jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thijs ter Rele | René van Roij | Marjolein Dijkstra
Source: arXiv (Cornell University) (preprint, 2025-12-20)
Field: Materials Science — Materials Chemistry

Machine-Learned Many-Body Potentials for Charged Colloids reveal Gas-Liquid Spinodal Instabilities only in the strong-coupling regime of Primitive Models Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas-liquid and gas-solid phase separation observed in primitive-model simulations at low charge numbers. Extending the ML-based simulations to higher valencies, where primitive-model simulations become prohibitively slow, also reveals like-charge attractions and gas-liquid spinodal instabilities, however only in the regime of strongly coupled electrostatic interactions and not in the weakly coupled Poisson-Boltzmann regime of the experimental observations of colloidal like-charge attractions. Spinodal ; Colloid ; Charge (physics) ; Valency ; Phase (matter) ; Colloidal particle ; Electrolyte ; Spinodal decomposition Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Tomoya Kanno (7380959) | Tatsushi Ikeda (6443480) | Akira Nakayama (1353762)
Source: Figshare (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Grand CanonicalMonte Carlo Simulations for HydrogenAdsorption on Metal Surfaces Using Neural Network Potentials Hydrogen adsorption on metal surfaces (Pt, Pd, and Ir) is investigated by the grand canonical Monte Carlo simulations using density functional theory-based neural network potentials. By introducing the modified cavity bias method, generalized hybrid Monte Carlo method, and replica exchange method, atomic configurations across various temperatures and pressures are efficiently sampled. To ensure accurate predictions of adsorption energy of hydrogen atoms in diverse configurations, the neural network potentials are refined using additional loss terms that capture energy differences between systems with and without hydrogen atoms. These combined techniques enable us to analyze hydrogen density profiles on metal surfaces with reliable accuracy. Our results highlight the influence of temperature and pressure on hydrogen atom configurations and their interactions with the surfaces. This approach not only elucidates the intricate interactions between hydrogen atoms under specific thermodynamic conditions, which are critical for optimizing catalytic and hydrogen storage materials, but also demonstrates the efficacy of neural network potentials in providing high-fidelity simulations that are computationally feasible for complex surface phenomena. Hydrogen ; Monte Carlo method ; Artificial neural network ; Adsorption ; Metallic hydrogen ; Grand canonical ensemble ; Metal ; Hydrogen storage ; Hydrogen atom Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Jhonny Tiscareno Ramirez | Rafael Enrique Ramos Amigo | O. Concepción | Marisol Carmona-Sanchez | Zoran Ikonic | Giovanni Capellini | Detlev Grützmacher | F. Rivadulla | Dan Buca
Source: JuSER (Forschungszentrum Jülich) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

GeSn binary alloys for Room Temperature CMOS compatible energy harvesting applications Thermoelectric materials for energy harvesting applications present a promising approach to reducing the overall energy consumption of modern technologies that require substantial processing power, such as machine learning. These technologies generate significant waste heat, which could be recovered to improve energy efficiency. While various materials have been proposed for this purpose, a CMOS-compatible option that operates efficiently at room temperature has yet to be identified. Energy harvesting ; CMOS ; Energy consumption ; Energy (signal processing) ; Thermoelectric effect ; Thermoelectric materials ; Efficient energy use ; Binary number Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Nanowire Synthesis and Applications
Authors: Raymond P. H. Wu | Susumu Otsuki
Source: Journal of Computer and Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Proposal of Genuine Computing Methods in Materials Informatics In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to contribute to the originality of the standardization of the whole process of MI, starting with data quality measurement, the reusability of data and models, the development of a genuine data structure for machine learning, the alteration of design space, and finally, a novel coefficient-based analysis that can optimize the target yield and the likelihood. To achieve the objectives, our research has focused on the numerical analysis of informatics in materials science, supported by innovations in measurement and optimization processes. It also provides an overview of some of the recent successful data-driven MI strategies undertaken in this decade. The research also identifies some challenges the community is facing and those that should be overcome shortly, and streamlines a genuine process of MI. Informatics ; Standardization ; Process (computing) ; Materials informatics ; Work (physics) ; Quality (philosophy) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Big Data and Digital Economy
Authors: Lorenzo Francesia
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automatiserad Segmentering av Metallkorn : En Metod som Utnyttjar EBSD-Härledda Annoteringar Accurate characterization of grain size and distribution in metallic materials is extremely important for predicting their macroscopic properties and ensuring quality in industrial applications. Traditional metallographic analysis relies on time-consuming manual methods or automated image processing techniques with limited-precision. This thesis investigates the development and application of a deep learning pipeline for the automated segmentation of grain boundaries in Light Optical Microscopy (LOM) images of electrical steels, leveraging Electron Backscatter Diffraction (EBSD) data to generate annotations for model training. To answer the central research question of whether a deep learning model trained on EBSD-LOM correlative data could replicate expert analysis, a comprehensive evaluation of leading segmentation architectures was conducted. U-Net and U-Net++ architectures with ResNet encoders were optimized and rigorously assessed not only on pixel-level accuracy but, more critically, on their ability to reproduce key microstructural statistics that are vital for materials characterization. The study successfully demonstrates that an appropriately configured deep learning model can achieve results that are in very close agreement with manual annotations. The optimal model proved highly effective, accurately replicating grain count, size distributions, and standard industrial metrics like the ASTM Grain Size number. A subsequent investigation into post-processing with Generative Adversarial Networks (GANs) for post- processing indicated limited utility for global enhancement of already high- quality segmentations, unless the process was performed locally under user- supervision. This thesis validates that appropriately configured and optimized deep learning models, trained with EBSD-derived annotations, can provide a robust and accurate automated solution for quantitative microstructural analysis. Segmentation ; Deep learning ; Pipeline (software) ; Encoder ; Process (computing) ; Pattern recognition (psychology) ; Quality (philosophy) ; Artificial neural network Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions Despite rapid advances in molecular and materials machine learning, most models still lack physical transferability: they fit correlations across whole molecules or crystals rather than learning the quantum interactions between atomic pairs. Yet bonding, charge redistribution, orbital hybridization, and electronic coupling all emerge from these two-body interactions that define local quantum fields in many-body systems. We introduce QuantumCanvas, a large-scale multimodal benchmark that treats two-body quantum systems as foundational units of matter. The dataset spans 2,850 element-element pairs, each annotated with 18 electronic, thermodynamic, and geometric properties and paired with ten-channel image representations derived from l- and m-resolved orbital densities, angular field transforms, co-occupancy maps, and charge-density projections. These physically grounded images encode spatial, angular, and electrostatic symmetries without explicit coordinates, providing an interpretable visual modality for quantum learning. Benchmarking eight architectures across 18 targets, we report mean absolute errors of 0.201 eV on energy gap using GATv2, 0.265 eV on HOMO and 0.274 eV on LUMO using EGNN. For energy-related quantities, DimeNet attains 2.27 eV total-energy MAE and 0.132 eV repulsive-energy MAE, while a multimodal fusion model achieves a 2.15 eV Mermin free-energy MAE. Pretraining on QuantumCanvas further improves convergence stability and generalization when fine-tuned on larger datasets such as QM9, MD17, and CrysMTM. By unifying orbital physics with vision-based representation learning, QuantumCanvas provides a principled and interpretable basis for learning transferable quantum interactions through coupled visual and numerical modalities. Dataset and model implementations are available at https://github.com/KurbanIntelligenceLab/QuantumCanvas. Quantum ; Benchmark (surveying) ; Generalization ; Representation (politics) ; Field (mathematics) ; Coupling (piping) ; Basis (linear algebra) ; Stability (learning theory) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Ikuma Kohata | Kaoru Hisama | Keigo Otsuka | Shigeo Maruyama
Source: ArXiv.org (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials. Space (punctuation) ; Order (exchange) ; Bond ; Carbon fibers ; Bond order ; Computer science ; Artificial intelligence ; Chemistry ; Business ; Bond length ; Algorithm ; Crystallography Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Jude Y. Betow | Ammar Qaseem | Boris D. Bekono | Yue Feng | Aurélien F. A. Moumbock | Conrad V. Simoben | Smith B. Babiaka | Solange Ayukncha Tanyi | Vanessa Asoh Shu | Ariane T Ndi | Pascal Amoa Onguéné | Clovis S Metuge | Siméon Akame | Donatus Bekindaka Eni | Cyril T. Namba-Nzanguim | Mathieu Jules Mbenga Tjegbe | Marianka N L Dikande | Patience T Akeh | Sounders Balgah | Cyprian D Meh | Akachukwu Ibezim | Idris F Tabi | Yvette I. Malange | Bakoh Ndingkokhar | Leonel E. Njume | Kiran K. Telukunta | Saïd Amrani | Oyere T. Ebob | Wolfgang Sippl | Stefan Günther | Fidele Ntie‐Kang
Source: Nucleic Acids Research (article, 2025-11-17)
Field: Chemistry — Spectroscopy

African Natural Products Database (ANPDB): A resource for exploring the therapeutic potential of natural compounds from Africa The African Natural Products Database (ANPDB) is a comprehensive repository currently encompassing over 11 000 natural compounds sourced from diverse species across the African continent. ANPDB integrates both experimental and predicted NMR and MS data to enhance compound annotation and facilitate dereplication, utilizing machine learning algorithms for spectral simulation and fragment prediction. The database offers a range of methods for compound structure search, including substructure matching, chemical similarity analysis, and property-based filtering, empowering users to efficiently explore chemical diversity. ANPDB also compiles detailed metadata on the traditional medicinal uses of source organisms, linking ethnobotanical knowledge with chemical and biological data. By consolidating information on biological sources, bioactivities, traditional applications, and bibliographic references (currently ranging from 1961 to 2024), ANPDB provides a robust platform for drug discovery, natural products research, and the exploration of Africa's unique chemical and medicinal biodiversity. The database is open access, and the entire dataset and metadata are available for download and for computer-aided drug design endeavours. Link to the website: https://african-compounds.org. Metadata ; Annotation ; Data curation ; Natural (archaeology) ; Resource (disambiguation) ; Chemical database ; Download ; Similarity (geometry) ; Natural resource Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Imbachi Ordonez, Stephania
Source: Civil War Book Review (article, 2025-06-12)
Field: Chemistry — Analytical Chemistry

Rapid Quality Assessment of Sugarcane Using Near-Infrared Spectroscopy and Machine Learning Models Sugarcane is a critical agricultural commodity, supporting sugar production, biofuel industries, and rural economies worldwide. The global sugar industry is valued at approximately 78 billion dollars annually. Cane payment systems between factories and growers in Louisiana and other major sugarcane-producing regions rely on quality parameters such as pol, Brix, and moisture, traditionally determined through wet chemistry methods. Although effective, these methods are labor-intensive, time-consuming, and vulnerable to variability. Rising levels of extraneous matter (EM), including soil and leaves, have further impacted sugar recovery, driven by climate change, mechanized harvesting, and green cane regulations. Despite its influence, EM content is not routinely measured in factories due to the lack of practical methods. This challenge is particularly critical in Louisiana, where EM levels are among the highest reported globally, potentially affecting calibration model performance. This dissertation evaluates the application of Near-infrared (NIR) spectroscopy combined with machine learning (ML) models as a rapid and nondestructive alternative for analyzing sugarcane quality parameters and EM content. Calibration models for Brix, pol, moisture, and EM were developed using partial least squares regression (PLSR), functional regression (FR), support vector regression (SVR), and artificial neural networks (ANN). Total leaf content was predicted from shredded cane mixtures with known concentrations of cane, leaves, and soil, while soil content was predicted based on incinerated ash as the reference method. Machine learning (ML) models achieved strong performance across most parameters, with coefficients of determination (R²) typically exceeding 90 percent and root mean square errors (RMSE) remaining below 10 percent of the measured range. FR consistently provided improved performance compared to PLSR. Improving prediction accuracy is critical because NIR predictions are directly tied to payment calculations, and any errors can affect financial transactions between growers and factories. This research presents the first comprehensive methodology to quantify EM in sugarcane using NIR spectroscopy, addressing a longstanding gap in quality evaluation. The approach not only advances sugarcane industry practices but also offers potential applications for other crops where EM affects processing efficiency and product value. These findings support the future integration of NIR and ML models into factory operations and payment systems, promoting more accurate and economically sustainable agricultural industries. Partial least squares regression ; Calibration ; Artificial neural network ; Cane ; Support vector machine ; Quality (philosophy) ; Regression ; Coefficient of determination Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Soil Geostatistics and Mapping
Authors: Csirik, Mihály A. | Laestadius, Andre | Oster, Mathias
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

Neural network approximation of regularized density functionals Density functional theory is one of the most efficient and widely used computational methods of quantum mechanics, especially in fields such as solid state physics and quantum chemistry. From the theoretical perspecive, its central object is the universal density functional which contains all intrinsic information about the quantum system in question. Once the external potential is provided, in principle one can obtain the exact ground-state energy via a simple minimization. However, the universal density functional is a very complicated mathematical object and almost always it is replaced with its approximate variants. So far, no ``first principles'', mathematically consistent and convergent approximation procedure has been devised that has general applicability. In this paper, we propose such a procedure by first applying Moreau--Yosida regularization to make the exact functionals continuous (even differentiable) and then approximate the regularized functional by a neural network. The resulting neural network preserves the positivity and convexity of the exact functionals. More importantly, it is differentiable, so it can be directly used in a Kohn--Sham calculation. Convexity ; Artificial neural network ; Regularization (linguistics) ; Quantum ; Simple (philosophy) ; Energy functional ; Object (grammar) ; Approximation theory ; Density functional theory Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Yunbo Chu | Naixing Feng Naixing Feng | Zhixiang Huang
Source:  (article, 2025-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Polyaniline Metasurface Based on CGAN Neural Network In this article, we propose improved conditional generative adversarial neural networks (CGAN) for the inverse design of polyaniline metasurfaces based upon THz multi-resonant absorption spectra. the improved CGAN can achieve the inverse design of graphene metasurface images directly from the desired ulti-resonant absorption spectra. The CGAN model uses the absorption spectra as the conditional input to generate metasurface images with specific electromagnetic properties, and the reliability of the generated samples is improved through adversarial training. It is turned out that this work an finish facilitating the design process of artificial intelligence generated metasurfaces (AIGM), and even provide a useful guide or developing complex THz metasurfaces based on 2D materials sing generative neural networks. Inverse ; Absorption (acoustics) ; Artificial neural network ; Inverse problem ; Terahertz radiation ; Polyaniline ; Generative adversarial network Metamaterials and Metasurfaces Applications ; Electromagnetic wave absorption materials ; Plasmonic and Surface Plasmon Research
Authors: Bahle Luyanda Dave-Junior Mntambo | ‪Jamiu Olaseni Aribisala | Saheed Sabiu | Senzekile Majola | Robert Moonsamy Gengan | Talent Raymond Makhanya
Source: Chemistry (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

Microwave-Irradiated Eco-Friendly Multicomponent Synthesis of Substituted Pyrazole Derivatives and Evaluation of Their Antibacterial Potential The synthesis of novel pyrazole derivatives (SPDs) and their evaluation for antibacterial potential against Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Staphylococcus aureus (S. aureus), and Streptococcus pneumoniae (S. pneumoniae) was developed herein. These compounds were obtained via a microwave-assisted eco-friendly multicomponent reaction (MCR) and were characterized for structural confirmation using 1H NMR, 13C NMR, 2D experiments, TOF-MS, and FTIR spectrometry. Antibacterial activity, as measured by minimum inhibitory concentrations (MICs) of SPDs, ranged between 0.212 and 2.50 mg/mL against S. aureus, S. pneumoniae, P. aeruginosa, and E. coli. Compound 4e was the most potent against S. pneumoniae, with an MIC value of 0.0156 mg/mL compared with Amoxicillin’s MIC value of 0.0306 mg/mL. Thus, compound 4e was observed as a potential lead candidate against S. pneumoniae. Further corroboration by molecular docking at the active site of the key penicillin-binding protein (PBP) revealed that the most potent compounds against each organism showed comparable docking scores to those of amoxicillin. In addition, a pharmacokinetics study showed that synthesized SPDs were predicted to be orally bioavailable and non-inhibitors of cytochrome 3A4 and belong to drug classes 4 and 6. Hence, they were suitable for drug development and warrant further studies such as in vitro assays, in silico modeling, DFT studies, and machine learning for drug design. Pyrazole ; Docking (animal) ; In silico ; Escherichia coli ; Antibacterial activity ; Streptococcus pneumoniae ; Minimum inhibitory concentration ; Staphylococcus aureus ; Drug Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Microwave-Assisted Synthesis and Applications