jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tongyu Liu | Wenxiang Zhao | Lijing Xie | Engao Peng | Feinong Gao
Source: International Journal of Impact Engineering (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Material constitutive modeling over a wide strain rate range by integration of cylinder and cap sample SHPB tests Constitutive equation ; Materials science ; Strain rate ; Strain (injury) ; Structural engineering ; Range (aeronautics) ; Cylinder ; Composite material ; Forensic engineering ; Mechanical engineering ; Engineering ; Finite element method High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Microstructure and mechanical properties
Authors: Greta M. Jacobson | Lixue Cheng | Vignesh C. Bhethanabotla | Jiace Sun | Anne B. McCoy
Source: The Journal of Physical Chemistry A (article, 2025-03-19)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Developing Potential Surfaces: Applications to OH <sup>–</sup> (H <sub>2</sub> O) <sub> <i>n</i> </sub> ( <i>n</i> = 1–3) Complexes An approach for obtaining high-level <i>ab initio</i> potential surfaces is described. The approach takes advantage of machine learning strategies in a two-step process. In the first, the molecular-orbital based machine learning (MOB-ML) model uses Gaussian process regression to learn the correlation energy at the CCSD(T) level using the molecular orbitals obtained from Hartree-Fock calculations. In this work, the MOB-ML approach is expanded to use orbitals obtained using a smaller basis set, aug-cc-pVDZ, as features for learning the correlation energies at the complete basis set (CBS) limit. This approach is combined with the development of a neural-network potential, where the sampled geometries and energies that provide the training data for the potential are obtained using a diffusion Monte Carlo (DMC) calculation, which was run using the MOB-ML model. Protocols are developed to make full use of the structures that are obtained from the DMC calculation in the training process. These approaches are used to develop potentials for OH<sup>-</sup>(H<sub>2</sub>O) and H<sub>3</sub>O<sup>+</sup>(H<sub>2</sub>O), which are used for subsequent DMC calculations. The results of these calculations are compared to those performed using previously reported potentials. Overall, the results of the two sets of DMC calculations are in good agreement for these very floppy molecules. Potentials are also developed for OH<sup>-</sup>(H<sub>2</sub>O)<sub>2</sub> and OH<sup>-</sup>(H<sub>2</sub>O)<sub>3</sub>, for which there are not available potential surfaces. The results of DMC calculations for these ions are compared to those for the corresponding H<sub>3</sub>O<sup>+</sup>(H<sub>2</sub>O)<sub>2</sub> and H<sub>3</sub>O<sup>+</sup>(H<sub>2</sub>O)<sub>3</sub> ions. It is found that the level of delocalization of the shared proton is similar for a hydroxide or hydronium ion bound to the same number of water molecules. This finding is consistent with the experimental observation that these sets of ions have similar spectra. Chemistry ; Physics ; Radiochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors
Authors: Peng Ai | Shuwei Tang | Shulin Bai | Da Wan | Xiaodong Li | Wanrong Guo | T. F. Zheng | H. Wang | Pengfei Zhang
Source: Langmuir (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Synergistically Tuning Thermoelectric Properties of BaCdPF via Strain Engineering The layered BaCdPF compound with the ZrSiCuAs-type structure emerges as a promising thermoelectric (TE) material due to the excellent electronic transport properties under the <i>p</i>-type doping circumstance. However, the fundamental mechanisms that regulate its thermal and electronic transport under strain engineering remain largely unexplored, hindering its practical applications. In current work, the crystal structure, thermal transport, and electronic transport properties of layered BaCdPF under strain engineering are systematically investigated by first-principles calculations in combination with a machine-learning interatomic potential approach. The BaCdPF maintains mechanical robustness, high dynamic and thermal stabilities across a wide range of strain conditions. The coexistence of band degeneracy and anisotropic band dispersions (heavy and light bands) at the valence band maximum plays a critical role in enhancing electronic transport properties. Under tensile strain, the chemical bond softening and four-phonon scattering favors two-dimensional (2D) phonon transport characteristics, yielding a low lattice thermal conductivity of 1.08 W m<sup>-1</sup>K<sup>-1</sup> at 300 K under a -4% tensile strain. Conversely, compressive strain significantly enhances the carrier mobility, promoting three-dimensional (3D) electronic transport. The current work not only provides effective strategies for optimizing the TE performance of layered BaCdPF but also sheds light on the distinct interplay of 3D charge and 2D phonon transport under strain engineering, which offers broader implications for the design of strain-modulated TE materials. Thermoelectric effect ; Strain engineering ; Strain (injury) ; Materials science ; Nanotechnology ; Engineering physics ; Optoelectronics ; Thermodynamics ; Engineering ; Physics ; Biology Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Chalcogenide Semiconductor Thin Films
Authors: Denise Adorno Lopes | Rinkle Juneja | Alicia Raftery | Matt Kurley | Will Cureton | Andrew Nelson
Source: Journal of Nuclear Materials (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products Fission products ; Degradation (telecommunications) ; Nuclear fission product ; Nuclear engineering ; Fission ; Nuclear fission ; Environmental science ; Chemistry ; Biochemical engineering ; Nuclear physics ; Radiochemistry ; Computer science ; Physics ; Engineering ; Neutron Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Jingan Wang | Yi Sun | Yuting Yang | Cheng Zhang | Weiqiang Zheng | Chen Wang | Wei Zhang | Lianqun Zhou | Hui Yu | Jinghong Li
Source: Advanced Science (article, 2025-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles. Image sequences are recorded by the state-of-the-art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep-SM can enhance signal detection and suppresses noise by leveraging spatio-temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. Deep-SM can provide significant scattering signal enhancement and noise reduction in dynamic imaging of biological nanoparticles as small as 10 nm, as well as the collision detection of metallic nanoparticle electrochemistry and quantum coupling with plasmonic microscopy. The high sensitivity and simplicity make this approach promising for routine use in nanoparticle analysis across diverse scientific fields. Plasmon ; Plasmonic nanoparticles ; Nanoparticle ; Microscopy ; Materials science ; Nanotechnology ; Sizing ; Optoelectronics ; Optics ; Chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Minghai Tang | Lei Wang | Jiangshan Song
Source: Computational Materials Science (article, 2025-04-05)
Field: Materials Science — Materials Chemistry

An advanced deep learning approach for energy absorption prediction in porous metals across diverse strain rate scenarios Strain (injury) ; Porosity ; Absorption (acoustics) ; Materials science ; Environmental science ; Engineering physics ; Composite material ; Physics ; Biology High-Velocity Impact and Material Behavior ; Cellular and Composite Structures ; Rock Mechanics and Modeling
Authors: Sudhaunsh Deshpande | Ajith Mohan Arjun | Guoyi Liu | Katarzyna Pawlak | Sanjiv Sharma
Source: Advanced Healthcare Materials (article, 2025-09-04)
Field: Chemistry — Electrochemistry

Redox‐Active Polyphenol Red Molecularly Imprinted Polymers on Porous Gold Electrodes for Ultrasensitive, AI‐Assisted Detection of Alzheimer's Biomarkers in Undiluted Biofluids Early diagnosis of Alzheimer's disease (AD) is hindered by the high cost, complexity, and centralization of current diagnostic platforms such as enzyme-linked immunosorbent assay (ELISA) and single-molecule array (SIMOA). Here, an integrated point-of-care (PoC) biosensing platform is reported based on redox-active polyphenol red molecularly imprinted polymers (pPhR MIPs) deposited on highly porous gold (HPG) electrodes for the ultrasensitive, reagent-free detection of phosphorylated tau 181 (p-tau 181) in undiluted plasma and serum. The unique electrochemical interface combines the signal-enhancing properties of HPG with the redox functionality of pPhR, eliminating the need for external redox probes. The resulting sensor achieves a limit of detection (LOD) of 980 fg mL<sup>-1</sup> and sensitivity of 4.39 µA/(pg mL<sup>-1</sup>), rivalling high-end lab-based assays while operating at a fraction of the cost. The platform integrates a low-cost, handheld potentiostat and a machine learning (ML)-enabled web app for real-time data classification and regression, achieving 100% classification accuracy with zero false positives or negatives. The system demonstrates compatibility with both ethylenediaminetetraacetic acid (EDTA) and heparin-treated samples and maintains accuracy in complex matrices. This work represents a step toward World Health Organization (WHO)-compliant, decentralized diagnostics for neurodegenerative disease, offering a scalable foundation for future multianalyte platforms targeting AD and related disorders. Molecularly imprinted polymer ; Detection limit ; Redox ; Reagent ; Biosensor ; Nanotechnology ; Chemistry ; Materials science ; Combinatorial chemistry ; Chromatography ; Biochemistry ; Inorganic chemistry ; Selectivity ; Catalysis ; Organic chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical chemistry methods development
Authors: Yingshuang Shan | C.L. Wang | Bo Wen | Wenjing Tang | Jing Wang | Hualong Chen | Kai Jiang | Xi Zhang | Wei Xia
Source: Carbon (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Perspective on low-dimensional carbon materials: Synergistic advancement of electromagnetic wave attenuation and AI-driven functionalization Surface modification ; Attenuation ; Perspective (graphical) ; Materials science ; Carbon fibers ; Nanotechnology ; Composite material ; Mechanical engineering ; Computer science ; Optics ; Physics ; Engineering ; Artificial intelligence Graphene research and applications ; Carbon and Quantum Dots Applications ; Supercapacitor Materials and Fabrication
Authors: Jiajie Xu | Shesen Guo | Miaolan Zhen | Zhuochen Yu | You‐Liang Zhu | Giuseppe Milano | Zhong‐Yuan Lu
Source: Materials Genome Engineering Advances (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

PyGAMD: Python graphics processing unit‐accelerated molecular dynamics software Abstract PyGAMD (Python GPU‐accelerated molecular dynamics software) is a molecular simulation platform developed from scratch. It is designed for soft matter, especially for polymer by integrating coarse‐grained/multi‐scale models, methods, and force fields. It essentially includes an interpreter of molecular dynamics (MD) which supports secondary programming so that users can write their own functions by themselves, such as analytical potential forms for nonbonded, bond, angle, and dihedral interactions in an easy way, greatly extending the flexibility of MD simulations. The interpreter is written by pure Python language, making it easy to be modified and further developed. Some built‐in libraries written by other languages that have been compiled for Python are added into PyGAMD to extend it's features, including configuration initialization, property analysis, etc. Machine learning force fields that are trained by DeePMD‐kit are supported by PyGAMD for conveniently implementing multi‐scale modeling and simulations. By designing an advanced framework of software, graphics processing unit‐acceleration achieved by the Numba library of Python and compute unified device architecture reaches a high computing efficiency. Python (programming language) ; Computer science ; Computer graphics (images) ; Graphics ; Software ; Graphics processing unit ; Programming language ; Operating system Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Iman Peivaste | Ahmed Makradi | Salim Belouettar
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Teaching artificial intelligence to perform rapid, resolution-invariant grain growth modeling via Fourier Neural Operator Artificial neural network ; Fourier transform ; Invariant (physics) ; Operator (biology) ; Fourier analysis ; Artificial intelligence ; Computer science ; Mathematics ; Mathematical analysis ; Chemistry Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Microstructure and mechanical properties
Authors: Xiaoyu Wu | Rui Zheng | Jianwen Jiang
Source: Journal of Chemical Theory and Computation (article, 2025-01-08)
Field: Chemistry — Inorganic Chemistry

Leveraging Cross-Diversity Machine Learning to Unveil Metal–Organic Frameworks with Open Copper Sites for Biogas Upgrading Biogas, primarily composed of methane (CH<sub>4</sub>) and carbon dioxide (CO<sub>2</sub>), is considered an alternative renewable energy resource. Efficient CO<sub>2</sub>/CH<sub>4</sub> separation is essential for biogas upgrading to increase energy density, and in this context, metal-organic frameworks (MOFs) have demonstrated significant potential. Here, we integrate multiscale modeling with cross-diversity machine learning (ML) to unveil MOFs with open copper sites (OCS-MOFs) that exhibit exceptional CO<sub>2</sub>/CH<sub>4</sub> separation performance. Our focus on diversity-adaptable ML guarantees that ML models trained in one chemical space are rigorously transferable to unseen MOFs from distinct chemical spaces, assuring their robustness in real-world applications. By leveraging a meticulously curated data set of 27592 OCS-MOFs, we develop ML models with high predictive accuracy, capable of identifying top-performing OCS-MOFs across diverse chemical environments. This work not only elucidates the reticular chemistry that governs optimal CO<sub>2</sub>/CH<sub>4</sub> separation performance in OCS-MOFs but also establishes a new benchmark for scalable and resilient digital MOF discovery, with cross-diversity accuracy as the key determinant of model transferability. Scalability ; Chemical space ; Computer science ; Biogas ; Metal-organic framework ; Robustness (evolution) ; Transferability ; Benchmark (surveying) ; Context (archaeology) ; Process engineering ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Chemistry ; Ecology ; Database ; Engineering ; Biology ; Bioinformatics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Xiaoye Zhang | Xinyi Chen | Jinglan Zhang | Fengyi Zhang | Yaxin Wang | Bin Ai | Yongjun Zhang | Xiaoyu Zhao
Source: Laser & Photonics Review (article, 2025-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Developing Neural Networks for Inverse Design of Chiral Metamaterials Abstract Chiral metamaterials, renowned for their unique optical properties such as circular dichroism, are pivotal in applications like spectroscopy, sensing, and imaging. However, their inherent asymmetry and complex light‐matter interactions present substantial design challenges. This study harnesses the power of deep neural networks (DNNs) for the inverse design of chiral nanohole arrays (CNAs). A bidirectional neural network (Bi‐DNN) is developed to address the one‐to‐many mapping issue, achieving high prediction accuracy (0.98). Various input‐output configurations are examined, including combining inputs and leveraging different input‐output models (e.g., using left‐handed circularly polarized light spectra to predict right‐handed circularly polarized spectra), enhancing prediction precision while reducing experimental workload. Additionally, the potential of CNAs as high‐performance surface‐enhanced Raman spectroscopy substrates for chiral detection is demonstrated. The Bi‐DNN enabled rapid and accurate design solutions, showing strong agreement with experimental validations. These findings emphasize the transformative role of DNNs in advancing chiral metamaterial design, unlocking efficient and customizable optical materials for next‐generation sensing and imaging technologies. Inverse ; Metamaterial ; Artificial neural network ; Computer science ; Materials science ; Mathematics ; Artificial intelligence ; Geometry ; Optoelectronics Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Kazuma Ito | Takashi Otaki | Tatsuya Yokoi | Katsutoshi Hyodo | Hideki Mori
Source: Communications Materials (article, 2025-12-26)
Field: Materials Science — Metals and Alloys

Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron Abstract Hydrogen embrittlement accompanied by cracking along general grain boundaries (GBs), which are characterized by a lack of crystallographic symmetry, is a persistent challenge in developing high-strength structural alloys. We develop a highly accurate and transferable machine learning interatomic potential (MLIP) for Fe–H by acquiring comprehensive and efficient learning data via simultaneous learning. Our MLIP accurately describes the density functional theory results for various lattice defects in α-Fe and their interactions with hydrogen, general GBs with hydrogen segregation, and their deformation and fracture behavior. Large-scale molecular dynamics simulations reveal that hydrogen can suppress &lt;111 &gt; /2 full dislocation emissions from general GBs and thereby potentially promote their fracture, supporting experimental suggestions. In contrast, for general GBs, where deformation twins are responsible for plasticity, the influence of hydrogen is minimal. This study contributes to the development of high-strength alloys by providing a robust MLIP construction methodology and new insights into hydrogen embrittlement mechanisms. Hydrogen embrittlement ; Grain boundary ; Embrittlement ; Hydrogen ; Interatomic potential ; Dislocation ; Deformation (meteorology) Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Dibyendu Maity | Suman Chakrabarty
Source: Journal of Chemical Theory and Computation (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

IceCoder: Identification of Ice Phases in Molecular Simulation Using Variational Autoencoder The identification and classification of different phases of ice within molecular simulations are challenging tasks due to the complex and varied phase space of ice, which includes numerous crystalline and amorphous forms. Traditional order parameters often struggle to differentiate between these phases, especially under the conditions of thermal fluctuations. In this work, we present a novel machine learning-based framework, <i>IceCoder</i>, which combines a variational autoencoder (VAE) with the smooth overlap of atomic position (SOAP) descriptor to classify a large number of ice phases effectively. Our approach compresses high-dimensional SOAP vectors into a two-dimensional latent space using VAE, facilitating the visualization and distinction of various ice phases. We trained the model on a comprehensive data set generated through molecular dynamics simulations and demonstrated its ability to accurately detect various phases of crystalline ice as well as liquid water at the molecular level. IceCoder provides a robust and generalizable tool for tracking ice phase transitions in simulations, overcoming the limitations of traditional methods. This approach may also be generalized to detect polymorphs in other molecular crystals, leading to new insights into the microscopic mechanisms underlying nucleation, growth, and phase transitions while maintaining computational efficiency. Autoencoder ; Molecular dynamics ; Computer science ; Nucleation ; Phase transition ; Identification (biology) ; Space (punctuation) ; Artificial intelligence ; Statistical physics ; Biological system ; Materials science ; Physics ; Chemistry ; Deep learning ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: P. Mukilan | Balasubramanian Murugesan | Jan Petrov | Jan Sobotík | R Narayanamoorthi
Source: Results in Engineering (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Sustainable tiles for renewable energy harvesting using integrated solar PV thermoelectric generator and piezoelectric technologies • Introduced eco-friendly tiles using 30% M-sand substitution for sustainability. • Integrated PV, TEG, and piezoelectric technologies for renewable energy harvesting. • Achieved superior thermal performance with optimized solar absorption and emissivity. • Applied predictive algorithms to enhance voltage generation and energy efficiency. This study introduces a novel method for sustainable energy solutions by creating eco-friendly energy-harvesting tiles that combine renewable energy production with environmental preservation. The suggested tiles include a 30% replacement of M-sand with torched fly ash, reducing dependence on natural resources while preserving structural integrity and improving thermal performance. The energy-harvesting capability is realised by integrating Solar Photovoltaic (PV) cells, Thermoelectric Generators (TEG), and Piezoelectric crystals, which jointly convert solar radiation, temperature differentials, and mechanical stress into electricity. The performance study indicates that the tiles possess a solar absorption of 0.256, solar reflectance of 0.81, thermal emissivity of 0.86, and a water absorption rate of 5.37%, assuring durability and resilience against environmental variables. Furthermore, predictive techniques like Support Vector Regression (SVR), Gradient Boosting, and Neural Networks enhanced energy production, with the Neural Networks model exhibiting greater prediction accuracy. The suggested tiles provide a sustainable construction solution by integrating energy production with efficient material use, rendering them appropriate for urban infrastructure and innovative building applications. This dual-focus design tackles energy and environmental issues, fostering sustainable building techniques. Renewable energy ; Photovoltaic system ; Energy harvesting ; Piezoelectricity ; Thermoelectric generator ; Thermoelectric effect ; Engineering physics ; Generator (circuit theory) ; Sustainable energy ; Materials science ; Environmental science ; Electrical engineering ; Engineering ; Energy (signal processing) ; Physics ; Power (physics) Advanced Thermoelectric Materials and Devices ; Transition Metal Oxide Nanomaterials ; Chalcogenide Semiconductor Thin Films