jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Zhang | Robert I. Horne | Z. Faidon Brotzakis | Charles B. Harris | Píetro Lió | Michele Vendruscolo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-17)
Field: Chemistry — Organic Chemistry

Structure-based Generation of a Secondary Nucleation Inhibitor in α-Synuclein Aggregation Using a Conditional Diffusion Model Abstract The process of α -synuclein aggregation results in the formation of amyloid fibrils, which accumulate in the brain of patients affected by Parkinson’s disease. Among possible therapeutic strategies to cure this disease, one approach is based on the development of compounds capable of inhibiting α -synuclein aggregation. An effective inhibition could be achieved by blocking the nucleation sites on the surface of the amyloid fibrils that are responsible for their autocatalytic proliferation. Here, we report a strategy based on deep learning to achieve this goal, which uses an E(3)-equivariant conditional diffusion model. By using this approach, we designed and tested experimentally candidate small molecules. We found that one of these small molecules acts as a potent inhibitor of secondary nucleation in α -synuclein aggregation. These results provide evidence that generative diffusion models offer effective tools for drug design. Nucleation ; Diffusion ; Alpha-synuclein ; Alpha (finance) ; Statistical physics ; Physics ; Thermodynamics ; Mathematics ; Medicine ; Statistics Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: Elliot de Florinier
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Distribution-Referenced Feature Significance (DRFS): A Permutation-Based Method for Robust Feature Selection Feature selection is a persistent challenge in applied machine learning, particularly in engineering and scientific domains where datasets are high-dimensional, noisy, and often limited in size. Ensemble models such as Random Forests provide intrinsic measures of feature importance, yet these measures are typically treated as deterministic point estimates, obscuring their stochastic variability and leading to unstable or misleading feature rankings. This paper introduces Distribution-Referenced Feature Significance (DRFS), a permutation-based embedded feature selection framework that evaluates the statistical relevance of features by comparing their true importances against null distributions derived from fully shuffled data. By reframing feature importance as a statistical significance problem, DRFS enables robust, interpretable, and noise-aware feature selection. The method is model-agnostic, computationally tractable, and well suited to engineering and materials science applications where interpretability and reliability are critical. Interpretability ; Feature selection ; Feature (linguistics) ; Minimum redundancy feature selection ; Feature model ; Relevance (law) ; Pattern recognition (psychology) ; Feature engineering ; Reliability (semiconductor) Machine Learning in Materials Science ; Anomaly Detection Techniques and Applications ; Face and Expression Recognition
Authors: Luis F. V. Thomazini | Alexandre Luiz Affonso Fonseca
Source: ArXiv.org (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

A Simple and Efficient Non-DFT-Based Machine Learning Interatomic Potential to Simulate Titanium MXenes Titanium MXenes are two-dimensional inorganic structures composed of titanium and carbon or nitrogen elements, with distinctive electronic, thermal and mechanical properties. Despite the extensive experimental investigation, there is a paucity of computational studies at the level of classical molecular dynamics (MD). As demonstrated in a preceding study, known MD potentials are not capable of fully reproducing the structure and elastic properties of every titanium MXene. In this study, we present a simply trained, but yet efficient, non-density functional theory-based machine learning interatomic potential (MLIP) capable of simulating the structure and elastic properties of titanium MXenes and bulk titanium carbide and nitride with precision comparable to DFT calculations. The training process for the MLIP is delineated herein, in conjunction with a series of dynamical tests. Limitations of the MLIP and steps towards improving its efficacy to simulate titanium MXenes are discussed. MXenes ; Titanium ; Titanium carbide ; Interatomic potential ; Molecular dynamics ; Carbide ; Titanium alloy Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials
Authors: Lutz, Tim
Source: Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Möglichkeiten der Erkennung physischer Materialien an der Schnittstelle zu digitalen Applikationen Dieser Beitrag möchte Materialerkennungssysteme vorstellen, die in verschiedenen Forschungsprojekten zum Einsatz kommen. Unter anderem werden Elemente der App ZahLegAR vorgestellt, die auf Machine Learning basierende Materialerkennungssysteme nutzen. Work (physics) ; Line (geometry) ; Field (mathematics) ; Context (archaeology) Machine Learning in Materials Science ; Libraries and Information Services ; Research Data Management Practices
Authors: Stuti Shrivas | Amarish Dubey
Source:  (article, 2025-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insight into the Prediction of Specific Capacitance of Activated Carbon-Based Electrode for Supercapacitor Using Machine Learning The optimal capacity of energy storage devices such as supercapacitors can be achieved through careful selection of electrode materials. In this article, the specific capacitance of activated carbon (AC)-based electrode supercapacitor was forecasted to be using machine learning (ML) models such as linear regression (LR), decision tree regression (DT), and gradient boost regression (GB). The result of the study proved that the GB model performed best among other models. The results were observed and verified using different performance metrics such as root mean square error (RMSE) and correlation coefficient (R2). The GB model depicted a low RMSE value of 66.374 and high R2 value of 0.71. To further investigate the effect of input features on the model was performed using feature importance score. This analysis helps us to provide important insights into the impact and contribution of each input feature to specific capacitance of AC based supercapacitor. Capacitance ; Supercapacitor ; Feature selection ; Decision tree ; Feature (linguistics) ; Mean squared error ; Linear regression ; Correlation coefficient ; Energy (signal processing) ; Regression Supercapacitor Materials and Fabrication ; Neural Networks and Applications ; Electric and Hybrid Vehicle Technologies
Authors: Kaniselvan, Manasa | Maeder, Alexander | Xia, Chen Hao | Ziogas, Alexandros Nikolaos | Luisier, Mathieu
Source: Repository for Publications and Research Data (ETH Zurich) (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales, enabling investigation of the electronic properties of materials with extended defects, interfaces, or exhibiting disordered phases. However, as interactions between atomic orbitals typically extend over 10+ angstroms, the graph representations required for this task tend to be densely connected, and the memory requirements to perform training and inference on these large structures can exceed the limits of modern GPUs. Here we present a distributed eGNN implementation which leverages direct GPU communication and introduce a partitioning strategy of the input graph to reduce the number of embedding exchanges between GPUs. Our implementation shows strong scaling up to 128 GPUs, and weak scaling up to 512 GPUs with 87% parallel efficiency for structures with 3,000 to 190,000 atoms on the Alps supercomputer. Equivariant map ; Embedding ; Scaling ; Graph ; Artificial neural network ; Inference ; Graph embedding ; Graph theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: A. Haboub | Abdelkrim Khelif | Brahim Aïssa
Source: EPJ Web of Conferences (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Inverse Design for Femtosecond-Laser Photonic Surfaces with Direct Gradient Optimization The inverse design of photonic surfaces produced by high-throughput femtosecond laser processing is limited by a strongly non-linear, many-to-one mapping from laser parameters (power, speed, hatch spacing) to the resulting optical spectrum. Tandem Neural Networks (TNNs) address this challenge by training separate forward and inverse models, but require artificial noise to find diverse solutions, and still provide limited exploration of the design space. We propose Direct Gradient Optimization (DGO), a single-network alternative that treats the pre-trained forward surrogate as a differentiable proxy for the laser-material interaction and back-propagates errors to the process parameters. Two optimization modes are assessed: single-start DGO and Tournament-DGO, which launches multiple random seeds, runs a brief qualification phase, and refines only the five most promising candidates. Across 10,000+ inverse design tasks, Tournament-DGO cuts the average spectral root mean squared error (RMSE) from 1.29% (best TNN) to 0.70%, and boosts design novelty (NEPD) from 0.26 to 0.38. Inverse ; Inverse problem ; Photonics ; Femtosecond ; Laser ; Gradient descent ; Mean squared error ; Artificial neural network ; Differentiable function Optical Coatings and Gratings ; Laser and Thermal Forming Techniques ; Laser Material Processing Techniques
Authors: Alexander Adel | Ralf Wanzenböck | Georg K. H. Madsen
Source: ChemRxiv (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Completing the Hierarchy of Rotational Defects inMonolayer MoS2 through Symmetry-Aware EvolutionarySearch Monolayer molybdenum disulfide (MoS2) shows a plethora of defect configurations, which constitutes the basis for tailoring material properties through defect engineering. Detailed characterization of these defects remains challenging due to the complexity of the potential energy surface. We efficiently explore the three-fold rotational defect potential energy surface in monolayer MoS2 by combining an evolutionary algorithm with a machine-learning force field. To improve the performance of the structure searches, the algorithm hierarchically restricts the exploration process to a lower-dimensional subspace, utilizing the symmetry operators associated with the investigated defects. We demonstrate that these constrained trajectories exhibit lower global uncertainty measures during the evolution, produce final structures with lower energy distributions and converge faster. Our approach results in the discovery of several novel structures with reasonable computational effort, thereby completing the hierarchy of rotational defects in MoS2. Hierarchy ; Monolayer ; Molybdenum disulfide ; Potential energy surface ; Computer science ; Symmetry (geometry) ; Subspace topology ; Materials science ; Energy (signal processing) ; Process (computing) ; Field (mathematics) ; Surface (topology) ; Chemical physics ; Statistical physics ; Nanotechnology ; Molecule ; Physics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Gabrielle Brooking | Luis Borunda
Source: VTechWorks (Virginia Tech) (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Proceedings of the IASS Annual Symposium 2025 This paper explores how Conditional Generative Adversarial Networks (cGANs) can be used as a tool for structural adaptation in hybrid systems such as gridshells and plate structures. The cGAN is trained to identify stress trajectories to enhance structural performance. Height-to-stress mappings act as the basis for testing how cGANs can assist in geometric adjustments decision making by providing quickly produced generalized patterns of a design system’s structural identity. By embedding structural analysis and quantitative constraints into a machine learning dataset, the method provides iterative feedback on stress distribution rather than treating computational analysis as a terminal output. There are two deployment strategies: 1. Applying various magnitudes on a plate structure to evaluate the model’s ability to distinguish between different levels of force based on limited representational differences in the cGANs training, 2. Redistributing material in response to generatively mapped stress on a shell structure. Generative grammar ; Embedding ; Adversarial system ; Stress (linguistics) ; Adaptation (eye) ; Software deployment ; Shell (structure) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: Yun-Zhong Fang | Chunyong Wu | Chentao Zhang | Yizhu Xu | Zhibin Yin | Zhouyi Xu | Wei Hang
Source: Analytical Chemistry (article, 2025-11-27)
Field: Chemistry — Spectroscopy

RSR-MSI: Reference-Based Super-Resolution for Mass Spectrometry Imaging of Tissues and Single Cells High-spatial-resolution mass spectrometry imaging (MSI) visualizes molecular distributions in tissues and cells. However, achieving higher spatial resolution typically necessitates smaller pixel dimensions and an increased number of pixels, leading to longer data acquisition times and diminished analytical throughput. Although deep learning approaches have demonstrated significant potential in MSI, they typically require large training data sets or paired images, which are often unavailable. Herein, we propose the reference-based super-resolution for mass spectrometry imaging (RSR-MSI) method, with optical microscopy images as reference frames to extract abundant texture information. By integrating this with ion intensity data from the original MS images, we develop an image-specific super-resolution network. Employing solely a single low-resolution MS image coupled with a reference optical image, we successfully reconstruct high-resolution MS images for biological tissues and single cells, producing results with rich chemical and textural details. This approach significantly decreases the routine pixel-by-pixel scanning time by an order of magnitude while achieving high spatial resolution using existing mass spectrometry instruments without any customized modifications. Overall, our work introduces and validates the application of image super-resolution methods within the realm of single-cell MSI at subcellular resolution, paving the way for the development of high-spatial-resolution and high-throughput MSI for cellular biology research. Mass spectrometry imaging ; Mass spectrometry ; Image resolution ; Pixel ; Resolution (logic) ; Chemical imaging ; Data acquisition ; Microscopy Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Fluorescence Microscopy Techniques
Authors: Wenyuan Tao (21236930) | Wenkai Zhao (9759378) | Qidong Zhao (1723477) | Yonghou Xiao (8157663)
Source: Figshare (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

Ensemble-Learning-GuidedOptimization Design for Metal–OrganicFramework Adsorbents toward CO Adsorption Metal–organic frameworks (MOFs) hold great potential for carbon monoxide (CO) adsorption owing to their large pore volume, diverse periodic network structures, and designability. Machine learning is anticipated to provide optimization parameters for designing high-efficiency MOFs adsorbents, avoiding time-consuming experiments. Here, we proposed an ensemble-learning strategy accounting for multidimensional analysis of features to rationally design pore geometries, structural properties, and synthesis conditions of MOFs toward high performance for CO adsorption. The extreme gradient boosting model exhibited the best predictive performance (<i>R</i><sup>2</sup> > 0.95) under limited data set size. Porous characteristic was identified as a dominant factor in pristine MOFs. Prediction results illustrated that MOFs featuring one-dimensional, two-dimensional, microporous, and isolated pores were optimal for CO adsorption, with 0.4–0.6 cm<sup>3</sup>/g total pore volume. This enhanced adsorption capacity can be attributed to the shortened molecular diffusion pathways. The relative significance of structural parameters followed: space groups > geometry > topology. The optimal structural configuration involved space group of <i>R</i>3<i>m</i>, binuclear paddle wheel geometry, and scorpionate-like topology. Regarding transition metal-modified MOFs, incorporated Cu­(I) demonstrated the strongest binding affinity toward CO, while Fe­(II) and Ni­(II) could serve as effective binding sites. This work offers a theoretical guidance for designing efficient adsorbents toward CO adsorption. Adsorption ; Porosity ; Carbon monoxide ; Work (physics) ; Metal-organic framework ; Data space ; Diffusion ; Space (punctuation) Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Evan Dramko | Yizhi Zhu | Aleksandar Krivokapic | Geoffroy Hautier | Thomas Reps | Christopher Jermaine | Anastasios Kyrillidis
Source: ArXiv.org (preprint, 2025-11-30)
Field: Materials Science — Materials Chemistry

On The Finetuning of MLIPs Through the Lens of Iterated Maps With BPTT Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic potentials (MLIPs), which strive to faithfully reproduce first-principles computed forces. We propose a fine-tuning method to be used on a pretrained MLIP in which we create a fully-differentiable end-to-end simulation loop that optimizes the predicted final structures directly. Trajectories are unrolled and gradients are tracked through the entire relaxation. We show that this method consistently improves performance across all evaluated pretrained models; resulting in an average of roughly 32% reduction in prediction error. Interestingly, we show the process is robust to substantial variation in the relaxation setup, achieving negligibly different results across varied hyperparameter and procedural modifications. Hyperparameter ; Iterated function ; Process (computing) ; Relaxation (psychology) ; Feature (linguistics) ; Reduction (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Nishigandha B. Chougale | Mayur A. Gaikwad | Tushar T. Bhosale | Ankush K. Wadar | Rinaj K. Shaikh | Aniketa Shinde | Satyajeet S. Patil | Tukaram D. Dongale | Jin Hyeok Kim | A.V. Moholkar
Source: Journal of Physics and Chemistry of Solids (article, 2025-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Time-Resolved growth of self-supported Dual-functional nickel Iron-layered double hydroxide nanostructures for enhanced oxygen evolution and supercapacitor performance Overpotential ; Supercapacitor ; Oxygen evolution ; Capacitance ; Electrochemistry ; Current density ; Nickel ; Electrocatalyst ; Energy storage Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Layered Double Hydroxides Synthesis and Applications
Authors: Sara Khan | Devendra Dharival | Rakesh Kumar Saket | Gaurav Kant Saraogi
Source: International Journal of Newgen Research in Pharmacy & Healthcare (article, 2025-12-31)
Field: Materials Science — Biomaterials

Emerging Trends in NanoTheranostics: Integrating Imaging and Therapy for Severe Diseases NanoTheranostics represents an advanced paradigm in precision medicine by integrating diagnostic imaging and therapeutic functions within a single nanoplatform. This dual-functional approach enables real-time disease detection, targeted therapy, and continuous monitoring of therapeutic response, thereby overcoming the limitations of conventional diagnostic and treatment strategies. Recent progress in nanotechnology has led to the development of multifunctional nanoparticles, including liposomes, polymeric nanoparticles, dendrimers, metallic nanoparticles, quantum dots, and magnetic nanostructures, which can simultaneously carry imaging agents and therapeutic payloads. These nanosystems are engineered to improve pharmacokinetics, enhance imaging sensitivity, minimize systemic toxicity, and achieve site-specific drug delivery. The integration of multiple imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and optical imaging has significantly improved diagnostic accuracy and treatment guidance. NanoTheranostic platforms have shown remarkable potential in managing severe diseases, particularly cancer, neurological disorders, cardiovascular diseases, and chronic inflammatory conditions. Furthermore, the incorporation of artificial intelligence and machine learning techniques is emerging as a powerful tool for image interpretation, treatment optimization, and personalized therapy planning. Despite substantial advances, challenges related to biocompatibility, long-term toxicity, large-scale manufacturing, and regulatory approval continue to hinder clinical translation. This review comprehensively discusses the emerging trends in NanoTheranostics, focusing on nanomaterial design, imaging-therapy integration, disease-specific applications, translational challenges, and future perspectives toward clinical implementation. Positron emission tomography ; Medical imaging ; Magnetic resonance imaging ; Precision medicine ; Modalities ; Molecular imaging ; Optical imaging ; Personalized medicine ; Clinical Practice Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Radiomics and Machine Learning in Medical Imaging