively applied to functional membranes, gas sensors, energy systems, and catalytic processes, addressing critical challenges in these respective areas. In conclusion, the groundbreaking potential of integrating artificial intelligence (AI)-driven optimization with sustainable material design, such as the use of environmentally-friendly "green" solvents, is emphasized. In the end, leveraging robotics-based electrospinning and AI-enhanced methodologies is essential to achieve stable scalability, optimized performance, and sustainability for research and industry. Electrospinning ; Materials science ; Nanotechnology ; Nanofiber ; Microscale chemistry ; Nanomaterials ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Chao Qian | Ido Kaminer | Hongsheng Chen
Source: Nature Communications (review, 2025-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A guidance to intelligent metamaterials and metamaterials intelligence The bidirectional interactions between metamaterials and artificial intelligence have recently attracted immense interest to motivate scientists to revisit respective communities, giving rise to the proliferation of intelligent metamaterials and metamaterials intelligence. Owning to the strong nonlinear fitting and generalization ability, artificial intelligence is poised to serve as a materials-savvy surrogate electromagnetic simulator and a high-speed computing nucleus that drives numerous self-driving metamaterial applications, such as invisibility cloak, imaging, detection, and wireless communication. In turn, metamaterials create a versatile electromagnetic manipulator for wave-based analogue computing to be complementary with conventional electronic computing. In this Review, we stand from a unified perspective to review the recent advancements in these two nascent fields. For intelligent metamaterials, we discuss how artificial intelligence, exemplified by deep learning, streamline the photonic design, foster independent working manner, and unearth latent physics. For metamaterials intelligence, we particularly unfold three canonical categories, i.e., wave-based neural network, mathematical operation, and logic operation, all of which directly execute computation, detection, and inference task in physical space. Finally, future challenges and perspectives are pinpointed, including data curation, knowledge migration, and imminent practice-oriented issues, with a great vision of ushering in the free management of entire electromagnetic space. Metamaterial ; Computer science ; Artificial intelligence ; Invisibility ; Artificial neural network ; Physics Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Underwater Vehicles and Communication Systems
Authors: Qimin Fang | Qiuye Sun | Jinming Ge | Haiwang Wang | Jian Qi
Source: Catalysts (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Multidimensional Engineering of Nanoconfined Catalysis: Frontiers in Carbon-Based Energy Conversion and Utilization Amid global efforts toward carbon neutrality, nanoconfined catalysis has emerged as a transformative strategy to address energy transition challenges through precise regulation of catalytic microenvironments. This review systematically examines recent advancements in nanoconfined catalytic systems for carbon-based energy conversion (CO2, CH4, etc.), highlighting their unique capability to modulate electronic structures and reaction pathways via quantum confinement and interfacial effects. By categorizing their architectures into dimension-oriented frameworks (1D nanotube channels, 2D layered interfaces, 3D core-shell structures, and heterointerfaces), we reveal how geometric constraints synergize with mass/electron transfer dynamics to enhance selectivity and stability. Critical optimization strategies—including heteroatom doping to optimize active site coordination, defect engineering to lower energy barriers, and surface modification to tailor local microenvironments—are analyzed to elucidate their roles in stabilizing metastable intermediates and suppressing catalyst deactivation. We further emphasize the integration of machine learning, in situ characterization, and modular design as essential pathways to establish structure–activity correlations and accelerate industrial implementation. This work provides a multidimensional perspective bridging fundamental mechanisms with practical applications to advance carbon-neutral energy systems. Materials science ; Nanotechnology ; Biochemical engineering ; Engineering Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Li Lin | Ramón A. Álvarez‐Puebla | Luis M. Liz‐Marzán | Matt Trau | Jing Wang | Laura Fabris | Xiang Wang | Guokun Liu | Shuping Xu | Xiao Han | Liangbao Yang | Aiguo Shen | Shikuan Yang | Yikai Xu | Chunchun Li | Jinqing Huang | Shao-Chuang Liu | Jian‐An Huang | Indrajit Srivastava | Ming Li | Limei Tian | Lam Bang Thanh Nguyen | Xinyuan Bi | Dana Cialla‐May | Pavel Matousek | Nicholas Stone | Randy P. Carney | Wei Ji | Wei Song | Zhou Chen | In Yee Phang | Malou Henriksen‐Lacey | Hao Chen | Zongyu Wu | Heng Guo | Hao Ma | Gennadii Ustinov | Si-Heng Luo | Sara Mosca | Benjamin Gardner | Yi‐Tao Long | Jürgen Popp | Bin Ren | Shuming Nie | Bing Zhao | Xing Yi Ling | Jian Ye
Source: ACS Applied Materials & Interfaces (review, 2025-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and <i>in vivo</i> deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging. Materials science ; Nanotechnology ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Spectroscopy ; Engineering physics ; Raman scattering ; Optics ; Engineering Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Ryan Jacobs | Dane Morgan | Siamak Attarian | Jun Meng | Chen Shen | Zhenghao Wu | Clare Yijia Xie | Julia H. Yang | Nongnuch Artrith | Ben Blaiszik | Gerbrand Ceder | Kamal Choudhary | Gábor Csányi | Ekin D. Cubuk | Bowen Deng | Ralf Drautz | Xiang Fu | Jonathan Godwin | Vasant Honavar | Olexandr Isayev | Anders Johansson | Boris Kozinsky | Stefano Martiniani | Shyue Ping Ong | Igor Poltavsky | KJ Schmidt | So Takamoto | Aidan P. Thompson | Julia Westermayr | Brandon M. Wood
Source: Current Opinion in Solid State and Materials Science (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

A practical guide to machine learning interatomic potentials – Status and future Interatomic potential ; Computer science ; Data science ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ilyes Batatia | Simon Batzner | Dávid Péter Kovács | Albert Musaelian | Gregor N. C. Simm | Ralf Drautz | Christoph Ortner | Boris Kozinsky | Gábor Cśanyi
Source: Nature Machine Intelligence (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

The design space of E(3)-equivariant atom-centred interatomic potentials Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets. Extrapolation ; Computer science ; Tensor (intrinsic definition) ; Equivariant map ; Benchmark (surveying) ; Set (abstract data type) ; Theoretical computer science ; Atom (system on chip) ; Cluster (spacecraft) ; Algorithm ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Parallel computing ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Francisco D. Rodríguez-Gómez | Dominique Monferrer | Oriol Penon | Pilar Rivera Gil
Source: Frontiers in Medicine (review, 2025-03-05)
Field: Materials Science — Materials Chemistry

Regulatory pathways and guidelines for nanotechnology-enabled health products: a comparative review of EU and US frameworks The integration of nanotechnology into healthcare has introduced Nanotechnology-Enabled Health Products (NHPs), promising revolutionary advancements in medical treatments and diagnostics. Despite their potential, the regulatory navigation for these products remains complex and often lagging, creating barriers to their clinical application. This review article focuses on dissecting the regulatory landscape for NHPs, particularly in the European Union and the United States, to identify applicable requirements and the main regulatory guidelines currently available for meeting regulatory expectations. Lagging ; European union ; Nanotechnology ; Regulatory science ; Health care ; Healthcare system ; Risk analysis (engineering) ; Business ; Management science ; Political science ; Engineering ; Medicine ; International trade Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Biomedical Ethics and Regulation
Authors: Dávid Péter Kovács | J. Harry Moore | Nicholas J. Browning | Ilyes Batatia | Joshua T. Horton | Yixuan Pu | Venkat Kapil | William C. Witt | Ioan-Bogdan Magdău | Daniel J. Cole | Gábor Cśanyi
Source: Journal of the American Chemical Society (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost. Chemistry ; Mace ; Molecule ; Range (aeronautics) ; Organic molecules ; Nanotechnology ; Chemical physics ; Aerospace engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Hongfu Liu | Jijun Li | Hua Yang | Junqiao Wang | Boxun Li | Han Zhang | Yougen Yi
Source: Photonics (article, 2025-05-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

TiN-Only Metasurface Absorber for Solar Energy Harvesting With global energy demand surging and traditional energy resources diminishing, the solar absorber featuring optimized design shows substantial potential in areas like power generation. This study proposes a solar absorber that is insensitive to wide-angle incidence and polarization. It has a cylindrical structure with square holes, which is constructed from titanium nitride (TiN). The calculation results indicate that, for plane waves, the average absorption of this solar absorber across the wavelength range of 300–2500 nm reaches 92.4%. Moreover, its absorption rate of the solar spectrum corresponding to AM1.5 reaches 94.8%. The analysis of the characteristics within the electric and magnetic field profiles indicates that the superior absorption properties arise from a cooperative resonance effect. This effect originates from the interaction among surface plasmon resonance, guided-mode resonance, and cavity resonance. In this study, the geometric parameters of the solar absorber’s structure significantly influence its absorption performance. Therefore, we optimized these parameters to obtain the optimal values. Even at a large incident angle, this absorber maintains high absorption performance and shows insensitivity to the polarization angle. The findings expected from this study are likely to be of considerable practical importance within the realm of solar photothermal conversion. Tin ; Energy harvesting ; Solar energy ; Materials science ; Optoelectronics ; Optics ; Energy (signal processing) ; Engineering physics ; Physics ; Electrical engineering ; Engineering Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Wireless Power Transfer Systems
Authors: Anber Abraheem Shlash Mohammad | Khaleel Ibrahim Al-Daoud | Maher Ali Rusho | Ahmad Alkhayyat | Hardik Doshi | Pranab Dey | Kunal Gagneja | M. Janaki Ramudu | Asokan Vasudevan | Mohammad Javad Kiani
Source: Microchemical Journal (article, 2025-02-04)
Field: Chemistry — Spectroscopy

Modeling polyethylene glycol density using robust soft computing methods Polyethylene glycol ; Soft computing ; Computer science ; Biological system ; Materials science ; Process engineering ; Chemistry ; Organic chemistry ; Artificial intelligence ; Engineering ; Artificial neural network Analytical Chemistry and Chromatography