jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ranjithkumar Rajamani | Sinouvassane Djearamane
Source: NanoNEXT (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

Green Synthesis of Silver Nanoparticles: A Comprehensive Review For Sustainable Nanotechnology Applications An eco-friendly and biocompatible alternative to the conventional chemical and physical synthesis methods is the green synthesis of silver nanoparticles (AgNPs). The synthesis of AgNP through plant, microbial, algal and enzyme is critically examined in this comprehensive overview. While nucleation and growth kinetics are governed by reaction parameters such as pH, temperature and extract concentration, information derived from spectroscopy, electron microscopy and computational modelling identifies phenolic, amino and protein functionalities to dominate reduction and stabilization. Green AgNPs have a wide range of functional variety in biomedical, environmental and catalytic applications. They have strong antibacterial, anticancer, photocatalytic and sensing properties. Although there are still challenges with reproducibility, mass production and long-term durability, comparative studies show advantages in energy efficiency, safety and environmental compatibility. Predictive control over particle qualities may be possible through the combination of experimental optimization with artificial intelligence (AI) and machine learning (ML). To ensure eco-friendly and industrially scalable applications, the emerging fields include hybrid synthesis techniques, life-cycle-oriented risk assessment and regulatory coordination. The integration of AI-based design and green chemistry is a ground-breaking approach to sustainable nanotechnology. Green chemistry ; Biocompatible material ; Nanoparticle ; Sustainable energy ; Sustainable production ; Human health ; Scalability Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Nanotechnology research and applications
Authors: Isabel Nha Minh Le | Oriel Kiss | Julian Schuhmacher | Ivano Tavernelli | Francesco Tacchino
Source: New Journal of Physics (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Symmetry-invariant quantum machine learning force fields Abstract Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data. However, the trainability and scalability of such models are still limited, due to both theoretical and practical barriers. Inspired by recent developments in geometric classical and quantum machine learning, here we design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Finally, we perform a barren plateau analysis and numerically observe that our model does not exhibit a barren plateau in the shallow depth regime. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools. Quantum machine learning ; Physics ; Quantum ; Scalability ; Invariant (physics) ; Statistical physics ; Artificial intelligence ; Artificial neural network ; Ab initio ; Theoretical physics ; Computer science ; Quantum computer ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Wenxing Gao | Zhibin Wang | Qiang Li | Wenfeng Liu | Hao Guo | Li Shang
Source: Analytical Chemistry (article, 2025-05-17)
Field: Chemistry — Spectroscopy

Deep Learning-Assisted Sensor Array Based on Host–Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives Accurate and rapid discrimination of multiple explosives with high precision is of paramount importance for national security, ecological protection, and human health yet remains a significant challenge with conventional analytical techniques. Herein, we present an innovative deep learning-assisted artificial vision platform based on cyclodextrin-protected multicolor fluorescent gold nanoclusters (CD-AuNCs) with four distinct emission wavelengths, enabling the highly accurate discrimination of seven explosives. The sensor array leverages the host-guest interactions between the cyclodextrin ligands on the AuNCs' surface and the target explosives, generating unique fluorescence fingerprint patterns. Mechanistic studies reveal that the fluorescence enhancement of CD-AuNCs is attributed to ligand rigidification, while fluorescence quenching is primarily caused by photoinduced electron transfer between CD-AuNCs and explosives. The multicolor fluorescence responses are captured by using a smartphone, and the corresponding RGB values are simultaneously extracted. To enhance the recognition accuracy, a dense convolutional network (DenseNet) algorithm with advanced image recognition capability is integrated with the fluorescence sensor array. This platform achieves remarkable 100% recognition accuracy at a concentration of 200 μM, enabling the rapid and precise visual classification of explosives. The proposed strategy not only provides a powerful tool for on-site explosive monitoring but also offers a versatile platform for the intelligent detection of diverse analytes, demonstrating significant potential for real-world applications in environmental and security monitoring. Chemistry ; Identification (biology) ; Fluorescence ; Explosive material ; Host (biology) ; Sensor array ; Nanotechnology ; Computational biology ; Organic chemistry ; Machine learning ; Botany ; Ecology ; Computer science ; Optics Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies ; Luminescence and Fluorescent Materials
Authors: Ping Wang | Yuan Jiang | Weijie Liao | Rong Wang | Minjie Lai | Hongchao Kou | Xiubing Liang | Jinshan Li | Turab Lookman | Ruihao Yuan
Source: Acta Materialia (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Generalizable descriptors for automatic titanium alloys design by learning from texts via large language model Materials science ; Titanium ; Titanium alloy ; Metallurgy ; Alloy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xueying Lu | Chenxi Zhao | Huanyu Tu | Siyu Wang | Aihui Chen | Haibin Zhang
Source: Sustainable materials and technologies (article, 2025-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning Supercapacitor ; Power density ; Energy density ; Biomass (ecology) ; Materials science ; Power (physics) ; Carbon fibers ; Composite material ; Composite number ; Engineering physics ; Physics ; Chemistry ; Capacitance ; Thermodynamics ; Electrode ; Physical chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials
Authors: Love Bansal | Subin Kaladi Chondath | Bhumika Sahu | Deb K. Rath | Nikita Ahlawat | A. Gupta | Shailendra K. Saxena | Pooi See Lee | Rajesh Kumar
Source: ACS Energy Letters (article, 2025-12-24)
Field: Materials Science — Polymers and Plastics

Emerging Multivalent Electrochromic Batteries: Dual-Function Energy Storage with Visual Intelligence Integrating multiple compatible functionalities into one device is a great step toward more efficient and sustainable technology. One such approach, a multivalent electrochromic battery, integrates the electrochromic color modulation with the multivalent batteries to realize color-indicative batteries that also power an in-built electrochromic display. Despite huge advancements at the laboratory scale, notable gaps exist that hinder seamless integration of electrochromism and energy storage, therefore preventing multivalent electrochromic batteries from being a technological/industrial reality. Various strategies, including lattice engineering, cavity resonance, water-in-salt electrolyte, hybrid electrolyte, and quasi-solid-state gel electrolyte, have proven effective for the growth of multivalent electrochromic batteries. Various approaches have been discussed here, with a focus on electrode and electrolyte engineering toward multivalent electrochromic batteries. Additionally, several challenges associated with the technology have been underlined, along with suggested plans to address the bottlenecks. Modern tools like artificial intelligence and machine learning (AI/ML) may offer accelerated approaches to realizing the multivalent electrochromic batteries for a sustainable future. Electrochromism ; Electrochromic devices ; Electrolyte ; Energy storage ; Sustainable energy ; Electrode Transition Metal Oxide Nanomaterials ; Advanced battery technologies research ; Thermal Expansion and Ionic Conductivity
Authors: Kun Li | Haoyuan Du | Lei Liu | Hang Yang | Junfei Fang | Dong Li
Source: Journal of Industrial and Engineering Chemistry (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Research progress of machine learning in the field of photocatalysis applications Photocatalysis ; Field (mathematics) ; Nanotechnology ; Materials science ; Computer science ; Engineering physics ; Biochemical engineering ; Environmental science ; Chemistry ; Engineering ; Mathematics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Taiwo W. Quadri | Ekemini D. Akpan | Saheed E. Elugoke | Lukman O. Olasunkanmi | Sheetal | Ashish Kumar Singh | Balaram Pani | Jaya Tuteja | Sudhish Kumar Shukla | Chandrabhan Verma | Hassane Lgaz | Valentine Chikaodili Anadebe | Rakesh Chandra Barik | Lei Guo | Akram Alfantazi | B.M. Mothudi | Eno E. Ebenso
Source: Applied Physics Reviews (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors Artificial intelligence (AI) and machine learning (ML) have attracted the interest of the research community in recent years. ML has found applications in various areas, especially where relevant data that could be used for algorithm training and retraining are available. In this review article, ML has been discussed in relation to its applications in corrosion science, especially corrosion monitoring and control. ML tools and techniques, ML structure and modeling methods, and ML applications in corrosion monitoring were thoroughly discussed. Furthermore, detailed applications of ML in corrosion inhibitor design/modeling coupled with associated limitations and future perspectives were reported. Adsorption ; Corrosion ; Artificial intelligence ; Computer science ; Coordination complex ; Molecular machine ; Materials science ; Nanotechnology ; Chemistry ; Metallurgy ; Metal ; Organic chemistry Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Soham Mandal | Prabal K. Maiti
Source: Communications Materials (article, 2025-02-02)
Field: Materials Science — Materials Chemistry

Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials Abstract Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective utilization in greenhouse gas capture. Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity ( κ ) of CALF-20. We predict that the anisotropic thermal transport in CALF-20, with κ below 1 Wm −1 K −1 at 300 K, is ideal for thermoelectric applications. Our analysis reveals a weak temperature dependence ( κ ~ 1/ T 0.56 ) and near invariance with pressure in κ value of CALF-20, which stands out from the typical trend observed in crystalline materials. The outcome of the study, leveraging advanced computational techniques for predictive modeling, offers valuable insights into more suitable applications of CALF-20 with tailored thermal properties. Thermal conductivity ; Interatomic potential ; Materials science ; Thermal ; Computer science ; Artificial intelligence ; Machine learning ; Thermodynamics ; Chemistry ; Physics ; Composite material ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Physics and Applications
Authors: Emir Kocer | Andreas Singraber | Jonas A. Finkler | Philipp Misof | Tsz Wai Ko | Christoph Dellago | Jörg Behler
Source: The Journal of Chemical Physics (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Iterative charge equilibration for fourth-generation high-dimensional neural network potentials Machine learning potentials allow performing large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations, provided that the selected model is able to capture the relevant physics of the system. For systems exhibiting long-range charge transfer, fourth-generation machine learning potentials need to be used, which take global information about the system and electrostatic interactions into account. This can be achieved in a charge equilibration step, but the direct solution of the set of linear equations results in an unfavorable cubic scaling with system size, making this step computationally demanding for large systems. In this work, we propose an alternative approach that is based on the iterative solution of the charge equilibration problem (iQEq) to determine the atomic partial charges. We have implemented the iQEq method, which scales quadratically with system size, in the parallel molecular dynamics software LAMMPS for the example of a fourth-generation high-dimensional neural network potential (4G-HDNNP) intended to be used in combination with the n2p2 library. The method itself is general and applicable to many different types of fourth-generation MLPs. An assessment of the accuracy and the efficiency is presented for a benchmark system of FeCl3 in water. Benchmark (surveying) ; Computer science ; Charge (physics) ; Artificial neural network ; Quadratic growth ; Scaling ; Linear scale ; Range (aeronautics) ; Set (abstract data type) ; Software ; Statistical physics ; Work (physics) ; Algorithm ; Computational science ; Artificial intelligence ; Physics ; Mathematics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tajala Magray | Yashal Arif | Mithila Thombare | Thomas Ebel | Dattatray S. Dhawale | Pradip B. Sarawade
Source: Coordination Chemistry Reviews (article, 2025-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering heterostructures for electrocatalysis and energy storage: classification, design, computational insights and property modulation Heterojunction ; Rational design ; Supercapacitor ; Electrocatalyst ; Density functional theory ; Electrochemical energy storage ; Electrochemistry ; Water splitting Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Xian Wang | Yifan Li | Ying Zhang | Peng Wang | Yi-Ming Zhao | Jun Zhou | Jie Yang | Xuesen Wang | Lei Shen
Source: Physical review. B./Physical review. B (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

Machine learning assisted identification of homobilayer sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers Two-dimensional (2D) ferroelectric materials hold great promise for ultrahigh-density data storage and ultrafast memory applications in flexible or wearable electronic devices. Because of depolarizing field effects and high-energy barriers for switching, only a handful of 2D ferroelectrics have been reported experimentally, such as ${\mathrm{In}}_{2}{\mathrm{Se}}_{3}$ and ${\mathrm{CuInP}}_{2}{\mathrm{S}}_{6}$. Through high-throughput calculations we identified a catalog of 25 synthesizable sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers. The high-throughput process started with 6351 monolayer materials in 2dmatpedia, narrowed down to 79 monolayer semiconductors with honeycomb structures, leading to the construction of 474 homobilayers sliding ferroelectrics, and ultimately identifying 25 high-performance 2D sliding ferroelectrics. Using big-data analysis and machine learning, we also revealed strong correlations between polarization and key physical properties, especially a hidden factor of effective van der Waals radius, which has been overlooked in previous fewer-sample studies. Based on these mechanistic insights, we proposed machine learning descriptors for predicting sliding ferroelectric properties. Polarization (electrochemistry) ; Materials science ; Identification (biology) ; Computer science ; Engineering physics ; Psychology ; Condensed matter physics ; Physics ; Chemistry Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Multiferroics and related materials
Authors: Trieu Nguyen | Aleksandra Karolak
Source: Biophysical Journal (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Transformer graph variational autoencoder for generative molecular design In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on simplified molecular input line entry system representations for molecular input data, which can limit the diversity and novelty of generated molecules. To address this, we present the transformer graph variational autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus capturing the complex structural relationships within molecules more effectively than string models. To enhance molecular generation capabilities, TGVAE combines a transformer, graph neural network (GNN), and VAE. Additionally, we address common issues like over-smoothing in training GNNs and posterior collapse in VAEs to ensure robust training and improve the generation of chemically valid and diverse molecular structures. Our results demonstrate that TGVAE outperforms existing approaches, generating a larger collection of diverse molecules and discovering structures that were previously unexplored. This advancement not only brings more possibilities for drug discovery but also sets a new level for the use of AI in molecular generation. Autoencoder ; Transformer ; Generative grammar ; Graph ; Generative model ; Computer science ; Mathematics ; Artificial intelligence ; Theoretical computer science ; Engineering ; Electrical engineering ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jiangchuan Hu | Kai Ma | Zhenquan Zhang | Ruiming Zhang | Jinyang Zheng
Source: International Journal of Hydrogen Energy (article, 2025-04-01)
Field: Materials Science — Metals and Alloys

Machine learning-based prediction of hydrogen-assisted fatigue crack growth rate in Cr–Mo steel Paris' law ; Materials science ; Growth rate ; Hydrogen ; Metallurgy ; Composite material ; Crack closure ; Chemistry ; Fracture mechanics ; Mathematics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Д. О. Филатов | Ivan V. Mikheev | Мikhail А. Proskurnin
Source: Diamond and Related Materials (article, 2025-04-20)
Field: Materials Science — Materials Chemistry

Machine learning-guided ATR-FTIR for in-depth analysis of graphene oxide dispersions Graphene ; Fourier transform infrared spectroscopy ; Oxide ; Materials science ; Chemical engineering ; Nanotechnology ; Attenuated total reflection ; Chemistry ; Computer science ; Optics ; Physics ; Engineering ; Metallurgy Graphene research and applications ; Graphene and Nanomaterials Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Mehrab Pourmadadi | Salar Mohammadi Shabestari | Hamidreza Abdouss | Abbas Rahdar | Sonia Fathi‐karkan | Sadanand Pandey
Source: Seminars in Oncology (article, 2025-11-11)
Field: Materials Science — Biomaterials

Artificial intelligence-driven intelligent nanocarriers for cancer theranostics: A paradigm shift with focus on brain tumors Artificial intelligence (AI) and nanotechnology are revolutionizing brain cancer theranostics by enhancing drug delivery and diagnostic accuracy. This review examines AI-enhanced engineering strategies for developing intelligent nanocarriers that target glioblastoma and other metastatic central nervous system malignancies. AI encompasses several computational methods, including machine learning (ML) and its subset deep learning (DL). Here, ML algorithms learn design rules for nanocarriers, and DL networks intricate pattern recognition for tumor segmentation and adaptive release. These approaches enable stimuli-responsive nanocarriers to react to tumor microenvironmental signals (eg, pH, enzyme activity) and external stimuli (eg, ultrasound), optimizing targeted medication release while minimizing off-target effects. Magnetic resonance imaging (MRI) and positron emission tomography (PET), in conjunction with AI, enhance tumor detection and segmentation, while the integration of multiomics data facilitates tailored treatment planning. Advanced technologies encompass transferrin-functionalized nanoparticles for traversing the blood-brain barrier (BBB) and dual-stimuli-responsive drug delivery systems. Notwithstanding general progress, apprehensions surrounding batch variability and industrial scalability persist. This review also addresses ethical concerns and cost disparities associated with AI-based therapeutics. The primary development target areas are federated learning for data privacy, explainable artificial intelligence (XAI) for regulatory transparency, and quantum ML for molecular-scale optimization. This paper charts the course to patient-specific, scalable neuro-oncology nanomedicine through the convergence of computational modeling, intelligent materials, and advanced imaging modalities. These themes are explored in greater detail in the introduction, where we lay the groundwork for intelligent nanocarriers, their design with the help of AI, and the clinical need for diagnostics-therapeutics convergence in brain cancer. Nanocarriers ; Nanomedicine ; Deep learning ; Scalability ; Drug delivery ; Paradigm shift ; Targeted drug delivery ; Cancer ; Magnetic resonance imaging Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Glioma Diagnosis and Treatment
Authors: Aina Tian | Tao Ding | Luyao He | Kailang Dong | Linqi Zhu | Chun Chang | Lu Lv | Jiuchun Jiang
Source: Journal of Energy Storage (article, 2025-05-28)
Field: Chemistry — Electrochemistry

A novel method for joint estimation of SOC and SOP based on electrochemical modeling Estimation ; Joint (building) ; Computer science ; Engineering ; Systems engineering ; Civil engineering Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Fuel Cells and Related Materials