jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Deng | Kebin Fan | Biaobing Jin | Jordan M. Malof | Willie J. Padilla
Source: Applied Physics Reviews (article, 2025-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-informed learning in artificial electromagnetic materials The advent of artificial intelligence—deep neural networks (DNNs) in particular—has transformed traditional research methods across many disciplines. DNNs are data driven systems that use large quantities of data to learn patterns that are fundamental to a process. In the realm of artificial electromagnetic materials (AEMs), a common goal is to discover the connection between the AEM's geometry and material properties to predict the resulting scattered electromagnetic fields. To achieve this goal, DNNs usually utilize computational electromagnetic simulations to act as ground truth data for the training process, and numerous successful results have been shown. Although DNNs have many demonstrated successes, they are limited by their requirement for large quantities of data and their lack of interpretability. The latter results because DNNs are black-box models, and therefore, it is unknown how or why they work. A promising approach which may help to mitigate the aforementioned limitations is to use physics to guide the development and operation of DNNs. Indeed, this physics-informed learning (PHIL) approach has seen rapid development in the last few years with some success in addressing limitations of conventional DNNs. We overview the field of PHIL and discuss the benefits of incorporating knowledge into the deep learning process and introduce a taxonomy that enables us to categorize various types of approaches. We also summarize deep learning principles which are critical to PHIL understanding and the Appendix covers some of the physics of AEMs. A few specific PHIL works are highlighted and serve as examples of various approaches. Finally, we provide an outlook detailing where the field is currently and what we can expect in the future. Physics ; Cognitive science ; Artificial intelligence ; Mathematics education ; Computer science ; Psychology Metamaterials and Metasurfaces Applications ; Electromagnetic Simulation and Numerical Methods ; Photonic Crystals and Applications
Authors: Hao‐Bo Guo | Chunlei Dong | Qingquan Han | Hongyu Jiang | Hongji Sun | Xuemei Sun | Songlin Zhang | Huisheng Peng
Source: ACS Central Science (review, 2025-06-03)
Field: Materials Science — Materials Chemistry

Advancing Carbon Nanotube Fibers: Addressing Challenges from Production to Application Carbon nanotube (CNT) fibers, renowned for their theoretically high tensile strength, low density, and outstanding electrical conductivity, are promising candidates for cutting-edge applications in wearable electronics, bioengineering, and aerospace engineering. Despite their immense potential, the widespread adoption of CNT fibers faces critical barriers, including the challenge of enhancing macroscopic fiber performance and achieving scalable, consistent production. The extraordinary intrinsic properties of individual CNTs are not fully transferred to macroscopic fibers due to weak intertube interactions, misalignment, and structural defects. Among the available production methods, floating catalyst chemical vapor deposition stands out for its promise to enable the large-scale synthesis of CNT fibers. However, achieving consistent quality and scalability via this technique remains a significant obstacle. This outlook highlights the importance of innovative strategies for multiscale performance optimization and advances in scalable fabrication methodologies. A distinctive perspective on CNT fiber production is provided, emphasizing the integration of machine learning with process optimization strategies to enhance the uniformity and efficiency. The outlook systematically discusses these challenges, exploring strategies for multiscale performance optimization, scaled-up fabrication methodologies, and efficient manufacturing processes. Additionally, it examines the transformative applications of CNT fibers across diverse industries, underscoring their potential to revolutionize next-generation technologies. Carbon nanotube ; Production (economics) ; Nanotechnology ; Computer science ; Business ; Materials science Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication ; Graphene research and applications
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AIQM2: organic reaction simulations beyond DFT Density functional theory (DFT) is the workhorse of reaction simulations, but it often suffers from either prohibitive cost or insufficient accuracy. In this work, we report AIQM2-the universal AI-enhanced QM method 2-as the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the high speed of AIQM2, which is orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction. Robustness (evolution) ; Computer science ; Computational chemistry ; Molecular dynamics ; Biochemical engineering ; Chemistry ; Biological system ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Mingyu Li | Kun Song | Jixiao He | Mingzhu Zhao | G.F. You | Jie Zhong | Mengxi Zhao | Arong Li | Yu Chen | Guobin Li | Ying Kong | Jiacheng Wei | Zhaofu Wang | Jiamin Zhou | Hongbing Yang | Shichao Ma | Hailong Zhang | Irakoze Loïca Mélita | Weidong Lin | Yuhang Lu | Zhengtian Yu | Xun Lu | Yu‐Jun Zhao | Jian Zhang
Source: Nature Machine Intelligence (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol Electron ; Medicine ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Molecular Junctions and Nanostructures
Authors: Jia‐Hong Tian | Zhe Zheng | Yu‐Chen Pan | Yuefei Wang | Dong‐Sheng Guo
Source: Responsive materials (article, 2025-01-26)
Field: Chemistry — Spectroscopy

Macrocycle‐based differential sensing: Design strategies and applications Abstract Differential sensing, inspired by the olfactory systems in mammals, utilizes the cross‐reactivity of multiple sensor units toward analytes to generate a distinctive fingerprint for each analyte. Widely acknowledged as a robust analytical technique, differential sensing has entered a flourishing era with the advancement of machine learning. Nevertheless, developing sensor units and optimizing signal transduction remain significant tasks left to chemists. Macrocyclic receptors serve as promising materials for constructing sensor arrays with enhanced cross‐reactivity, facilitated by their ease of synthesis and derivatization, inherent broad‐spectrum encapsulation capability, and compatibility with multiple responsive signal transduction approaches. Herein, we present a concise overview of the fundamental processes involved in a sensor array, encompassing array construction, signal transduction, and data acquisition and analysis, with an emphasis on the unique advantages provided by macrocyclic receptors in the former two aspects. Then, we present fascinating application scenarios where macrocyclic receptors shine in differential sensing that rely on various ingenious sensing strategies. Finally, we discuss several issues with potential improvement and future directions for macrocyclic receptor‐based differential sensing, offering a forward‐looking perspective. Differential (mechanical device) ; Computer science ; Engineering ; Aerospace engineering Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials
Authors: Andrei Agafonov | Nayely Pineda-Romero | Matthew Witman | Veronica Enblom | Martin Sahlberg | Vivian Nassif | Lei Lei | David M. Grant | Martin Dornheim | Sanliang Ling | Vitalie Stavila | Claudia Zlotea
Source: ACS Applied Materials & Interfaces (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Promising Alloys for Hydrogen Storage in the Compositional Space of (TiVNb)<sub>100–<i>x</i></sub>(Cr,Mo)<sub><i>x</i></sub> High-Entropy Alloys This study reports on the search for the most promising alloys in the compositional space of (TiVNb)<sub>80</sub>Cr<sub>20-<i>x</i></sub>Mo<sub><i>x</i></sub> (<i>x</i> = 5, 10, and 15) and (TiVNb)<sub>75</sub>Cr<sub>25-<i>x</i></sub>Mo<sub><i>x</i></sub> (<i>x</i> = 5, 10, 15, and 20) high-entropy alloys. First, data-driven machine learning applied to these systems predicts that increasing the Mo content destabilizes the enthalpy of the hydride phases. Second, experimental and density functional theory (DFT) validations were performed. The as-prepared alloys have single-phase bcc lattices and rapidly absorb hydrogen to form fcc-type hydrides with a high capacity between 1.6 and 2.0 H/M. Despite a positive effect on the thermodynamics of the hydride phases, increasing the Mo content in these alloys has a negative effect on the maximum capacity. The cycling experiments highlight the need to balance the reversible capacity, cycle life, and crystalline stabilities of these phases. Therefore, considering all these results, the most promising alloy with trade-off properties within the targeted compositional space has been identified to be (TiVNb)<sub>75</sub>Cr<sub>5</sub>Mo<sub>20</sub> that shows a maximum capacity of 2.6 wt % (1.8 H/M), a reasonable enthalpy of hydride formation (-38.6 kJ/mol H<sub>2</sub>), and a notable gravimetric reversible capacity of 1.42 wt % at room temperature. To identify the most promising high-entropy alloys for this application, integrated machine learning predictions followed by experimental and DFT validations proved to be an effective strategy. Materials science ; Hydrogen storage ; High entropy alloys ; Hydrogen ; Space (punctuation) ; Metallurgy ; Thermodynamics ; Crystallography ; Engineering physics ; Alloy ; Physics ; Computer science Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Pierre Lanari | Mahyra Tedeschi
Source: Applied Computing and Geosciences (article, 2025-02-01)
Field: Chemistry — Spectroscopy

Chemical map classification in XMapTools Chemical mapping using electron beam or laser instruments is an important analytical technique that allows the study of the compositional variability of materials in two dimensions. While quantitative compositional mapping of minerals has received considerable attention over the last two decades, pixel misclassification in commonly used software solutions remains a fundamental limitation affecting several applications. Calibration of intensity maps to fully quantitative compositional maps requires accurate classification, for example when a calibration curve is applied to a group of pixels that are assumed to have the same matrix behavior under the electron beam or the laser. This paper compares seven automated supervised machine learning classification algorithms implemented in the open source XMapTools software along with various tools for manual classification, for selecting training data and assessing the quality of a classification result. This new implementation aims to provide the research and industry communities with a free software tool for fast and robust classification of chemical maps. A standardized color scheme with reference colors for minerals and mineral groups is proposed to improve the readability of the classified maps in petrological studies. The performance of each algorithm varies depending on the data set, especially when minerals exhibit strong compositional zoning or when different minerals have similar compositions for a given element. The random forest algorithm based on bootstrap aggregation provides satisfactory results in most situations and is recommended for general use in XMapTools. • Rapid classification of multi-dimensional chemical dataset • Implementation and test of multiple machine learning algorithms • Training sets are crucial for accurate classification • Random forest is combined with principal component analysis • Classification algorithms are implemented in XMapTools Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Various Chemistry Research Topics
Authors: Wenzhu Luo | Ershuai Yin | Lei Wang | Wenlei Lian | Neng Wang | Qiang Li
Source: International Journal of Heat and Mass Transfer (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces Materials science ; Semiconductor ; Heat transfer ; Mechanics ; Optoelectronics ; Physics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Intermetallics and Advanced Alloy Properties
Authors: Chen Chen | Yanhu Zhang | Yi Zheng | Yi Zhang | Hongyi Liu | Jiayang Wu | Liang Yang | Zhengbao Yang
Source: Advanced Materials (review, 2025-06-04)
Field: Chemistry — Organic Chemistry

Topology in Biological Piezoelectric Materials Topology is fundamental in determining the properties and functions of biological piezoelectric materials by influencing service performances across multiple scales, from nanoscale molecular arrangements to macroscopic assembly structures. At each scale, topology governs electrical, mechanical, and biological behaviors, facilitating multifunctional integration and multi-field coupling advances. Recent progress demonstrates the potential of topological optimization to enhance piezoelectric coefficients and enable complex functionalities. Strategies such as multi-scale design, machine learning-guided optimization, and precision fabrication techniques are being explored to address persistent challenges, including limited energy conversion efficiency, long-term stability, and biocompatibility. Critical applications include health monitoring, biosensing, energy harvesting, and disease treatment, highlighting opportunities and unresolved technical bottlenecks. Future research directions are discussed to present theoretical insights and practical pathways to the development of biological piezoelectric materials. Piezoelectricity ; Topology optimization ; Nanotechnology ; Materials science ; Topology (electrical circuits) ; Field (mathematics) ; Scale (ratio) ; Computer science ; Systems engineering ; Engineering ; Electrical engineering ; Physics Polydiacetylene-based materials and applications ; Antimicrobial Peptides and Activities ; Advanced Sensor and Energy Harvesting Materials
Authors: M. Ramesh | M. Tamil Selvan | P. Sreenivas | A. Felix Sahayaraj
Source: International Journal of Biological Macromolecules (article, 2025-03-02)
Field: Materials Science — Polymers and Plastics

Advanced machine learning-driven characterization of new natural cellulosic Lablab purpureus fibers through PCA and K-means clustering techniques Lablab purpureus ; Cellulosic ethanol ; Cluster analysis ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Biology ; Materials science ; Botany ; Cellulose ; Nanotechnology ; Biochemistry Natural Fiber Reinforced Composites
Authors: Manjunath Shettar | Ashwini Bhat | Nagaraj N. Katagi | Mandya Channegowda Gowrishankar
Source: Journal of Composites Science (article, 2025-04-21)
Field: Materials Science — Polymers and Plastics

Experimental Investigation on Mechanical Properties of Glass Fiber–Nanoclay–Epoxy Composites Under Water-Soaking: A Comparative Study Using RSM and ANN Fiber-reinforced polymer composites are exposed to severe environmental conditions throughout their intended lifespan. It is essential to investigate how they age when exposed to cold and hot water to increase the durability of fiber-reinforced polymer composites. This work uses a hand lay-up process to create composites with different weight percentages of glass fiber, nanoclay, and epoxy. ASTM guidelines are followed for performing tensile and flexural tests. The input parameters, varying wt.% of glass fiber and nanoclay, are continuous, and the aging condition is deemed a categorical factor. The mechanical properties are considered as response variables (output). The mechanical properties are optimized using Response Surface Methodology (RSM), while Artificial Neural Networks (ANNs) provide a reliable predictive model with high correlation coefficients. The findings demonstrate that ANNs outperform RSM in flexural strength prediction, whereas RSM offers greater accuracy for tensile strength modeling. SEM analysis of the fracture surfaces reveals the causes of specimen failure under tensile load, with distinct differences between dry, cold, and boiling water-soaked specimens. Composite material ; Epoxy ; Materials science ; Glass fiber ; Response surface methodology ; Fiber ; Mathematics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Electrospun Nanofibers in Biomedical Applications
Authors: Giorgio Bonollo | Gauthier Trèves | Д. В. Комаров | Samman Mansoor | Elisabetta Moroni | Giorgio Colombo
Source: The Journal of Physical Chemistry Letters (review, 2025-04-03)
Field: Materials Science — Materials Chemistry

Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways and define cell phenotypes through dynamic mechanisms and interactions. Advances in structural biology and molecular simulations have revealed how protein systems respond to changes in their environments, such as ligand binding, stress conditions, or perturbations like mutations and post-translational modifications, influencing signal transduction and cellular phenotypes. Here, we discuss how computational approaches, ranging from molecular dynamics (MD) simulations to AI-driven methods, are instrumental in studying protein dynamics from isolated molecules to large assemblies. These techniques elucidate conformational landscapes, ligand-binding mechanisms, and protein-protein interactions and are starting to support the construction of multiscale realistic representations of highly complex systems, ranging up to whole cell models. With cryo-electron microscopy, cryo-electron tomography, and AlphaFold accelerating the structural characterization of protein networks, we suggest that integrating AI and Machine Learning with multiscale MD methods will enhance fundamental understating for systems of ever-increasing complexity, usher in exciting possibilities for predictive modeling of the behavior of cell compartments or even whole cells. These advances are indeed transforming biophysics and chemical biology, offering new opportunities to study biomolecular mechanisms at atomic resolution. Computer science ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Data science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jie Zhou | Zhengkun Luo | Y. Kou | Xiao Zhou | Xiaodong Li | Wei‐xian Zhang | Zilong Deng
Source: Chemical Engineering Journal (article, 2025-05-27)
Field: Materials Science — Biomaterials

Long-chain nanocellulose-based aerogels for water treatment: strategies from fabrication towards modification Nanocellulose ; Fabrication ; Chain (unit) ; Nanotechnology ; Materials science ; Chemical engineering ; Cellulose ; Engineering Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity
Authors: Kexin He | Wenhao Ran | Shaodi Xu | J. Wen | Shujun Qiu | Tingwei Liu | Kaiyao Xin | Yali Yu | Duanyang Liu | Qianqian Huang | G. Shen | Zhongming Wei | Ziqi Zhou
Source: Advanced Materials (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Polarization Signal Amplification of 2D GeSe‐Based Polarization‐Sensitive Photodetectors Polarization-sensitive photodetectors, resolving light intensity, wavelength, and polarization states, enable the characterization of probing microstructure, internal stress, and compositional heterogeneity. Polarizer-free polarization-sensitive photodetectors based on in-plane anisotropic 2D semiconductors offer potential for device miniaturization and on-chip integration, owing to their inherent linear dichroism and orientation-dependent carrier mobilities. Hundreds of in-plane anisotropic 2D materials have been successfully discovered; however, the limited anisotropic photocurrent ratio (PR<10) has hindered the practical application. Herein, a field-effect transistor (FET)-based amplification strategy, enhancing PR from 2.1 to 54.8 in 2D GeSe photodetectors is proposed. This significant PR enhancement arises from polarization-induced resistance variations dynamically modulating gate potentials. Coupled with a steep transistor subthreshold region, small gate fluctuations produce substantial drain current changes, amplifying output anisotropy sensitively. Evaluating four types of FET identifies SMT-Si transistors as optimal due to their high stability, sharp subthreshold, and excellent noise immunity. In addition, the amplified PR signal directly enhances image contrast and recognition accuracy. Notably, with a high-PR signal, the machine learning model achieves a recognition rate of 0.99 in only 17 training epochs, reflecting a computational cost reduction of over 60%. This work provides an effective strategy to enhance PR, benefiting from high-resolution polarization imaging and advanced optoelectronic sensing. Materials science ; Photodetector ; Optoelectronics ; Polarization (electrochemistry) ; Transistor ; Anisotropy ; Responsivity ; Photocurrent ; Field-effect transistor ; Subthreshold conduction ; Optics ; Physics ; Voltage 2D Materials and Applications ; Optical Polarization and Ellipsometry ; Plasmonic and Surface Plasmon Research