jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md Zaibul Anam | Ogheneyoma Aghoghovbia | Mohammed Al‐Fahdi | Lingyu Kong | Victor Fung | Ming Hu
Source: arXiv (Cornell University) (preprint, 2025-09-03)
Field: Materials Science — Materials Chemistry

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs (EquiformerV2, MatterSim, MACE, and CHGNet) on 2,429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory (DFT) and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFC, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFC, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties. Phonon ; Benchmark (surveying) ; Benchmarking ; Force constant ; Thermal conductivity ; Lattice (music) ; Foundation (evidence) ; Thermal Machine Learning in Materials Science ; Thermal properties of materials ; Quantum many-body systems
Authors: Donald Hlungwani | Raesibe Sylvia Ledwaba | Phuti Ngoepe
Source: MATEC Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Development of machine-learned force fields for lithium-manganese-oxide (Li-Mn-O) spinel. Density functional theory accuracy at a lower computational cost The on-the-fly machine learning implemented in the VASP code was utilized to generate interatomic potentials for LiMn2O4 spinel. The potentials will be further expanded to incorporate the Li2MnO3 layered character to enable accurate large-scale atomistic simulations of layered-spinel composite structures at practical computational cost. Li2MnO3 is incorporated in LiMn2O4 structures, which are plagued by capacity fading due to the loss of manganese caused by irreversible structural changes, to improve the electrochemical performance of this material. As such, the generated MLFFs in the current study are a significant step in that direction. The MLFFs were able to reproduce the DFT data with a percentage difference of less than 2%, showing impressive accuracy. The generated MLFFs will be further expanded and improved in a future study. Density functional theory ; Current (fluid) ; Development (topology) ; Code (set theory) ; Computational complexity theory ; Interatomic potential ; Experimental data ; Simplicity Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Konrad Niderla | Tomasz Rymarczyk | Grzegorz Kłosowski | Monika Kulisz | Grzegorz Bartnik | Paweł Kaleta | Emanuel Józefacki | Dariusz Dudek
Source: Energies (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Crystallization Process Optimization Using Hybrid Tomographic Imaging and Deep Reinforcement Learning for Sustainable Energy Systems Crystallization is a fundamental unit operation in chemical, pharmaceutical, and energy industries, where strict control of crystal size distribution (CSD) is essential for ensuring product quality and process efficiency. However, the nonlinear dynamics of crystallization and the absence of explicit functional relationships between process variables make effective control a significant challenge. This study proposes a hybrid approach that integrates process tomography with deep reinforcement learning (RL) for adaptive crystallization control. A dedicated hybrid tomographic system, combining Electrical Impedance Tomography (EIT) and Ultrasound Tomography (UST), was developed to provide complementary real-time spatial information, while a ResNet neural network enabled accurate image reconstruction. These data were used as input to a reinforcement learning agent operating in a Simulink-based simulation environment, where temperature was selected as the primary controlled variable. To evaluate the applicability of RL in this context, four representative algorithms: Actor–Critic, Asynchronous Advantage Actor–Critic, Proximal Policy Optimization (PPO), and Trust Region Policy Optimization, were implemented and compared. The results demonstrate that PPO achieved the most stable and effective performance, ensuring improved control of CSD and improved control proxies consistent with potential energy savings. The findings confirm that hybrid tomographic imaging combined with RL-based control provides a promising pathway toward sustainable, intelligent crystallization processes with enhanced product quality and energy efficiency. Reinforcement learning ; Process (computing) ; Tomography ; Model predictive control ; Tomographic reconstruction ; Energy (signal processing) ; Process control ; Asynchronous communication ; Control reconfiguration Crystallization and Solubility Studies ; Process Optimization and Integration ; Fluid Dynamics and Mixing
Authors: Praveen Kumar Thanikonda | Govada Rambabu | Ratnam Chanamala | Adarsha Kumar Kaladari
Source: Engineering Research Express (article, 2025-05-27)
Field: Materials Science — General Materials Science

Optimization of dry sliding wear and hardness characteristics of AL-5083 metal matrix composites reinforced with SiC, Mg, AND Sr particles Abstract This work provides an investigation of the dry slide wear properties and Brinell hardness test results for a stir-cast aluminum alloy, namely Al-5083, that has been reinforced with Silicon Carbide (SiC) Magnesium (Mg) and Strontium (Sr) particles. During the synthesis process, various weight percentages of SiC (2–12 wt%), Mg (2–12 wt%), and Sr (0.02–0.12 wt%) particles were used. An investigation was carried out to analyse the properties of wear in both the aluminum alloy and its composites. A pin-on-disc testing equipment was used to subject them to a wear test. The effect of wear factors on metal matrix composites (MMCs) dry sliding wear was studied, including factors such as, sliding speed, applied load, slide-distance, and 0%–12% of reinforcement. Results were systematically gathered by performing a sequence of experiments utilizing Taguchi’s methodology as per ASTM standards. To better model nonlinear behavior and enhance prediction, a supervised Machine Learning model-baseline Random Forest Regression-was introduced, achieving an R 2 value of 0.954. This integration of data-driven modelling enhances the novelty of the study compared with traditional methods such as ANOVA, Taguchi’s L7 design, and linear regression approaches. Tests were conducted to compare the experimental outcomes predicted by the aforementioned connection. Materials science ; Composite material ; Metal ; Dry friction ; Matrix (chemical analysis) ; Metallurgy Material Properties and Applications ; Engineering Technology and Methodologies ; Additive Manufacturing and 3D Printing Technologies
Authors: James Paoloni | Miriam Jackson | Elvis Pandžić | Md. Shariful Alam | Erik Meijering | Jonathan Yeow | Megan S. Lord
Source: Small (article, 2025-12-30)
Field: Materials Science — Biomaterials

Decoding Endothelial Glycocalyx‐Nanoparticle Interactions via a Systematic Polymeric Nanoparticle Library and High‐Content Characterization Understanding how nanoparticle physicochemical properties influence cellular interactions is critical for optimizing drug delivery systems. Here, we report the high-throughput synthesis and biological characterization of a 42-member library of self-assembled polymeric nanoparticles that systematically vary in hydrodynamic diameter and surface charge. Using reversible addition-fragmentation chain-transfer (RAFT) polymerization and polymerization-induced self-assembly (PISA), nanoparticles were synthesized in microplate formats and characterized for cytotoxicity and uptake by endothelial cells expressing either immature or mature glycocalyces. We found that surface charge had a greater impact on cytotoxicity and cellular uptake than hydrodynamic diameter, with some cationic nanoparticles exhibiting higher toxicity and uptake. Notably, cells with a mature glycocalyx showed increased tolerance and uptake of cationic nanoparticles, suggesting a protective and regulatory role of the glycocalyx. A semi-automated imaging workflow incorporating machine learning-based cell segmentation enabled single-cell quantification of nanoparticle uptake, revealing population-level variability across the library. Protein corona formation in serum conditions further modulated nanoparticle surface charge and interactions. This study highlights the importance of integrating nanoparticle design with biological context and scalable analytical tools, offering a framework for optimizing nanoparticle formulations for targeted delivery across diverse cell types. Nanoparticle ; Cytotoxicity ; Context (archaeology) ; Surface charge ; Drug delivery ; Cationic polymerization ; Biocompatibility ; Characterization (materials science) ; Nanogel Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Trauma, Hemostasis, Coagulopathy, Resuscitation
Authors: Sonam Kumari | Aman Kumar | Jyotsana Mehta | Giovanna Marrazza | Ganga Ram Chaudhary | Sandeep Kumar
Source: TrAC Trends in Analytical Chemistry (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

AI-enhanced surface functionalization in biosensors: From fundamentals to future prospects Surface modification ; Biosensor ; Nanomaterials ; Biomolecule ; Biomimetic materials Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xu Zhongqi | SHURUI ZHANG | Yujue Wang | Bin Wu | Yu Xiong | Xiucheng Liu
Source: Measurement Science and Technology (article, 2025-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of excitation parameters for neural network-based prediction of mechanical properties in low-carbon steel with varying thicknesses Abstract This study explores a neural network model–based micromagnetic method for predicting three key mechanical properties of low-carbon steel under complex excitation parameters. By simultaneously considering excitation frequency, excitation amplitude, and specimen thickness (0.4 mm, 0.5 mm, and 1 mm), large numbers of magnetic signals are acquired through micromagnetic experiments. Based on the ReliefF algorithm, magnetic features are ranked in descending order according to their weight coefficients for effectively reducing the dimensionality of the feature set. After training and evaluating neural network models, an optimal excitation range (OER) was identified, significantly improving prediction accuracy across a variety of excitation parameters. Notably, the proposed OER exhibited strong generalization capabilities with respect to material thickness, thereby enabling a unified excitation strategy that can be applied to specimens of varying dimensions. These results underscore both the effectiveness of neural networks in modeling high-dimensional excitation–property relationships and the methodological potential of OER for enhancing predictive performance in materials with different thicknesses. Excitation ; Artificial neural network ; Generalization ; Curse of dimensionality ; Micromagnetics ; Feature (linguistics) ; Range (aeronautics) Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Yilin Wang | Jingwei Zhao | Shengxiao Gao | Si-Si Zhao | Xiaoqi Li | Xue Chen | Hang Zhang | Tianyi Tian | Zhen Zhao
Source: Journal of Materials Science (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Nitrogen-doped carbon dots as fluorescent probes for a web system-assisted intelligent detection model for flavonoids Detection limit ; Fluorescence ; Kaempferol ; Convolutional neural network ; Quantum dot ; Dynamic range Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Guancheng Lu | Yanmei Meng | Juan Huang
Source: Journal of Food Science (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

A Novel Control Method of Sugar Boiling Based on Model‐Free Adaptive Control and Neural Networks To enhance sugar absorption and improve crystal uniformity, a novel adaptive control method for sugar boiling that integrates model-free adaptive control and neural networks is proposed to address the challenge of effective control owing to the complex nonlinear relationships. This method leverages the sugar brix deviation and sugar level deviation in the vessel to drive neural networks, which calculate the opening coefficients of hot water valves, syrup valves, and steam valves. Consequently, adaptive control was achieved during the sugar boiling process. The experimental results demonstrate that the apparent purity obtained by the proposed method reached 22.52%, surpassing both the manual method (with an apparent purity of 21.53%) and the PID-based method (with an apparent purity of 21.31%). Compared with these methods, the proposed approach excels in crystal uniformity, color value, and apparent purity difference. It is strongly recommended that this method be adopted for automatic control of sugar boiling to further enhance these critical parameters. Sugar ; Boiling ; Artificial neural network ; Adaptive control ; Control theory (sociology) ; Nonlinear system ; Boiling point Crystallization and Solubility Studies ; Process Optimization and Integration ; Advanced Control Systems Optimization
Authors: Ling Zhang | Ning Lin | Lu Yang
Source: Minerals (article, 2025-04-05)
Field: Materials Science — Polymers and Plastics

Machine Learning Approaches for Predicting the Elastic Modulus of Basalt Fibers Combined with SHapley Additive exPlanations Analysis The elastic modulus of basalt fibers is closely associated with their chemical composition. In this study, eight machine learning models were developed to predict the elastic modulus, with hyper-parameter tuning implemented through the GridSearchCV technique. Model performance was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE). SHAP analysis was employed to uncover the relevance of oxide compositions and their interactions with the elastic modulus. Among these models, the Categorical Boosting algorithm exhibited the best results, with an R2 of 0.9554, an RMSE of 4.7556, and an MAE of 2.0323. SHAP analysis indicated that CaO had the most significant influence on elastic modulus predictions. The importance of other oxides was ranked as follows: SiO2, Al2O3, MgO, K2O, Na2O, Fe2O3, FeO, and TiO2. Additionally, SHAP analysis determined oxide ranges for positive elastic modulus prediction. This research provides new insights into leveraging machine learning to optimize the mechanical properties of basalt fibers. Basalt ; Modulus ; Elastic modulus ; Geology ; Geochemistry ; Mathematics ; Computer science ; Materials science ; Composite material Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Textile materials and evaluations
Authors: Pavel Martynenko | R.V. Duritsyn
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Computational analysis of N-B bond strengths in BBr3-amine complexes: a multi-method study of binding energies, ESP distribution, and solvent effects Lewis acids and bases ; Adduct ; Solvent effects ; Ligand (biochemistry) ; Range (aeronautics) ; Potential energy surface ; Solvent ; Molecule Organoboron and organosilicon chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Sifan Cao (4538038) | Yuan Liu (88411) | Yuwan Du (20605315) | Wenlong Li (571749) | Xincun Dou (1784302)
Source: Figshare (article, 2025-04-25)
Field: Chemistry — Organic Chemistry

MonitoringAmphetamine and Methamphetamine MixturesBased on Deep Learning Involves Colorimetric Sensing Precise recognition and discrimination of highly similar analytes (either in structure or property) with distinguishable sensing responses are challenging but significant in the practical application of drug seizing, food additive inspection, environmental monitoring, etc. Here, a colorimetric differentiation strategy was proposed by modulating the probe structure to influence the aggregate behaviors of the reaction products; thus, amphetamine (AMP) and methamphetamine (MA) with the sole structural difference of a methyl group were successfully discriminated. Specifically, upon recognition of the furan ring-opening reaction, the probe was screened out from a series of furan-based probes with different electron-withdrawing groups, which further facilitated the aggregate state difference of reaction products and then amplified the difference in colorimetric responses. In addition, the probe-embedded porous polymer substrate was fabricated to accelerate the response for trace AMP and MA, and the judgment of doping ratios of AMP and MA in the mixtures was realized for the first time by further combining with the self-developed Drugs Analyst as well as deep learning algorithms. Hence, we envisage that this structural-modulation-enabled colorimetric differentiation strategy will shine light on the multianalyte discrimination from aspects of optical sensing development and multidisciplinary fusion. Analyte ; Methamphetamine ; Substrate (aquarium) ; Amphetamine ; Significant difference ; Colorimetry Polydiacetylene-based materials and applications ; Advanced Chemical Sensor Technologies ; Molecular Sensors and Ion Detection
Authors: Juan I. Di Filippo | Santiago Rómoli | Claudio N. Cavasotto
Source: ACS Omega (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, high-throughput docking campaigns aided by machine learning (ML)-based protocols have emerged as a way to accelerate the identification of top-scoring molecules within ultralarge chemical molecule libraries. However, studies validating these ML approaches used one or two targets and/or small molecule libraries. Herein, we extended the validation of ML protocols at retrieving virtual hits in an accelerated fashion by using two standard publicly available ∼100M molecule libraries and also a comprehensive benchmark set involving molecular docking scores of a 10M molecule library in 10 diverse protein targets with two docking programs, PLANTS and AutoDock Vina. In the 10M benchmark set, we have shown that, on average, more than 60 and 70% of the top 10k and top 1k molecules, respectively, can be retrieved while reducing the number of docking evaluations by more than 97%, indicating a robust performance of the ML protocol. With larger molecule libraries, we have shown that a proportional increase in the training set size enhances the performance of the ML model at retrieving virtual hits. In summary, our results support the use of ML methods to retrieve top-scoring molecules for chemical libraries containing hundreds of millions or even billions of molecules, where the role of ML models becomes even more critical as brute-force exploration of such chemical libraries through molecular docking is inaccessible in reasonable time frames. Robustness (evolution) ; Scalability ; Computer science ; Docking (animal) ; Throughput ; Artificial intelligence ; Machine learning ; Chemistry ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software System Performance and Reliability
Authors: Natasha
Videcrantz Faurschou (22312844) | Victor Friis (22312847) | Priyanka Raghavan (18586296) | Christian Marcus Pedersen (1452088) | Connor W. Coley (1496653)
Source: Figshare (article, 2025-09-25)
Field: Chemistry — Organic Chemistry

Anomeric Selectivityof Glycosylations through a MachineLearning Lens Predicting the stereoselectivity of glycosylations is a major challenge in carbohydrate chemistry. Herein we show that it is possible to build machine learning models that can predict the major anomer of a glycosylation, whether the other anomer is observed as the minor product, and the anomeric ratio of the two anomers. The three models are integrated into a publicly available tool, GlycoPredictor. From a statistical analysis of literature data, we analyze glycosylation trends and compare them to known trends in the field of carbohydrate chemistry, making it possible to elucidate a hierarchy of rules governing the stereoselectivity of glycosylations and discover promising new trends that complement expert intuition, which are tested in novel glycosylation methods. Anomer ; Glycosylation ; Stereoselectivity ; Complement (music) ; Training set ; Selectivity Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Machine Learning in Materials Science
Authors: Narayan Kabra | Gianluca Serra | Miguel Betancourt Lozano | Matteo Tommasini | Karthik Sankaranarayanan | Nisha Rani Agarwal
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Cannabinoid Spectra Using Machine Learning Pattern recognition (psychology) ; Noise (video) ; Raman spectroscopy ; Spectral line ; Random forest ; Process (computing) ; Gaussian process ; Class (philosophy) Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies