jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuo Liu | Xiaoqian Fan | Hongjian Ye | Makhambet Ibragim | Haifeng Song | Xingyu Gao | Gulmira Yar-Mukhamedova | Daniel M. Zellele | Peixuan Li | William Yi. Wang | Jinshan Li
Source: Journal of Materials Informatics (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions Under extreme service conditions, adiabatic shear banding critically limits the performance of titanium alloys in warhead applications, creating an urgent demand for strategies to achieve strength-ductility synergy. In this work, a knowledge-enabled data-driven multi-objective optimization framework is proposed to investigate the composition of near-α titanium alloys under high strain rates. By integrating domain knowledge with twelve machine learning models, key performance parameters (KPPs) governing strength are identified through feature engineering, including strain rate, Fermi energy, and phase formation parameters, while ductility is controlled by the KPPs of strain rate, bulk/shear modulus (B/G) ratio, and mixing enthalpy. Using a gradient boosting regression tree model for strength prediction [test the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) = 0.91] and a random forest model for ductility prediction (test R&lt;sup&gt;2&lt;/sup&gt; = 0.82), the nondominated sorting genetic algorithm II (NSGA-II) is integrated to identify 14 Pareto-optimal alloys from a pool of 200,000 candidate compositions of near-α titanium alloys (Ti-Al-V-Mo-Zr-Sn system). A breakthrough combination of 1,600 MPa dynamic compressive strength and 26% ductility at a strain rate of 3,000 s&lt;sup&gt;-1&lt;/sup&gt; is achieved, which is superior to that of the TA15 alloy, as confirmed by a constitutive equation model. This framework successfully designs novel near α-titanium alloys with strength-ductility synergy through knowledge-driven feature engineering and multi-objective optimization algorithms, establishing a new paradigm for the intelligent design of titanium alloys under extreme conditions. Titanium alloy ; Ductility (Earth science) ; Adiabatic shear band ; Strain rate ; Titanium ; Genetic algorithm ; Random forest Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Rui Ye Han | Emily Xi Tan | Yangcenzi Xie | Hong Sheng Cheng | Nguan Soon Tan | Yun Lv | In Yee Phang | Xing Yi Ling
Source: ACS Applied Materials & Interfaces (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Building a Foundation SERS Model for Lipids through Fatty Acid Pretraining for Annotation across Chemical Spaces Machine learning analysis of vibrational spectra is often closed-set, performing well for known molecular classes but degrading sharply when test molecules fall outside the training library. Herein, we introduce a SERS-based domain-informed foundation model for fatty acid-derived lipids that replaces categorical assignments with vector matching. The model is trained exclusively on primitive single-chain free fatty acids, with each structural attribute encoded by five orthogonal molecular vectors in a multidimensional space: (1) carbon number, (2) number of C═C bonds, (3) C═C position, (4) C═C geometry, and (5) number of carbon chains. This modular ensemble enables zero-shot prediction of all five vectors in parallel from previously unseen spectra, allowing untargeted molecular reconstruction by proximity in this space rather than by class labels. Despite being trained only on free fatty acids, the model generalizes to complex, multichain lipids absent from the training set, achieving accuracies of 91.7% for withheld free fatty acids, 85.5% for fatty acid esters of hydroxy fatty acids, and 80.0% for triglycerides. To enhance interpretability, we utilize density functional theory to provide a mechanistic basis for the spectral features underlying each prediction. We further demonstrate matrix-tolerant multiplex quantitation in artificial sweat and urine, recovering mixture ratios with 2-9% error across broad composition ranges. Collectively, this strategy enables extrapolative, interpretable spectral-to-structure prediction from SERS across adjacent chemical spaces. Chemical space ; Fatty acid ; Categorical variable ; Pattern recognition (psychology) ; Annotation ; Modular design ; Molecule ; Support vector machine Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Jianxin Xie | Wonhee Ko | Rui-Xing Zhang | Bing Yao
Source: Journal of Applied Physics (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Physics-guided deep learning with adversarial domain adaptation: Applications to STM image denoising Image denoising is essential in materials characterization, particularly for recovering fine structural details in scanning tunneling microscopy (STM) images. While supervised denoising approaches have shown strong performance, they typically rely on large datasets of paired noisy and clean images, which are often unavailable in experimental settings. Unsupervised methods, though not requiring paired data, often rely on a collection of unpaired clean images for training—resources that are frequently unavailable in real-world STM laboratory environments. In this work, we propose PDA-Net, a physics-guided deep learning framework with adversarial domain adaptation for unsupervised STM image denoising. PDA-Net leverages a physics-based simulator to generate synthetic STM images for the surface of copper single crystals, i.e., Cu(111), serving as a proxy for the clean ground truth. Built upon a generative adversarial network architecture, the framework integrates cycle-consistency and domain adversarial modules to bridge the gap between simulated and real experimental domains in the absence of paired data. Additionally, feature alignment and weight-sharing strategies are employed to enhance knowledge transfer between domains. Experimental results demonstrate that PDA-Net significantly improves STM image quality, enabling more accurate interpretation of quantum material properties and facilitating accelerated scientific discovery. Deep learning ; Domain (mathematical analysis) ; Pattern recognition (psychology) ; Image (mathematics) ; Adversarial system ; Noise reduction ; Feature (linguistics) ; Image translation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Udhayanithi N
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Spectrophotometric Quantification Of Vericiguat By 3-Methyl-2-Benzothiazolinone Hydrazone (Mbth) Reagent In Bulk And Tablet Dosage Form With Ai Integrated Artificial Neural Network Assisted Prediction Objective: A simple, novel, quick and sensitive method for the quantification of Vericiguat with the help of oxidative coupling reaction by MBTH reagent to estimate the amount of drug present in the bulk and pharmaceutical dosage form. Materials and Methods: The visible reaction between the drug vericiguat and the coloring reagent MBTH was prepared and which results in colored chromogen and exhibited a maximum absorbance at 637 nm. Results: By involving various optimization studies, methanol is selected as a solvent and the suitable concentration of MBTH and ferric chloride reagents were identified and used in the developmental study. This colorimetric method development by MBTH reagent was validated as per the guidelines of International Council of Harmonization and the results obtained were found to be obeying the Beer’s Lambert’s law at the concentration range of 100-500 μg/ml with a good correlation of about 0.9998. The assay of marketed vericiguat tablet was performed and found to be 100.19 and the limit of detection and quantification were identified within the acceptance criteria (%RSD < 2.00). To strengthen the project, with the help of a normalized absorbance value, an AI integrated Artificial Neural Network (ANN) model was developed to predict the concentration. MS Excel Solver used to train the feed-forward ANN (1–1–1 architecture) which demonstrate an excellent predictive performance with minimal mean squared error. Conclusion: Above quantified analytical development method was proved to be a novel and sensitive and more convenient for employing analytical validation process of vericiguat in pharmaceutical dosage form. Reagent ; Dosage form ; Absorbance ; Hydrazone ; Solvent ; Ferric ; Methanol Analytical Methods in Pharmaceuticals ; Pharmaceutical Quality and Counterfeiting ; Pharmacological Effects and Assays
Authors: Jingwen Zhou | Yawen Yu | Xuwei Liu | Chungen Liu
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Explicit Electric Potential-Embedded Machine Learning Framework: A Unified Description from Atomic to Electronic Scales To further develop accurate and large-scale simulations of electrochemical interfaces, we propose a unified explicit electric potential framework to simultaneously predict atomic forces and electron density distributions. The framework consists of three components: data generation, model training, and application. The data generation component, implemented in Hy-DFT, efficiently regulates the potential during constant-potential ab initio molecular dynamics (CP-AIMD), reducing the number of single-point calculations required for convergence. The model training component includes two modules: Potential-Embedded MACE (PE-MACE) and Potential-Embedded Electron Density Prediction (PE-EDP). PE-MACE implements an explicit electric potential machine learning force field (EEP-MLFF) based on the MACE architecture. We develop PE-EDP to overcome the limitation of EEP-MLFF in describing atom forces. PE-EDP, also based on equivariant graph neural networks, predicts electron density distributions under arbitrary potentials. Using the Pt(111)/water interface as a model system, both PE-MACE and PE-EDP show high accuracy on training and test sets. Radial distribution functions from CP-MLMD agree well with CP-AIMD, and long-timescale simulations reveal potential-induced reorganization of interfacial water. Planar-integrated charge profiles and Bader analysis from PE-EDP are consistent with DFT results. These results demonstrate that the framework can simultaneously describe atomic dynamics and electron density distributions under arbitrary potentials, providing a useful tool for studying electrochemical interfaces. Electric field ; Charge density ; Electron density ; Atom (system on chip) ; Electron ; Molecular dynamics ; Density functional theory ; Electric potential Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Timothy Zaklama | Max Geier | Liang Fu
Source: arXiv (Cornell University) (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Large Electron Model: A Universal Ground State Predictor We introduce Large Electron Model, a single neural network model that produces variational wavefunctions of interacting electrons over the entire Hamiltonian parameter manifold. Our model employs the Fermi Sets architecture, a universal representation of many-body fermionic wavefunctions, which is further conditioned on Hamiltonian parameter and particle number. On interacting electrons in a two-dimensional harmonic potential, a single trained model accurately predicts the ground state wavefunction while generalizing across unseen coupling strengths and particle-number sectors, producing both accurate real-space charge densities and ground state energies, even up to $50$ particles. Our results establish a foundation model method for material discovery that is grounded in the variational principle, while accurately treating strong electron correlation beyond the capacity of density functional theory. Wave function ; Hamiltonian (control theory) ; Ground state ; Electron ; Representation (politics) ; Electronic correlation ; Electron density ; Harmonic oscillator Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Liangchao Guo | Peng Wang | Chunyu Du | Sheng Hu | Feng Zhao | Junfeng Cui | Qilong Yuan | Chao Zhang
Source: Materials Horizons (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Memristive behavior of a V <sub>2</sub> C MXene/PANI:PSS composite and its applications in ammonia detection Memristive devices that integrate gas sensing with neuromorphic functionality have been identified as a promising route to compact, low-power, and multifunctional artificial olfaction. Herein, we presented a chemically gated memristive device based on a V<sub>2</sub>CT<sub><i>x</i></sub> MXene/PANI:PSS nanocomposite. A dense, strongly bonded heterostructure was constructed <i>via</i> HF etching followed by <i>in situ</i> oxidative polymerisation, enabling molecular interactions to directly modulate device conductance and thus realise gas-electrical co-operative synaptic operation. As a gas sensor, the device shows a 31.8% response to 5 ppm NH<sub>3</sub>, with response/recovery times of 26 s/52 s and >95% signal retention over 15 days at room temperature. As a memristor, it exhibits stable bipolar resistive switching (ON/OFF > 10<sup>2</sup>) with a switching time of ∼10 ms; under preset gaseous atmospheres and pre-synaptic electrical pulses, EPSC/IPSC and PPD-type temporal plasticity are elicited. Mechanistically, protonation/deprotonation of PANI sets the chemical working point, while interfacial trap charging and barrier modulation at the V<sub>2</sub>CT<sub><i>x</i></sub>/PANI interface enable reversible conductance updates, mapping molecular events into time-amplitude fingerprints. At the array level, a 2 × 2 crossbar with a multi-task neural network enables parallel identification of gas type and concentration, achieving 96.2% and 94.1% accuracy, respectively. This strategy enables an end-to-end chain-from molecular transduction through synaptic modulation to algorithmic inference-within a single device, providing a scalable path to compact, low-power artificial olfaction hardware. Neuromorphic engineering ; Memristor ; Conductance ; Resistive touchscreen ; Heterojunction ; Etching (microfabrication) ; Modulation (music) ; SIGNAL (programming language) ; Scalability MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Xuan Li | Pandi Teng | Yunna Ou | Zhao Niu | Shu Zhan | Jiajia Xu
Source: Computational Materials Science (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Selective differential attention enhanced cartesian atomic moment machine learning interatomic potentials with cross-system transferability Cartesian coordinate system ; Scalar (mathematics) ; Artificial neural network ; Moment (physics) ; Tensor (intrinsic definition) ; Scalar potential ; Support vector machine ; Scaling Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Måns K. Rosenbaum | D der Spoel
Source: Journal of Chemical Information and Modeling (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

plotXVG: Batch Generation of Publication-Quality Graphs from GROMACS Output Molecular simulation tools, such as GROMACS, are used routinely to produce time series of energies and other observables. To turn these data into publication-quality figures, a user can either use a (commercial) software package with a graphical user interface, often offering fine control and high-quality output, or write their own code to make plots using a scripting language. In the age of big data and machine learning, it is often necessary to generate many graphs, be able to rapidly inspect them, and make plots for manuscripts. Here, we provide a simple Python tool, plotXVG, built on the well-known Matplotlib plotting library, that will generate publication-quality graphics for line graphs as well as heatmaps and contour plots. This will allow users to rapidly and reproducibly generate a series of graphics files without programming, but a simple application programming interface is available as well for incorporation in, e.g., machine learning applications. Obviously, the tool is applicable to any kind of line graph data or heatmap, not just that from molecular simulations. plotXVG is available as free and open source, which implies that users can extend the tool to their own needs. Python (programming language) ; Scripting language ; Graphics ; Graphical user interface ; Software ; Simple (philosophy) ; Computer graphics ; Graph ; User interface Machine Learning in Materials Science ; Scientific Computing and Data Management ; Protein Structure and Dynamics
Authors: Hongbiao Dong
Source: Metallurgical and Materials Transactions A (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Evolution of Dendritic Patterns During Directional Solidification of Ni-Base Alloys: Towards Hexagonally Ordered, Close-Packed Dendrite Arrays Abstract Dendrites are the predominant microstructural feature formed during the solidification of metallic alloys. In directionally solidified alloys, the length scale and regularity of dendritic patterns strongly influence final properties. We introduce a supervised machine learning approach, shape-limited primary spacing (SLPS), that automatically and rapidly quantifies local primary dendrite arm spacing (PDAS) and packing order from microstructural images. SLPS provides a general, image-driven framework for quantifying directionally solidified dendrite structures. By applying SLPS to directionally solidified microstructures and to complementary simulations using a solutal dendrite model, we investigate how hexagonally ordered dendrite arrays correspond to near steady-state tip growth under homogeneous liquid composition. The admissible range of local PDAS is determined by local growth-rate variations and the degree of lateral adjustment to thermal–solutal gradients. Simulations comparing packing geometries (square vs. hexagonal in two-dimensional cross sections) reveal that hexagonal arrays adapt local PDAS more readily than square arrays, owing to enhanced secondary-arm development and tertiary-to-primary branching. Consequently, hexagonally packed dendrite arrays yield finer PDAS than square-packed ones. These findings provide a mechanistic basis for process design: seeding or processing routes that promote hexagonally ordered, close-packed dendrite arrays can achieve finer PDAS, thereby reducing microsegregation, lowering the propensity for defect-grain formation, and shortening solution heat treatment times. Graphical Abstract Dendrite (mathematics) ; Directional solidification ; Microstructure ; Hexagonal crystal system ; Homogeneous ; Yield (engineering) ; Seeding ; Process (computing) Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Elisa Roldan | Kirstie Andrews | Stephen M. Richardson | Reyhaneh Fatahian | Glen Cooper | Rasool Erfani | Tasneem Sabir | Neil D. Reeves
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Biomaterials

FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions Electrospinning is a scalable technique for producing fibrous scaffolds with tunable micro- and nanoscale architectures for applications in tissue engineering, drug delivery, and wound care. While machine learning (ML) has been used to support electrospinning process optimisation, most existing approaches predict only mean fibre diameters, neglecting the full diameter distribution that governs scaffold performance. This work presents FibreCastML, an open, distribution-aware ML framework that predicts complete fibre diameter spectra from routinely reported electrospinning parameters and provides interpretable insights into process structure relationships. A meta-dataset comprising 68538 individual fibre diameter measurements extracted from 1778 studies across 16 biomedical polymers was curated. Six standard processing parameters, namely solution concentration, applied voltage, flow rate, tip to collector distance, needle diameter, and collector rotation speed, were used to train seven ML models using nested cross validation with leave one study out external folds. Model interpretability was achieved using variable importance analysis, SHapley Additive exPlanations, correlation matrices, and three dimensional parameter maps. Non linear models consistently outperformed linear baselines, achieving coefficients of determination above 0.91 for several widely used polymers. Solution concentration emerged as the dominant global driver of fibre diameter distributions. Experimental validation across different electrospinning systems demonstrated close agreement between predicted and measured distributions. FibreCastML enables more reproducible and data driven optimisation of electrospun scaffold architectures. Electrospinning ; Interpretability ; Scalability ; Process (computing) ; Fiber ; Nanoscopic scale ; Scaffold ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science
Authors: Roberta Palmieri | Giuseppe Capobianco | Giuseppe Bonifazi | Silvia Serranti
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-04-01)
Field: Chemistry — Analytical Chemistry

LED vs. halogen illumination for hyperspectral imaging in the NIR range of space-derived waste materials Circular economic models play an important role in the context of waste management, which is essential for reducing environmental impact and preserving resources. For recycling procedures to be optimized, materials must be accurately identified and classified. In space, waste accumulation poses risks to missions and increases debris in orbit. Therefore, effective waste management systems are essential for mitigating hazards and reducing the environmental footprint of space activities. Hyperspectral Imaging (HSI) in the Near Infrared (NIR) range offers a non-invasive solution for real-time material analysis in space, where traditional sample preparation is challenging. This study examines the use of two distinct illumination sources, LED and Halogen light, for hyperspectral data collection to classify space-derived waste materials, including foams, technical textiles, and plastics, while also comparing their performance. Machine learning algorithms were employed to develop a classification model capable of automatically discriminating among the investigated material categories based on their spectral signatures. Furthermore, a calibration transfer strategy was explored to enable the transferability of models developed under one illumination condition to datasets acquired with a different light source, improving the robustness and flexibility of the proposed approach. LED lighting is particularly beneficial in scenarios demanding energy efficiency and stability. At the same time, halogen illumination is more suitable in contexts where a broader spectral range is crucial for accurate material differentiation. The results demonstrate that the integration of NIR hyperspectral imaging with machine learning and calibration transfer techniques enables reliable material identification under varying illumination conditions. Overall, the findings highlight the potential of this approach to support automated classification systems and advance space waste recycling strategies. Hyperspectral imaging ; Context (archaeology) ; Robustness (evolution) ; Calibration ; Near-infrared spectroscopy ; Halogen lamp ; Range (aeronautics) ; Spectral imaging ; Spectral signature Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Optical Polarization and Ellipsometry