jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Grzegorz Matyszczak | Krzysztof Krawczyk | Albert Yedzikhanau
Source: Nanomaterials (review, 2025-02-11)
Field: Materials Science — Materials Chemistry

Computational Modeling of Properties of Quantum Dots and Nanostructures: From First Principles to Artificial Intelligence (A Review) Nanomaterials, including quantum dots, have gained more and more attention in the past few decades due to their extraordinary properties that make them useful for many applications, ranging from catalysis, energy generation and storage, biotechnology, and medicine to quantum informatics. Mathematical descriptions of the phenomena in which nanostructures are involved are of great demand because they may be utilized for the purpose of controlling these phenomena (e.g., the growth of nanostructures with certain sizes, shapes, and other properties). Such models may be of distinct nature, including calculations from first principles, ordinary and partial differential equations, and machine learning models (including artificial intelligence) as well. The aim of this article is to review the most important and useful computational and mathematical approaches for the description and control of processes involving nanostructures. Quantum dot ; Nanostructure ; Computer science ; Nanotechnology ; Nanomaterials ; Computational intelligence ; Quantum ; Artificial intelligence ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Qin Ouyang | Hui-Lin Chang | Z. Hugh Fan | S. Ma | Quansheng Chen | Zhonghua Liu
Source: Computers and Electronics in Agriculture (article, 2025-06-09)
Field: Chemistry — Analytical Chemistry

Monitoring changes in constituents during black tea fermentation using snapshot multispectral imaging and 1D-CNN enhanced with data augmentation Multispectral image ; Snapshot (computer storage) ; Black tea ; Computer vision ; Artificial intelligence ; Computer science ; Remote sensing ; Computer graphics (images) ; Environmental science ; Chemistry ; Geography ; Food science ; Database Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Romina Wild | Felix Wodaczek | Vittorio Del Tatto | Bingqing Cheng | Alessandro Laio
Source: Nature Communications (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy. Feature selection ; Python (programming language) ; Computer science ; Weighting ; Curse of dimensionality ; Artificial intelligence ; Interpretability ; Feature (linguistics) ; Differentiable function ; Pattern recognition (psychology) ; Classifier (UML) ; Feature vector ; Gradient descent ; Data mining ; Dimensionality reduction ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: K Fan | Yun Wang | Chengcheng Tian | Xiaohong Guan | Xubiao Luo | Yanbo Zhou
Source: Chemical Engineering Journal (article, 2025-08-18)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks for CO2 capture: Tailoring structure and function through modification strategies Metal-organic framework ; Function (biology) ; Surface modification ; Materials science ; Nanotechnology ; Chemical engineering ; Environmental science ; Chemistry ; Engineering ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Francesc Sabanés Zariquiey | Stephen E. Farr | Stefan H. Doerr | Gianni De Fabritiis
Source: Journal of Chemical Information and Modeling (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on the TensorNet architecture for small molecules that broadens the applicability to diverse drug-like compounds, including all important chemical elements and supporting charged molecules. Using established benchmarks, we show overall improved accuracy and correlation in binding affinity predictions compared with GAFF2 for molecular mechanics and ANI2-x for NNPs. Slightly less accuracy but comparable correlations with OPLS4. We also show that we can run the NNP simulations at 2 fs time step, at least two times larger than previous NNP models, providing significant speed gains. The results show promise for further evolutions of free energy calculations using NNPs while demonstrating its practical use already with the current generation. The code and NNP model are publicly available for research use. Artificial neural network ; Energy (signal processing) ; Biological system ; Statistical physics ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Artificial intelligence ; Mathematics ; Statistics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Kuan‐Chun Chen | Shih-En Chen | Chuan-Fang Tsao | Yu-Chieh Huang | Ru‐Shi Liu
Source: Progress in Materials Science (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Revolutionary near-infrared phosphors with emerging structures and mechanisms driving next-generation applications Phosphor ; Light-emitting diode ; Diode ; Energy transfer ; Rare earth ; Luminescence ; Broadband Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Zixiong Sun | Liming Diwu | Ruyue Gao | Peiyao Sun | Hongmei Jing | Sizhao Huang | Ye Tian | Zhuo Wang | Li Jin | Daniel Q. Tan
Source: Advanced Functional Materials (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Ultra‐High Energy Storage Performance in Ferro‐Superparaelectric Capacitors Abstract The development of high‐performance lead‐free dielectric capacitors based on BCZT ceramics has traditionally relied on compositional doping to enhance energy storage density and efficiency, though this strategy introduces considerable uncertainties. In this study, a machine learning‐assisted screening strategy is implemented, utilizing a AdaBoost model to predict the recoverable energy density of (1– x )BCZT– x BNZN ceramics. It is found that compositions with doping levels above 0.10 display superparaelectric (SPE) behavior at room temperature, and an optimized relaxor‐ferroelectric composition with 0.15 BNZN is identified by the model as having outstanding energy storage potential. The viscous polymer process (VPP) is subsequently employed, leading to further improvement in dielectric properties and the unprecedented formation of a core‐shell structure within the grains. Consequently, a record energy density of 11.6 J cm −3 with 92% efficiency and an ultrahigh breakdown electric field of 700 kV cm −1 is achieved in a superparaelectric relaxor ferroelectric (SPE‐RFE). Moreover, a discharge energy density of ≈3.2 J cm −3 at an ultrafast rate of 34 ns at 150 °C is demonstrated. Superior temperature, frequency, and fatigue stability highlight the potential of these ceramics for extreme environment applications, opening new avenues for advanced lead‐free dielectric energy storage materials. Materials science ; Energy storage ; Capacitor ; Supercapacitor ; Engineering physics ; Nanotechnology ; Electrical engineering ; Capacitance ; Power (physics) ; Voltage ; Engineering Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Multiferroics and related materials
Authors: Pinchen Xie | Yixiao Chen | E Weinan | Roberto Car
Source: Physical review. B./Physical review. B (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate We report a molecular dynamics study of quality of the ferroelectric phase transition in crystalline <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mi>PbTi</a:mi><a:msub><a:mtext>O</a:mtext><a:mn>3</a:mn></a:msub></a:mrow></a:math>. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases. Softening ; Ferroelectricity ; Condensed matter physics ; Materials science ; Phonon ; Phase transition ; Lead titanate ; Phase (matter) ; Thermal ; Lead (geology) ; Composite material ; Thermodynamics ; Physics ; Dielectric ; Optoelectronics ; Geology ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Yiping Hao | Xiaoxiao Lu | Bina Fu | Dong H. Zhang
Source: Journal of Chemical Theory and Computation (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

New Algorithms to Generate Permutationally Invariant Polynomials and Fundamental Invariants for Potential Energy Surface Fitting Symmetric functions, such as Permutationally Invariant Polynomials (PIPs) and Fundamental Invariants (FIs), are effective and concise descriptors for incorporating permutation symmetry into neural network (NN) potential energy surface (PES) fitting. The traditional algorithm for generating such symmetric polynomials has a factorial time complexity of <i>N!</i>, where <i>N</i> is the number of identical atoms, posing a significant challenge to applying symmetric polynomials as descriptors of NN PESs for larger systems, particularly with more than 10 atoms. Herein, we report a new algorithm which has only linear time complexity for identical atoms. It can tremendously accelerate generation process of symmetric polynomials for molecular systems. The proposed algorithm is based on graph connectivity analysis following the action of the generation set of molecular permutational group. For instance, in the case of calculating the invariant polynomials for a 15-atom molecule, such as tropolone, our algorithm is approximately 2 million times faster than the previous method. The efficiency of the new algorithm can be further enhanced with increasing molecular size and number of identical atoms, making the FI-NN approach feasible for systems with over 10 atoms and high symmetry demands. Invariant (physics) ; Algorithm ; Computer science ; Surface fitting ; Surface (topology) ; Mathematics ; Geometry ; Mathematical physics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; X-ray Diffraction in Crystallography
Authors: Tahir Mahmood | Abdul Wasy Zia
Source: Journal of Manufacturing Processes (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Predicting the hardness of diamond-like carbon coatings using machine learning and generative adversarial networks Producing diamond-like carbon (DLC) coatings with increased hardness remains an inspiration for achieving diamond-like properties. A multivariable parametric analysis may necessitate 50+ experiments to establish optimum plasma dynamics with a combination of electric, magnetic, kinetic, and thermal energies that build a DLC coating of a specific hardness. Overall, this places a strain on resources and the climate. This research aims to predict the hardness of DLC coatings as a function of bias voltage (0 to 140 V) and annealing temperature as a direct and two-stage heat treatment. In addition, this work investigates the critical features for estimating the hardness of DLC coatings. The conditional tabular generative adversarial networks (CTGANs) model is used to expand the small experimental data of DLC coatings, and a large dataset is obtained from the optimal CTGANs model. A range of 15 machine learning models are used to predict the hardness of DLC, and their efficacy is measured using the six well-known error-based performance measures. The data-driven modelling reflects that top-performing models, including SVR, XGBoost, LightGBM, CatBoost, ANNs, and FNNs, achieved exceptional predictive accuracy (∼99.9 %). Furthermore, the significance of each explanatory variable is indicated using the Shapley additive explanations (SHAP) model, which identified bias voltages of 40 V and 120 V and second-stage heat treatment as critical factors influencing hardness. Therefore, it is concluded that the implementation of the second stage potentially increasing the hardness and the bias levels of 40 and 120 V may decrease the hardness of the DLC coating. The approach demonstrates a novel and efficient data-driven strategy for optimizing DLC coating processes, offering significant potential to accelerate material design in applications where experimental trials are resource-intensive. Materials science ; Generative grammar ; Diamond ; Carbon fibers ; Generative adversarial network ; Composite material ; Metallurgy ; Artificial intelligence ; Computer science ; Deep learning ; Composite number Diamond and Carbon-based Materials Research ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Zemin Ren | Qingxu Deng | Yu Wang | Yajun Yang | Hongbin Wang | Fufeng Liu | Wenjie Jing
Source: Food Chemistry (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Machine learning assisted nanozyme sensor array for accurate identification and discrimination of flavonoids in healthy tea Identification (biology) ; Artificial intelligence ; Computer science ; Computational biology ; Biology ; Botany Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Qihao Shi | LI Zi-yuan | Yu Wang | Fufeng Liu | Wenjie Jing
Source: Analytical Chemistry (article, 2025-08-10)
Field: Materials Science — Materials Chemistry

Machine Learning-Enhanced Nanozyme Sensor Array for Accurate Multiple Quinolone Antibiotics Recognition The overuse of quinolone antibiotics (QNs) seriously endangers human health and the ecological environment. In this work, a copper dihydroxosulfate (Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>) nanosheet exhibiting notable peroxidase-like (POD) and laccase-like (LAC) activities has been developed in basic deep eutectic solvents (DES). The unique physicochemical properties of QNs allow them to enhance the POD activity of Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>, and with the extension of reaction time, this enhancement gradually intensifies. Conversely, when QNs are introduced into the LAC reaction system of Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>, they significantly inhibit its LAC activity, with the degree of inhibition growing increasingly evident as the reaction time increases. A nanozyme sensing array has been developed via reaction dynamics to identify eight QNs. This method cleverly achieves self-calibration through two reverse signals, further improving the sensing performance of the sensor array. Moreover, through the optimization of various machine learning (ML), the precision of the concentration-independent recognition model built upon this array has been enhanced from 39.08% to 91.95%. This improvement is advantageous for the identification of unknown samples within actual samples. This work carries significant implications for enhancing the discrimination of QNs in complex samples. Chemistry ; Biological system ; Point of delivery ; Eutectic system ; Quinolone ; Analyte ; Combinatorial chemistry ; Antibiotics ; Nanotechnology ; Biochemistry ; Chromatography ; Botany ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Zhi-Yuan Li | Xiao Huang | Jiang-Xu Yang | Sheng‐Hui Luo | Jiang Wang | Qunling Fang | Ai-Ling Hui | Feng‐Xia Liang | Chunyan Wu | Li Wang | Yan Wang | Xiang Zhang | Guo Dongfeng | Lin‐Bao Luo
Source: Journal of Food Composition and Analysis (article, 2025-05-05)
Field: Chemistry — Analytical Chemistry

An improved 1D CNN with multi-sensor spectral fusion for Detection of SSC in pears Fusion ; Sensor fusion ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Industrial Vision Systems and Defect Detection
Authors: Shafidah Shafian | Mohd Nizam Husen | Lin Xie | Kyungkon Kim
Source: Materials Today Sustainability (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Predicting high-performance perovskite solar cells using AI-based machine learning models Perovskite solar cells (PSCs) have garnered significant attention in the photovoltaic field due to their remarkable power conversion efficiencies (PCEs), with the certified PCE reaching 27.0% for single-junction cells and 30.1% for tandem perovskite/perovskite multijunction cells over the past decade. However, challenges remain including material instability, compositional inconsistency, and limited long-term performance. Machine learning (ML) has emerged as a transformative tool to address these challenges by accelerating material discovery, optimizing device design, and enabling data-driven insights from large and complex datasets. This review presents a comprehensive analysis of how ML is being applied to advance PSCs technologies. It begins with an overview of perovskite structures, device architectures, and performance parameters relevant to ML modelling. A structured ML workflow is introduced, covering data acquisition, feature selection, model development, performance evaluation, and model interpretability through explainable AI (XAI) techniques. Recent studies are examined across two major domains: material discovery and device performance optimization. Unlike previous reviews, this work emphasis on quantitative comparisons of ML algorithms by systematically assessing models reported in recent literature to identify the most effective predictors across various tasks. Furthermore, it discusses the strengths and limitations of current datasets and modelling strategies. The review concludes with insights into existing challenges and outlines future directions to support the efficient, interpretable, and scalable application of ML in PSCs research. Perovskite (structure) ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Machine Learning and ELM
Authors: Edgar Miguel García-Carrillo | Aimeé Alejandra Hernández‐López | Adriana B. Espinoza‐Martínez
Source: Food Packaging and Shelf Life (article, 2025-05-26)
Field: Materials Science — Biomaterials

Modeling and optimization of mechanical, water vapor permeability and haze properties of PLA and PBAT films reinforced with montmorillonite, halloysite nanotubes and palygorskite using artificial neural networks and genetic algorithms Palygorskite ; Montmorillonite ; Halloysite ; Materials science ; Artificial neural network ; Permeability (electromagnetism) ; Chemical engineering ; Composite material ; Nanotechnology ; Artificial intelligence ; Computer science ; Geology ; Clay minerals ; Mineralogy ; Chemistry ; Engineering ; Membrane biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Hydrogels: synthesis, properties, applications