jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hande Selvi Yanık
Source: Next frontier. (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Quantum-Enhanced Computational Drug Discovery: Leveraging Variational Quantum Algorithms for Accurate Binding Affinity Prediction and Lead Optimization in Complex Biological Targets Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics. Quantum computer ; Workflow ; Scalability ; Quantum ; Computational complexity theory ; Quantum entanglement ; Drug discovery ; Superposition principle ; Minification ; Identification (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Zhiming Liu
Source: Research Square (preprint, 2025-02-13)
Field: Materials Science — Materials Chemistry

Band-gap-tunable carbon quantum dots for surface-enhanced Raman scattering Raman scattering ; Quantum dot ; Carbon quantum dots ; Raman spectroscopy ; Materials science ; Band gap ; Surface (topology) ; X-ray Raman scattering ; Scattering ; Optoelectronics ; Carbon fibers ; Optics ; Physics ; Composite material Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: E. O. Khazieva | N. M. Chtchelkatchev | Nikolay Katkov | R. E. Ryltsev
Source: Physical review. E (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Accuracy and limitations of machine-learned interatomic potentials for magnetic systems: A case study on Fe-Cr-C Machine-learned interatomic potentials (MLIPs) are rapidly becoming the standard for atomistic simulations, but their application to magnetic materials remains challenging because spin fluctuations must be treated either explicitly or implicitly. Here, we investigate this problem for the technologically important Fe-Cr-C system by constructing two deep machine learning potentials within the DeePMD framework: one trained on nonmagnetic DFT data (DP-NM) and one on spin-polarized DFT data (DP-M). Extensive validation against experiments reveals that DP-NM accurately reproduces dynamic, collective properties such as viscosity and melting temperatures, while DP-M excels in describing static, local properties such as density, especially for Fe-rich alloys. We rationalize these findings by noting that, at high temperatures, local magnetic moments self-average over space and time, making explicit spin treatment unnecessary for transport properties but essential for equilibrium volumes. To mitigate the high cost of spin-polarized DFT sampling, we employ a transfer-learning strategy, pretraining on nonmagnetic data followed by fine-tuning on a small spin-polarized dataset, which reduces computational expense by more than an order of magnitude. Furthermore, we benchmark several state-of-the-art foundation models: MACE, GRACE, DPA3 and fine-tuned variants of the latter against our specialized potentials. We find that foundation models offer competitive accuracy after fine-tuning but remain significantly slower in molecular dynamics simulations, limiting their practicality for large-scale transport property calculations. Our results establish clear design principles for MLIPs targeting magnetic alloys: (i) nonmagnetic training data suffice for dynamic properties of paramagnetic melts, and (ii) spin-polarized training is essential only for precise static properties of ferromagnetic phases. These insights provide a roadmap for efficient development of transferable MLIPs for magnetic systems and clarify the role of foundation models and transfer learning in accelerating this process. Benchmark (surveying) ; Ferromagnetism ; Limiting ; Spin (aerodynamics) ; Molecular dynamics ; Magnetic moment ; Experimental data ; Space (punctuation) ; Interatomic potential Machine Learning in Materials Science ; Quantum many-body systems ; Electrocatalysts for Energy Conversion
Authors: Jie Chen | Yongming Liu
Source:  (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Probabilistic Physics-Guided Machine Learning With Missing Data: Applications in Additive Manufacturing Abstract Machine learning (ML) is increasingly utilized in materials and structural analysis. However, for an ML model to perform effectively, it requires a large amount of high-quality data for training. When experimental or simulation data are insufficient, training data can be sourced from literature. However, literature data often come from diverse sources (e.g., different testing equipment and materials) and lack standardization, leading to significant data variability and missing information. This work aims to develop a physics-guided machine learning model that accounts for these data variations and missing data. We focus on the probabilistic fatigue life prediction of additively manufactured (AM) Ti-6Al-4V, considering the influence of process parameters. Fatigue data for AM Ti-6Al-4V, both in as-built and annealed conditions, were collected from extensive literature, capturing various process parameters (e.g., scanning speed, laser power, hatch space, layer thickness, heat temperature, and heat time). A Probabilistic Physics-Guided Neural Network (PPgNN) is proposed to model these data, with the following novelties: (1) Handling Data Variability: Given the wide range of data sources, there are significant variations for the same variables. The PPgNN can be trained on noisy literature data and predict both the mean and variance of fatigue life. (2) Addressing Missing Data: Due to inconsistencies in how data are reported, some process parameters are available in some literature but missing in others. The model can be trained with incomplete datasets and adapt when additional information becomes available. (3) Incorporating Domain Knowledge: Existing fatigue analysis knowledge is integrated into the ML model to guide learning and prediction. The PPgNN includes constraints on model parameters to ensure the generated probabilistic stress-life (P-S-N) curves have physics-consistent curvature and non-constant variance. The results demonstrate that PPgNN effectively manages uncertainties arising from data variations and missing information, producing physics-consistent probabilistic predictions. A global sensitivity analysis using the Sobol sensitivity was also performed to assess the importance of the process parameters. Probabilistic logic ; Computer science ; Missing data ; Machine learning ; Artificial intelligence ; Data modeling ; Software engineering Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis
Authors: Ho Jang | Yang Yang | Gia-Wei Chern
Source: ArXiv.org (preprint, 2025-10-23)
Field: Materials Science — Materials Chemistry

Kinetics of Peierls dimerization transition: Machine learning force-field approach We present a machine learning (ML) force-field framework for simulating the non-equilibrium dynamics of charge-density-wave (CDW) order driven by the Peierls instability. Since the Peierls distortion arises from the coupling between lattice displacements and itinerant electrons, evaluating the adiabatic forces during time evolution is computationally intensive, particularly for large systems. To overcome this bottleneck, we develop a generalized Behler-Parrinello neural-network architecture -- originally formulated for ab initio molecular dynamics -- to accurately and efficiently predict forces from local structural environments. Using the locality of electronic responses, the resulting ML force field achieves linear scaling efficiency while maintaining quantitative accuracy. Large-scale dynamical simulations using this framework uncover a two-stage coarsening behavior of CDW domains: an early-time regime characterized by a power-law growth $L \sim t^α$ with an effective exponent $α\approx 0.7$, followed by a crossover to the Allen-Cahn scaling $L \sim \sqrt{t}$ at late times. The enhanced early-time coarsening is attributed to anisotropic domain-wall motion arising from electron-mediated directional interactions. This work demonstrates the promise of ML-based force fields for multiscale dynamical modeling of condensed-matter lattice models. Scaling ; Exponent ; Lattice (music) ; Crossover ; Anisotropy ; Force field (fiction) ; Adiabatic process ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Organic and Molecular Conductors Research
Authors: Clark Griscom | Dongyi Wang | Corliss A. O’Bryan | Rimmo Rõõm | Philip G. Crandall
Source: Foods (article, 2025-12-18)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Chicken Residues on Poultry Plant Surfaces Using Color and Fluorescence Spectrometry Color and fluorescence spectrometry were evaluated as rapid, objective tools for verifying the cleanliness of poultry-processing food-contacting surfaces contaminated with a model chicken solution across six common materials. Both techniques detected chicken residues at dilutions several orders of magnitude below human visual and olfactory thresholds, with stainless steel and blue plastic yielding the largest color differences between clean and contaminated states and fluorescence measurements remaining highly sensitive on all tested surfaces. Representative limits of detection were on the order of 1:50-1:100 dilution of chicken residue for color measurements on most surfaces and approximately 1:50 for fluorescence measurements, compared with human detection thresholds of approximately 1:50. Cleaning chemicals routinely used in poultry plants did not measurably reduce detection performance, and a simple machine learning classifier further improved separation of clean versus contaminated readings. These findings indicate that compact color and fluorescence instruments can provide fast, quantitative pre-sanitation checks that strengthen SSOP verification and reduce reliance on subjective human inspection in poultry processing facilities. Fluorescence ; Serial dilution ; Contamination ; Residue (chemistry) ; Detection limit ; Dilution Spectroscopy and Chemometric Analyses ; Indoor Air Quality and Microbial Exposure ; Mycotoxins in Agriculture and Food
Authors: T. Anuja | EM Malathy
Source:  (article, 2025-11-06)
Field: Chemistry — Analytical Chemistry

Analyzing the Quality Evaluation of Integrated Fruit Ripeness Using CNN Inception V3 Quality control of fruit ripeness is important to extend shelf life and ensure optimum moment of purchase. To automate and improve accuracy of this process machine learning methods have increasingly been applied in recent years. In this paper, the Inception V3 Convolutional Neural Networks (CNN) architecture is employed to assess the quality of the integrated ripeness of the fruit and shows its efficiency. The purpose of this work is to predict fruit ripeness using image processing in inception v3 model. The paper describes a portable fruit ripeness prediction system in fruits such as Apple, Banana, Guava, Lime and Orange based on Inception V3 Module. We describe an automated ripeness phase prediction system which uses the visual appearance of the fruit images and other fruits data in an integrated manner to enhance the prediction of the ripeness phases of the fruits. The images of the fruit are analysed and their ripeness is classified using the Inception V3 model, famous for its high accuracy and good performance in image classification tasks. As a side function internal material properties (like texture and water content) are determined from the data. The results demonstrate that the proposed approach outperforms state-of-the-art methods and presents more reliable and non-culture consuming method to evaluate fruit maturity. The feasibility of the real-time agricultural practices recording and decision making is there, which make enhancement in supply chain management practice better. Ripeness ; Quality (philosophy) ; Process (computing) ; Traceability ; Convolutional neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Jingjing Ma | Shuai Zhang | Xiangjun Liu | Junqi Wang
Source: Energy (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Neural network-based prediction of levoglucosan yield: A novel modeling approach Levoglucosan ; Artificial neural network ; Yield (engineering) ; Biological system ; Computer science ; Chemistry ; Artificial intelligence ; Thermodynamics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Food composition and properties
Authors: Chen Li | Jiewen Nie | Jiajun Li | Haining Yang
Source:  (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

c-GAN for Resolving Ill-Posed Spectral Reconstruction in Liquid Crystal Metasurface Spectrometers We proposed a conditional GAN for computational spectral reconstruction for liquid crystal metasurface spectrometers, which resolved 0.5nm FWHM peaks under a 20dB signal-to-noise ratio, 30dB below the minimum requirement of conventional deep learning methods. Spectrometer ; Full width at half maximum ; Crystal (programming language) ; Liquid crystal ; Iterative reconstruction ; Spectral line Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Photonic Crystals and Applications
Authors: Youssef Kassem | Hüseyin Çamur | Almonsef Alhadi Salem Mosbah
Source: Engineering Technology & Applied Science Research (article, 2025-08-02)
Field: Chemistry — Analytical Chemistry

Kinematic Viscosity of Petroleumat at 37.78 ℃: A Comprehensive Comparison of Machine Learning Techniques The viscosity of crude oil is a significant component influencing oil recovery and flow behavior, yet accurately predicting viscosity remains a significant challenge in reservoir optimization, requiring the use of predictive models. This study evaluates eight Artificial Intelligence (AI) models, Feed-forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Elman Neural Network (ENN), Multi-layer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN), k-Nearest Neighbor (kNN), Support Vector Regression (SVR), and Extreme Learning Machine (ELM), as well as three mathematical models, Poisson Regression Model (PRM), Quadratic Model (QM), and Multiple Linear Regression (MLR), for predicting the Kinematic Viscosity (KV) of crude oil at 37.78 ℃. A dataset of 274 crude oil samples was compiled from literature sources, including Molecular Weight (MW), Refractive Index (RI), Sulfur content (S),Specific Gravity (SG), and Initial Boiling Points (IBP)ranging from 70℃ to 565℃. To identify the most influential factors affecting KV, Principal Component Analysis (PCA), Pearson's Correlation Coefficient (PCC), and Decision Trees (DT)were applied, highlightingIBP-280 ℃, IBP-343 ℃, S, RI, MW, and SG as key variables. Subsequently, 63 MLPNN models were trained with various input combinations to evaluate their impact on prediction accuracy. Four models, M#1 [SG, MW], M#2 [SG, IBP-280 ℃, IBP-343 ℃], M#3 [SG, S, IBP-280 ℃, IBP-343 ℃], and M#4 [SG, IBP-280 ℃, IBP-343 ℃, RI, MW], demonstrated superior performance based on multiple statistical metrics. These four feature sets were then incorporated into the AI and mathematical models for further evaluation. Comparative analysis revealed that ELM, RBFNN, and PRM models exhibited the highest accuracy and stability among individual approaches. To further enhance prediction quality, a hybrid ensemble model (RBFNN-ELM-PRM) was developed, integrating the strengths of these three models. The ensemble, using the feature set [SG, IBP-280℃, IBP-343℃, RI, MW], outperformed all individual models, offering improved robustness and precision in KV prediction. Kinematics ; Viscosity ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Engineering drawing ; Biological system ; Mechanical engineering ; Engineering ; Composite material ; Physics ; Classical mechanics ; Biology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Matěj Mezera | Paolo Andrea Erdman | Zeno Schätzle | P. Bernát Szabó | Frank Noé
Source: The Journal of Chemical Physics (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Partitioning the electronic wave function using deep variational Monte Carlo We propose a novel wave function partitioning method that integrates deep-learning variational Monte Carlo with ansätze based on generalized product functions. This approach effectively separates electronic wave functions (WFs) into multiple partial WFs representing, for example, the core and valence domains or different electronic shells. Although our ansätze do not explicitly include correlations between individual electron groups, we show that they accurately reproduce the underlying physics and chemical properties, such as dissociation curve, dipole moment, reaction energy, ionization energy, or atomic sizes. We identify the optimal number of core electrons and define physical core sizes for Li to Mg atoms. Our results demonstrate that core electrons can be effectively decoupled from valence electrons. We show that the core part of the WF remains nearly constant across different molecules and their geometries, enabling the transfer and reuse of the core part in WFs of more complex systems. This work provides a general framework for WF decomposition, offering potential advantages in computing and studying larger systems, and possibly paving the way for ab initio development of effective core potentials. Although currently limited to small molecules due to scaling, we highlight several directions for extending our method it to larger systems. Wave function ; Core electron ; Statistical physics ; Monte Carlo method ; Electron ; Dipole ; Electronic structure ; Valence electron ; Ab initio ; Physics ; Quantum Monte Carlo ; Ionization energy ; Ionization ; Atomic physics ; Quantum mechanics ; Mathematics ; Ion Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Yan‐He Lv | Yang Hong | Zhi Ning Chen
Source:  (article, 2025-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prior Knowledge-Guided Deep Learning-Enabled Methods for Generative Design of Metacells Increasing the degrees of freedom in metacells-the fundamental “atoms” in metamaterial-based designs-can expand performance and functionality boundaries but also introduces challenges, such as high computing cost and complex non-linear mapping. We propose an efficient deep-learning framework, guided by prior knowledge (PK) for generative metacell designs. The framework integrates various types of PK to guide generative deep learning at the data, model, and decision levels, effectively constraining the design space, improving training efficiency, enhancing controllability, and refining the synthesis process. Three series of generative metacell designs tailored for lens antennas, spatial filters, and phased arrays are exemplified to illustrate the framework implementation. The proposed efficient framework enables beyond-logical topology designs and beyond-conventional performance, emphasizing its potential for multi-objective synthesis of quasi-free form metacells. Generative grammar ; Generative Design ; Series (stratigraphy) ; Generative model ; Degrees of freedom (physics and chemistry) Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Antenna Design and Optimization
Authors: Guijie Wang | Z.Z. Wang | Qiang Zhang | Xinyi Wang | Yi Zhao | Chengming Gong | Meng Miao | Xiaoxiao Zheng
Source: Journal of Materials Research and Technology (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Corrosion resistance characteristics and predictive modeling of electrochemically polished medical NiTi shape memory alloy milled surfaces The NiTi shape memory alloy is widely employed in medical applications owing to their exceptional corrosion resistance and biocompatibility, with the milling-electropolishing process being a standard surface treatment approach. This study employed a three-factor, three-level Box-Behnken experimental design based on response surface methodology (RSM) to systematically investigate how electrochemical polishing parameters (current density, electrode gap, and polishing time) influence corrosion characteristics including corrosion potential, current density, and polarization resistance in simulated body fluid (SBF). The BP neural network model, developed using full factorial experimental data, provides quantitative predictions of corrosion current density from process parameters. Results demonstrate that current density predominately governs the corrosion characteristics, while the electrode gap-polishing time interaction exhibits secondary influence. The optimized BP neural network (single hidden layer with four neurons, tansig-purelin transfer functions, trainlm training algorithm) achieves exceptional prediction accuracy ( R 2 = 0.9969) for corrosion current density, offering both theoretical guidance and technical support for optimizing surface treatment processes and enhancing the service reliability of NiTi alloy. Corrosion ; Shape-memory alloy ; Nickel titanium ; Polishing ; Polarization (electrochemistry) ; Current density ; Electrode ; Alloy ; Electrochemistry Shape Memory Alloy Transformations ; Advanced Machining and Optimization Techniques ; Titanium Alloys Microstructure and Properties
Authors: Tao Wen | Rongshuai Che | Haiyan Chen | Chenchen Jin | Haibo Wang | Ke-Jing Huang | Kaisheng Diao | Xuecai Tan
Source: Sensors and Actuators B Chemical (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

An intelligent nanozyme-enhanced triple-modal detection platform integrated with machine learning for self-validating pathogen monitoring Reliability (semiconductor) ; Consistency (knowledge bases) ; Limit (mathematics) ; SIGNAL (programming language) ; Fuse (electrical) ; Key (lock) ; Detection limit Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques