jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kamal Prasad Kamal
Source: Journal of Agriculture Sustainability and Environment (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

Silver Nanoparticles: Synthesis, Characterization, and Emerging Applications in Agriculture and Biomedicine for Enhancing Crop Production and Human Health Nanotechnology is an emerging field with vast potential in agriculture and biomedicine due to its unique physicochemical properties. Silver nanoparticles (SNPs), typically ranging from 1–100 nm, have garnered significant interest for their antimicrobial, antioxidant, and therapeutic effects. This review focuses on the synthesis, characterization, and multifunctional applications of SNPs in both the agricultural and biomedical sectors. Various synthesis methods, including biological approaches, are explored, along with the mechanisms through which SNPs exert their effects. In agriculture, SNPs have shown promise in enhancing soil quality, improving plant growth, and controlling crop diseases through nano-fertilizers and nano-pesticides. In the biomedical field, SNPs are used in antibacterial and antibiofilm treatments, wound healing, dentistry, bone and cardiac implants, and cancer therapy. Given the increasing global population and the urgent need for sustainable solutions in food and healthcare systems, SNPs offer a promising avenue for improving crop productivity and human health. Biomedicine ; Characterization (materials science) ; Crop production ; Human health ; Nanotechnology ; Production (economics) ; Agriculture ; Crop ; Nanoparticle ; Biotechnology ; Materials science ; Agronomy ; Medicine ; Geography ; Biology ; Environmental health ; Economics Nanoparticles: synthesis and applications
Authors: R. Langer
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

My Struggles and Dreams as a Chemical Engineer My career has not been straightforward. Although I am a chemical engineer, and I'm proud of that, I took a path from chemistry and engineering to one that also involved experimental biology and medicine. This was very unusual many decades ago. In so doing, I met with rejection and ridicule early in my career. However, by going down that path, I was able to make discoveries and inventions that I hope have saved and improved lives, and I've been able to train a great number of people who are going down the road I began traveling over many years ago. Career path ; Path (computing) ; Engineering ; Environmental ethics ; Engineering ethics ; Management ; Computer science ; Philosophy ; Engineering management ; Economics Photochromic and Fluorescence Chemistry ; Various Chemistry Research Topics ; Polymer composites and self-healing
Authors: Kerrowenergetics.org.uk, Cornwall, UK | Julian Andrew Perry
Source: Journal of Electrical Electronics Engineering (article, 2025-01-27)
Field: Chemistry — Electrochemistry

An Investigation into the Origins of Pulse-Induced Energy Gains in Electrochemical Systems In an earlier study, inductive pulse charging (IPC), using solenoid generated high voltage transients, also known as flyback or kickback pulses, have been shown to induce energy gains in Lead acid and LiFePO4 batteries, when using specific operational parameters, but with no clear indication as to the source of the additional energy. While there are presently no widely accepted theories or models regarding the energetic pathways and processes involved, it is proposed that there are only two viable possibilities for the source of the observed energy gains, as distinct from the actual mechanisms involved. The energy influx either derives from an internal response of the electrochemistry to high voltage electrostatic pulses, whereby enthalpic energy is released from the electrochemistry and serves as a form of ‘fuel’, or the energy influx derives from the local environment by as yet unrecognised processes and pathways. Here the battery is considered to function as part of a thermodynamically open system in the presence of ‘far from equilibrium’ events, such as those triggered by high voltage pulses. This follow-up study, undertaken again within the Open Science Framework (OSF), sets out to test the proposed hypothesis, that internal enthalpy is the source of any pulse-induced energy influx, by looking at evidence from three main areas. Firstly, the effect of pulses on capacitors, they being devoid of any functional electrochemistry, secondly, through thermodynamic analysis and bench testing of battery capacities in conjunction with a ‘chemical deficit model’, and thirdly, by looking at records of battery pulse and cyclic histories to identify any long-term effects on capacity. The results, in particular the correlation between predicted and measured battery capacities with both cell chemistries, together with their pulse histories, have clearly shown that the null hypothesis of an enthalpy source must be rejected in favour of the alternative, an external source and where the battery and the local environment comprise a thermodynamically open system. Consideration is also given to the possible implications of these findings for classical and quantum electrodynamic theory and how the integration of ‘non-linear’ and ‘far from equilibrium’ states might be seen as further evidence of the need for an extended and more complete model that includes interaction with the environment and otherwise anomalous phenomena. Electrochemistry ; Pulse (music) ; Energy (signal processing) ; Materials science ; Environmental science ; Engineering physics ; Electrical engineering ; Chemistry ; Physics ; Engineering ; Voltage ; Electrode Electrochemical Analysis and Applications ; Hydrogen embrittlement and corrosion behaviors in metals ; Laser Design and Applications
Authors: Sebastián Bonardd | Jon Maiz | Javier Maisueche | Ester Verde‐Sesto | José A. Pomposo
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Anthracene-functionalized dipolar glass copolymers as precursors for high-dielectric single-chain nanoparticles Dipolar glass copolymers (DGCs) incorporating 9-anthrylmethyl methacrylate (ANMA) with dipolar monomers 2-(methylsulfonyl)ethyl methacrylate (SO<sub>2</sub>MA) and 2-cyanoethyl methacrylate (CNMA) were synthesized <i>via</i> RAFT polymerization to develop high-performance dielectric materials with photo-responsive capabilities. Structural and spectroscopic analyses confirmed successful copolymer formation and anthracene incorporation, enabling ultraviolet (UV)-triggered functionalities. Thermal characterization revealed a significant increase in glass transition temperature (Δ<i>T</i> <sub>g</sub> ≈ +28 °C) compared to their homopolymer counterparts, despite a moderate reduction in thermal stability. Dielectric measurements demonstrated high permittivity and low loss factors across a broad temperature range, with distinct γ, β, and α relaxation processes identified. Upon UV irradiation, anthracene photodimerization enabled efficient intramolecular cross-linking under dilute, oxygen-free conditions, leading to the formation of discrete single-chain nanoparticles (SCNPs) as confirmed by combined UV-visible spectroscopy, size-exclusion chromatography, and dynamic light scattering analyses. We explore, for the first time, the use of programmable nanoscale architectures to develop a new generation of single-chain high-dielectric nanomaterials. These materials exhibit high permittivity, relatively low dielectric losses, and suitable thermal properties in terms of glass transition temperature. Our results show that the localized environment and the reduction of long-range entanglements, arising from the collapse of linear DGC precursors into single-chain nanoparticles, do not significantly alter the dielectric response of these entities. As a result, they provide efficient dielectric materials with potential applications in energy storage, flexible electronics, sensors, and smart materials. Glass transition ; Copolymer ; Methacrylate ; Dielectric ; Nanoparticle ; Polymerization ; Dynamic light scattering ; Anthracene ; Relaxation (psychology) Synthesis and properties of polymers ; Polydiacetylene-based materials and applications ; Liquid Crystal Research Advancements
Authors: Long-Fei Lv | Cai‐Rong Zhang | Cuicui Sang | Xiaomeng Liu | Meiling Zhang | Ji‐Jun Gong | Yuhong Chen | Hongshan Chen
Source: npj Computational Materials (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Integrating deep learning and symbolic regression for molecular design and virtual screening of organic solar cells The photovoltaic performance of organic solar cells (OSCs) is significantly determined by the electron donor and acceptor materials in active layers. Traditional trial-and-error experiments for exploring high-performance materials suffer from long development cycles, high experimental costs, and low screening efficiency. Herein, the established database includes 547 donor-acceptor pairs, integrating photovoltaic parameters and molecular representations. The 30 molecular structure descriptors that closely relate power conversion efficiency (PCE) were extracted. Long short-term memory networks (LSTM), convolutional neural networks (CNN), and symbolic regression (SR) were trained to predict the PCE of OSCs. After hyperparameter optimization via grid search algorithm, the metrics indicate the trained models achieved high-precision for PCE prediction, and the performance of LSTM model prevail over than that of other models. Through dual validation by SHapley Additive exPlanations(SHAP) interpretability analysis and SR formulas, it was revealed that the number of structural units with double rings or more in acceptor molecules showed the significant correlation with PCE. Based on the dataset constructed using molecular fragment recombination strategy, the developed LSTM generative model successfully generated 210,660 novel donor molecules and 878,268 acceptor molecules. Following screening of 185,015,936,880 donor-acceptor pairs by the LSTM prediction model, 5753 donor-acceptor pairs with the predicted PCE exceeding 18.50% were identified, among which the highest predicted PCE reached 18.66%. This approach provides theoretical guidance for the discovery of organic photovoltaic materials and may accelerate the development of high-performance OSCs, but also can be generalized to functional molecular design. Interpretability ; Photovoltaic system ; Organic solar cell ; Deep learning ; Convolutional neural network ; Hyperparameter ; Virtual screening ; Hyperparameter optimization ; Pattern recognition (psychology) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Feiyang Cai | Katelin Zacour | Tianyu Zhu | Tzuen‐Rong Tzeng | Yongping Duan | Ling Liu | Srikanth Pilla | Gang Li | Feng Luo
Source: Communications Chemistry (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

ChemFM as a scaling law guided foundation model pre-trained on informative chemicals Traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization across different tasks. Here, we introduce ChemFM, a large foundation model specifically developed for chemicals. By conducting a series of scaling experiments, we identify UniChem as the informative molecular database for pre-training the foundation model. ChemFM comprises 3 billion parameters and is pre-trained on 178 million molecules using self-supervised causal language modeling to extract generalizable molecular representations. This model can be adapted to diverse downstream chemical applications using either full-parameter or parameter-efficient fine-tuning methods. ChemFM consistently outperforms state-of-the-art task-specific AI models across all tested tasks. Notably, it achieves up to 67.48% performance improvement across 34 property prediction benchmarks, up to 33.80% reduction in mean average deviation between conditioned and actual properties of generated molecules in conditional molecular generation tasks, and up to 3.7% top-1 accuracy improvement across 4 reaction prediction datasets. Moreover, ChemFM demonstrates its superior performance in predicting antibiotic activity and cytotoxicity, highlighting its potential to advance the discovery of novel antibiotics. Furthermore, we demonstrate that, as a foundation model, ChemFM exhibits strong data efficiency, requiring significantly fewer labeled training samples to achieve state-of-the-art performance. We anticipate that ChemFM will significantly advance chemistry research by providing a foundation model capable of effectively generalizing across a broad range of tasks with minimal additional training. Generalization ; Foundation (evidence) ; Scalability ; Property (philosophy) ; Scaling ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Yousif Hussein Azeez | Rebaz Anwar Omer | Khdir Ahmed Othman | Karzan A. Omar | Aryan Fathulla Qader
Source: Orbital - The Electronic Journal of Chemistry (article, 2025-04-27)
Field: Chemistry — Analytical Chemistry

A Comprehensive Study of Pharmacologic Complexity: Study of Interactions and Properties of Paracetamol, Aspirin, Naproxen, and Diclofenac The present study explores the molecular and electronic properties of four widely used pharmaceutical compounds—Paracetamol, Aspirin, Naproxen, and Diclofenac—to enhance our understanding of their stability, reactivity, and potential applications in drug design. Through computational analysis, key electronic properties and intermolecular interactions were examined to reveal factors influencing their chemical behavior. Using density functional theory (DFT) with the B3LYP/6-31G(d,p) level, the study investigates their electronic structure, charge distribution, and optical characteristics. Reduced Density Gradient (RDG) and Non-Covalent Interaction (NCI) analyses highlight distinct intermolecular interactions, such as strong hydrogen bonding in Paracetamol, π→π* transition stabilization in Aspirin, dispersion interactions in Naproxen, and diverse attractive forces in Diclofenac. Natural Bond Orbital (NBO) analysis further elucidates electron delocalization effects. The study also evaluates Nonlinear Optical (NLO) properties, suggesting potential applications beyond pharmaceuticals. Quantum chemical parameters indicate that Aspirin has the highest ionization energy (10.18 eV), making it a strong electron acceptor, while Paracetamol demonstrates significant electron-donating ability. Molecular Electrostatic Potential (MEP) mapping provides insights into charge distribution and reactivity, while UV-Visible absorption spectra reveal optical characteristics relevant to various applications. These findings offer valuable insights into the fundamental electronic behavior of these pharmaceutical compounds, which may inform drug formulation, bioavailability studies, and potential modifications for enhanced therapeutic efficacy. By linking computational analysis with pharmaceutical applications, this study contributes to a deeper understanding of molecular interactions that govern drug stability and performance. Naproxen ; Diclofenac ; Aspirin ; Medicine ; Antipyretic ; Pharmacology ; Analgesic ; Internal medicine ; Alternative medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Pharmacogenetics and Drug Metabolism
Authors: S. M. Mamykin | Marc Ingram | Leyla Alieva | Varvara Privalova
Source: Frontiers in Mechanical Engineering (article, 2025-05-21)
Field: Materials Science — Metals and Alloys

Preventing electrically induced subsurface initiated pitting failures (incl. WSF, WEC, WEA) with copper based lubricant additives Premature bearing failures in large electric machines such as wind turbines and locomotives are often caused by White Etching Cracks (WEC), White Etching Areas (WEA), and White Structure Flaking (WSF)—phenomena that, despite extensive study, remain not fully understood. This work introduces a new hypothesis of WEC, WEA and WSF formation combining Garkunov’s scientific discoveries of “hydrogen wear of metals” and the “wearlessness effect” with the existing knowledge and offers a possible solution. To test this hypothesis and solution, a prototype lubricant containing oil-soluble copper salts was evaluated using an electrically induced WEC test on a three ring-on-roller tribometer. The test applied high contact pressures and direct current to simulate operating conditions, followed by subsurface serial sectioning to examine material degradation. The results showed that the reference lubricant exhibited typical WEC and WEA damage, while the copper-based candidate lubricant prevented such failures entirely. This outcome supports the idea that hydrogen activity plays a central role in damage formation, and that targeted additives can prevent it by reducing surface temperatures and forming protective film, which blocks hydrogen diffusion. These findings offer a scientifically grounded and practically viable solution to extend bearing life and prevent costly failures in high-load, high-reliability applications. Lubricant ; Copper ; Metallurgy ; Materials science ; Environmental science ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability ; Corrosion Behavior and Inhibition
Authors: Mikhail Teppone
Source: Preprints.org (preprint, 2025-07-14)
Field: Materials Science — Biomaterials

Nano-Organotherapy: Precision Medicine with Organ Specific Nano-Peptides Organotherapy is a field of medical practice that uses animal-origin organ extracts to treat the same organs in human. This therapeutic modality has gone through a long history from the empirical use of organ-specific extracts in antiquity to the use of nano-sized peptides today. The main features of organotherapy are the use of a mixture of the active ingredients; all active ingredients are derived from a natural source; the used concentration and combination of the active ingredients correspond to the natural concentration and combination of these substances in a healthy organ. Since the effectiveness of any treatment depends on the competence of the physician, so, the more knowledgeable and experienced the doctor who practices organotherapy, the higher the expected therapeutic effect, and the greater the likelihood that the treatment can be attributed to "Precision Medicine." Nano- ; Nanotechnology ; Materials science ; Composite material Nanoparticle-Based Drug Delivery
Authors: Kai Ruan | Yilong Xu | Ze-Feng Gao | Yang Liu | Yike Guo | Ji-Rong Wen | Hao Sun
Source: Nature Computational Science (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Discovering physical laws with parallel symbolic enumeration Symbolic regression has a crucial role in modern scientific research owing to its capability of discovering concise and interpretable mathematical expressions from data. A key challenge lies in the search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. Here, to this end, we introduce parallel symbolic enumeration (PSE) to efficiently distill generic mathematical expressions from limited data. Experiments show that PSE achieves higher accuracy and faster computation compared with the state-of-the-art baseline algorithms across over 200 synthetic and experimental problem sets (for example, improving the recovery accuracy by up to 99% and reducing runtime by an order of magnitude). PSE represents an advance in accurate and efficient data-driven discovery of symbolic, interpretable models (for example, underlying physical laws), and improves the scalability of symbolic learning. Bottleneck ; Symbolic regression ; Scalability ; Enumeration ; Symbolic data analysis ; Key (lock) ; Symbolic trajectory evaluation ; Computation Machine Learning in Materials Science ; Evolutionary Algorithms and Applications ; Model Reduction and Neural Networks
Authors: Abhisek Ganguly | Alessandro Gabbana | V. V. V. RAGHAVA RAO | Sauro Succi | Santosh Ansumali
Source: Machine Learning Science and Technology (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

A kinetic-based regularization method for data science applications Abstract We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets. Regularization (linguistics) ; Interpolation (computer graphics) ; Computer science ; Analogy ; Algorithm ; Kinetic energy ; Noisy data ; Mathematical optimization ; Applied mathematics ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Neural Networks and Applications
Authors: Lan Zhou | Hongyi Ling | Keqiang Yan | Kaiji Zhao | Xiaoning Qian | Raymundo Arróyave | Xiaofeng Qian | Shuiwang Ji
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery. Executable ; Workflow ; Python (programming language) ; Novelty ; Planner ; Scripting language ; Scientific discovery ; Robustness (evolution) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electronic and Structural Properties of Oxides