jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Doaa Mohamed | Samuel García Vázquez | Behnam Mardani | Victor Dudarev | Alfred Ludwig | Maribel Quiñonez Acosta | Markus Stricker
Source: ArXiv.org (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in form of thin-film materials libraries, various materials characterization and simulations techniques are used to explore the materials data space of the problem. Machine learning and artificial intelligence techniques guide its exploration and navigation. The effective use of the combined heterogeneous data requires more than just a simple research data management plan. Consequently, our research data management system maps different data modalities in different formats and resolutions from different labs to the correct spatial locations on physical samples. Besides a graphical user interface, the system can also be accessed through an application programming interface for reproducible data-driven workflows. It is implemented by a combination of a custom research data management system designed around a relational database, an ontology which builds upon materials science-specific ontologies, and the construction of a Knowledge Graph. Along with the technical solutions of research data management system and lessons learned, first use cases are shown which were not possible (or at least much harder to achieve) without it. Ontology ; Data management ; Field (mathematics) ; Interface (matter) ; Relational database ; User interface ; Component (thermodynamics) ; Data model (GIS) ; Data modeling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Mozafar Rezaee | Saeid Ekrami | Seyed Majid Hashemianzadeh
Source: Scientific Reports (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Retraction Note: Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules Organic molecules ; Potential energy ; Energy (signal processing) ; Molecule ; Artificial neural network ; Potential of mean force Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Max Eissler | Tim Korjakow | Stefan Ganscha | Oliver T. Unke | Klaus-Robert Müller | Stefan Gugler
Source: The Journal of Chemical Physics (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

How simple can you go? An off-the-shelf transformer approach to molecular dynamics Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are increasingly replaced by general-purpose architectures trained on vast datasets. In line with this trend, several recent studies have questioned the necessity of architectural features commonly found in MD models, such as built-in rotational equivariance or energy conservation. In this study, we contribute to the ongoing discussion by evaluating the performance of an MD model with as few specialized architectural features as possible. We present a recipe for MD using an edge transformer (ET), an "off-the-shelf" transformer architecture that has been minimally modified for the MD domain, termed MD-ET. Our model implements neither built-in equivariance nor energy conservation. We use a simple supervised pretraining scheme on ∼30 × 106 molecular structures from the QCML database. Using this "off-the-shelf" approach, we show state-of-the-art results on several benchmarks after fine-tuning for a small number of steps. Using MD-ET as a simple and expressive testbed, we examine the effects of being only approximately equivariant and energy conserving for MD simulations and thereby try to evaluate the practical usefulness of unconstrained MD models. While our model exhibits runaway energy increases on larger structures, we show approximately energy-conserving NVE simulations for a range of small structures. Transformer ; Simple (philosophy) ; Architecture ; Molecular dynamics ; Energy minimization ; Artificial neural network ; Energy (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Qikun Tian | Ailing Chen | Haofeng Qin | Ruyi Li | Yi Zhang | Yuting Jiang | Xiong Zheng | Zhenzhen Qin | Guangzhao Qin
Source: Materials Today Physics (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Atomic-level engineering anisotropic thermal transport for directional heat dissipation in silicon electronics Anisotropy ; Thermal conductivity ; Phonon ; Thermal ; Heat transfer ; Work (physics) ; Silicon Thermal properties of materials ; Silicon and Solar Cell Technologies ; Machine Learning in Materials Science
Authors: Fergany Badry | Merve Gencturk | Karim Ahmed
Source: Journal of Nuclear Engineering (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

An Integrated Approach for Generating Reduced Order Models of the Effective Thermal Conductivity of Nuclear Fuels Accurate prediction of the effective thermal conductivity (ETC) of nuclear fuels is essential for optimizing fuel performance and ensuring reactor safety. However, the experimental determination of ETC is often limited by cost and complexity, while high-fidelity simulations are computationally intensive. This study presents a novel hybrid framework that integrates experimental data, validated mesoscale finite element simulations, and machine-learning (ML) models to efficiently and accurately estimate ETC for advanced uranium-based nuclear fuels. The framework was demonstrated on three fuel systems: UO2-BeO composites, UO2-Mo composites, and U-10Zr metallic alloys. Mesoscale simulations incorporating microstructural features and interfacial thermal resistance were validated against experimental data, producing synthetic datasets for training and testing ML algorithms. Among the three regression methods evaluated, namely Bayesian Ridge, Random Forest, and Multi-Polynomial Regression, the latter showed the highest accuracy, with prediction errors below 10% across all fuel types. The selected multi-polynomial model was subsequently used to predict ETC over extended temperature and composition ranges, offering high computational efficiency and analytical convenience. The results closely matched those from the validated simulations, confirming the robustness of the model. This integrated approach not only reduces reliance on costly experiments and long simulation times but also provides an analytical form suitable for embedding in engineering-scale fuel performance codes. The framework represents a scalable and generalizable tool for thermal property prediction in nuclear materials. Robustness (evolution) ; Thermal conductivity ; Uncertainty quantification ; Mesoscale meteorology ; Experimental data ; Scalability ; Thermal ; Nuclear fuel ; Finite element method Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Benjamin Baylis | John Dutcher
Source: Soft Matter (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning approaches to quantify nanoscale variations in the mechanical properties of soft nanoparticles We use machine learning to analyze atomic force microscopy-force spectroscopy (AFM-FS) measurements of the mechanical properties of soft nanoparticles on a hard substrate. We compare two approaches based on the manual selection of features that describe various aspects of the mechanical properties measured using AFM-FS - one that uses an extreme gradient boosting algorithm (supervised learning), and the other based on <i>k</i>-means clustering (unsupervised learning) to classify the force-distance curves according to the features. We used these approaches to generate two machine learning (ML) classifiers - one to differentiate between the soft nanoparticles and the hard substrate, and the other to identify structure within individual nanoparticles based on nanoscale variations in their mechanical properties. After training the classifiers on data from just two AFM images, we found that both approaches were successful in correctly identifying individual soft nanoparticles within the AFM-FS scans, whereas the supervised approach was more successful in correctly identifying and quantifying stiffer inner regions within the nanoparticles. The results of our study show that ML strategies can be used to accurately and efficiently characterize nanoscale variations in the mechanical properties of soft, biological materials. Nanoscopic scale ; Nanoparticle ; Force spectroscopy ; Atomic force microscopy ; Soft computing ; Spectroscopy Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Jie Hao | Youqi Zhang | Xiuli Chen | Yanzhe Zhu | Yu Tian | Xingwu Tian | Longguo Wu
Source: Industrial Crops and Products (article, 2026-03-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for chlorophyll detection: 1D-2D-COS feature optimization and adaptive deep learning modeling The chlorophyll of tomato leaves can be characterized in response to the influence of salt on leaf membrane permeability and plant growth. This study investigates the characterization of tomato leaf chlorophyll in response to salt stress by leveraging near-infrared (NIR) hyperspectral imaging (HSI). A novel predictive model integrating two-dimensional correlation spectroscopy (2D-COS) with a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) architecture was developed. To enhance model robustness, the sparrow search algorithm (SSA) was employed for parameter optimization alongside a self-attention mechanism. 2D-COS analysis identified chlorophyll-sensitive peaks at 1438 nm, 1627 nm, 1660 nm, and 993 nm, revealing the sequence of molecular bond variations. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and a combined one-dimensional-two-dimensional correlation spectroscopy (1D-2D-COS) approach. The SSA-CNN-BiLSTM-Multi-head-Attention model based on characteristic wavelengths extracted by 1D-2D-COS method worked best, reaching 0.9817 and 0.9178 for R C and R P , and 1.6091 and 3.1546 for RMSEC and RMSEP. The results demonstrate that the integration of 1D-2D-COS with the advanced deep learning algorithm provides an effective tool for monitoring leaf chlorophyll, offering theoretical support for the early detection of salt stress in plants. • 2D-COS combined with CNN-BiLSTM was used to predict chlorophyll content. • 2D-COS was used to locate sensitive peaks and resolve the order of chemical bonds. • 1D spectra and 2D-COS (1D-2D-CNN) were combined to extract the feature wavelengths. • SSA and self-attention mechanism was used to improve the robustness of models. • 1D-2D-COS-SSA-CNN-BiLSTM-Mutilhead-Attention model yielded the best results. Hyperspectral imaging ; Imaging spectroscopy ; Robustness (evolution) ; Convolutional neural network ; Pattern recognition (psychology) ; Deep learning ; Chlorophyll Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Zhongyu Liu | Boning Zhang | Yue Zhang | Wengang Bu | Zhenfeng Hu | Lei Zheng | Xiubing Liang
Source: Renewable and Sustainable Energy Reviews (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Recent advances in high-capacity hydrogen storage materials of multi-system systems via multi-scale exploration: A review Hydrogen storage ; Hydrogen ; Work (physics) ; Scope (computer science) Hydrogen Storage and Materials ; Advanced Photocatalysis Techniques ; Advancements in Battery Materials
Authors: Jie Li | Xingna Zheng | Pengyang Wang | Yiming Wang | Yihui Zhao | Yongxin Li | Hui Huang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Tailoring a Nanozyme-Based Electronic Tongue for Coffee Flavor Evaluation: Transcending Molecular Identification Electronic tongue ; Flavor ; Task (project management) ; Electrospinning ; Identification (biology) ; Component (thermodynamics) ; Pattern recognition (psychology) Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced Chemical Sensor Technologies
Authors: Jonas Düker | Lukas Hebing | Samuel Leweke | Rachel L. Nicholls | Maximilian Lübbesmeyer | Giulio Volpin | Burkhard König | Julius Hillenbrand
Source: Organic Process Research & Development (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Data Science-Assisted Workflow for Reaction Optimization in Process Chemistry We present a newly developed, data-assisted workflow at Bayer that integrates Bayesian optimization (BO) with CIME4R, an open-source data visualization tool with explainable AI features, to facilitate chemical reaction optimization. The workflow leverages the efficiency of BO for navigating high-dimensional reaction spaces while using CIME4R to visualize the algorithm’s decision-making process, exploration of the reaction space, and the influence and interactions of individual variables. These visualizations aid the interpretation of complex data sets and provide a platform for scientists to efficiently develop a deeper understanding of machine-learning-guided optimization campaigns, thereby improving accessibility and user trust, as well as decision-making efficiency. We demonstrate the workflow in a case study involving a new class of oxime amide ligands evaluated in Ullmann-type C–N cross-coupling reactions. This data-science-driven approach enabled the rapid identification of high-yielding conditions by sampling only 0.5% of the full parameter space within 4 days of experimental work. Feature-importance analysis revealed the solvent, propylene glycol methyl ether, as the most influential parameter, followed by K3PO4 as the preferred base. Workflow ; Visualization ; Class (philosophy) ; Identification (biology) ; Process (computing) ; Data visualization ; Chemical space ; Sampling (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Mohammadhasan Dinpajooh | Michael D. LaCount | Scott E. Muller | Neil J. Henson | Daniel Mejia-Rodriguez | Axel Gomez | Christopher J. Mundy | Andrew M. Ritzmann
Source: The Journal of Chemical Physics (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Modeling the behavior of concentrated aqueous HNO3 using machine learning interatomic potentials We develop two multi-defect machine learning interatomic potentials (MLIPs) trained at the BLYP-D2 and PBE-D3 density functional theories using the DeepMD-kit, allowing for the investigation of structural and thermodynamic properties of nitric acid over a wide range of concentrations via molecular dynamics (MD) simulations. We directly compute the degree of dissociation, α, and pKa from MD simulations, revealing that HNO3 behaves as a weaker acid at higher concentrations, noting that our standard-state pKa value is in excellent agreement with the experimental one. In general, good agreement is observed with experimental results such as α and density outside the training dataset, with only modest deviations at low-to-medium concentrations. We benchmark our custom multi-defect DeepMD MLIPs against foundational models MACE-MP0 and MACE-OFF23. The foundation models capture some aspects of HNO3/NO3- solvation in concentrated nitric acid but show noticeable density errors and miss subtle structural features relevant to spectroscopy, whereas the bespoke DeepMD MLIPs yield more compact solvation shells, reproduce density-concentration trends, and run ∼12-15× faster than MACE-MP0. Although classical FFs are still more efficient and match experimental densities better, they lack chemical reactivity and thus cannot predict α or pKa, underscoring the need for system-specific reactive MLIPs beyond universal MLIPs. Solvation ; Molecular dynamics ; Yield (engineering) ; Density functional theory ; Bespoke ; Range (aeronautics) ; Benchmark (surveying) ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Shinji Kotani | Tadashi Nakano
Source:  (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Trainable Multicellular Phase-Field Model for Targeted Pattern Formation via Local Interactions Multicellular systems form diverse morphologies through chemical and physical interactions among cells.In this study, we propose a trainable multicellular phase-field model that incorporates a neural network.The phase-field model represents cell morphology and position as continuous fields and serves as the basis for learning intercellular dynamics.In the proposed model, the neural network takes the local state as input and predicts the subsequent state, adjusting the spatial evolution of the multicellular system to approach a target pattern defined by a loss function.Through simulation experiments, we confirmed that the trained neural network enables cells to aggregate into a cluster and reproduce the spatial pattern of a given image.Moreover, similar results were obtained in a larger simulation domain than that used during training, demonstrating the scalability of the model. Multicellular organism ; Artificial neural network ; Pattern recognition (psychology) ; Pattern formation ; Domain (mathematical analysis) ; Scalability ; Evolutionary acquisition of neural topologies ; Position (finance) ; Cluster (spacecraft) Solidification and crystal growth phenomena ; Mathematical Biology Tumor Growth ; Lattice Boltzmann Simulation Studies
Authors: Bhasha Sathyan | Vishnu Harikumar | Gaurav Banerjee | Jobin Cyriac
Source: The Journal of Physical Chemistry C (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Smart Visual Fluorescence Sensing of Tetracycline Using N-Doped Nb <sub>2</sub> C Quantum Dots and CNN-Based Regression Quantification MXene quantum dots (QDs) have emerged as a promising class of zero-dimensional nanomaterials, attracting growing attention due to their remarkable physicochemical properties. Niobium carbide QDs (Nb2C QDs), derived from two-dimensional (2D) Nb2CTx, is a rising material with great potential in optical applications. Herein, nitrogen-doped Nb2C QDs (N–Nb2C QDs) were synthesized via sonication-assisted hydrothermal treatment. The obtained N–Nb2C QDs exhibit blue fluorescence with excitation-dependent emission. We developed a fluorescent sensor enabling N–Nb2C QDs for visual and quantitative detection of tetracycline (TC) antibiotics. The blue fluorescence of N–Nb2C QDs can be quenched by TC through static quenching and inner filter effect (IFE). The synergistic effect of both static quenching and IFE caused by TC makes the developed fluorescence sensor have a linear range for TC from 0.25 to 6.50 μM with a detection limit of 30.2 nM and a significant fluorescence color change from blue to cyan-green. Due to the simplicity and recognizable color change, a machine learning based approach was employed to determine the TC concentration from a visual fluorescence image. A convolutional neural network (CNN) architecture was used for the regression analysis. The model shows a strong predictive performance, achieving a coefficient of determination (R2) of 0.966, which reflects a high level of accuracy in estimating the TC concentrations. Quantum dot ; Fluorescence ; Quenching (fluorescence) ; Detection limit ; Analytical Chemistry (journal) ; Linear range ; Niobium MXene and MAX Phase Materials ; Carbon and Quantum Dots Applications ; 2D Materials and Applications
Authors: Ryotaro Sahashi | Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Decoupling structural and bonding effects on ferroelectric switching in ScAlN via molecular dynamics under an applied electric field ScxAl1-xN has emerged as a promising wurtzite-type ferroelectric material, where increasing the Sc composition reduces both the coercive field (Ec) and remanent polarization (Pr). This composition-dependent behavior is physically attributed to two simultaneous changes: the increase in the internal structural parameter u (structural effect) and the weakening of bond strength (bonding effect). Because these factors are strongly coupled in experiments, their individual contributions to ferroelectric switching remain unclear. In this study, we systematically decoupled these effects using machine-learning force field-based molecular dynamics (MD) simulations under an applied electric field. By artificially tuning u via in-plane strain at a fixed composition, we demonstrated that Pr is determined exclusively by the structural effect, exhibiting a universal linear dependence regardless of the composition. In contrast, Ec deviated from this structural trend, implying an additional compositional contribution. To isolate this, we evaluated configurations with identical u but varying Sc compositions; Pr remained constant, whereas Ec systematically decreased due to bond weakening. Furthermore, static nudged elastic band (NEB) calculations revealed that the static switching barrier depends solely on u, failing to explicitly capture the bonding effect on Ec. These results establish that while Pr is governed strictly by the structural effect, Ec is determined by a superposition of structural and bonding effects. Our findings highlight the necessity of dynamic MD simulations for fully understanding ferroelectric switching in compositionally tunable materials. Ferroelectricity ; Superposition principle ; Electric field ; Molecular dynamics ; Decoupling (probability) ; Polarization (electrochemistry) Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices