jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yujia Zhang | Xin Zhang | Gang Zheng | Pinghui Mo | Zhuoying Zhao | Chenyang Li | Kai Tang | Jie Liu
Source: Neural Computing and Applications (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

A novel method of heterogeneous parallel machine learning by CPU–TPU for molecular dynamics Computational Science and Engineering ; Computer science ; Dynamics (music) ; Parallel computing ; Molecular dynamics ; Computational science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Spectroscopy and Quantum Chemical Studies
Authors: Huo, Songze | Cao, Xiao-Ming
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures The adsorption energy serves as a crucial descriptor for the large-scale screening of catalysts. Nevertheless, the limited distribution of training data for the extensively utilised machine learning interatomic potential (MLIP), predominantly sourced from near-equilibrium structures, results in unreliable adsorption structures and consequent adsorption energy predictions. In this context, we present DBCata, a deep generative model that integrates a periodic Brownian-bridge framework with an equivariant graph neural network to establish a low-dimensional transition manifold between unrelaxed and DFT-relaxed structures, without requiring explicit energy or force information. Upon training, DBCata effectively generates high-fidelity adsorption geometries, achieving an interatomic distance mean absolute error (DMAE) of 0.035 \textÅ on the Catalysis-Hub dataset, which is nearly three times superior to that of the current state-of-the-art machine learning potential models. Moreover, the corresponding DFT accuracy can be improved within 0.1 eV in 94\% of instances by identifying and refining anomalous predictions through a hybrid chemical-heuristic and self-supervised outlier detection approach. We demonstrate that the remarkable performance of DBCata facilitates accelerated high-throughput computational screening for efficient alloy catalysts in the oxygen reduction reaction, highlighting the potential of DBCata as a powerful tool for catalyst design and optimisation. Adsorption ; Interatomic potential ; Refining (metallurgy) ; Catalysis ; Potential energy ; Artificial neural network ; Outlier ; Molecular dynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: I Wayan Budiastra | Chorida Okasyari | Sutrisno Sutrisno
Source: Jurnal Keteknikan Pertanian (article, 2025-07-11)
Field: Chemistry — Analytical Chemistry

Prediction of Chemical Content in Robusta Coffee Beans Using Near Infrared Spectroscopy and Artificial Neural Network A rapid method to predict chemical content of robusta coffee is necessity, instead of chemical method that time consuming and expensive. This research aimed to predict chemical content of Pagar Alam robusta coffee beans using Near Infrared Spectroscopy (NIRS) and calibration through Principal Component Analysis-Artificial Neural Network (PCA-ANN). Reflectance of coffee bean was measured using NIR Flex N500 Spectrometer (1000–2500 nm), followed by determination of its chemical content using chemical method. The reflectance data were processed using spectra pre-treatment, then calibrated and validated with chemical content using PCA-ANN. The best model for moisture content was achieved with No1SG1 pre-treatment and 8 PC (r = 0.95; RPD = 2.95; consistency = 90.36%). Protein content can be predicted using 8 PC and No1SG1 pre-treatment (r = 0.92; RPD = 2.38; consistency = 96.78%). Good fat prediction employed No1SG1 pre-treatment and 10 PC (r = 0.93; RPD = 2.19; consistency = 80.08%). Ash content can be predicted using 8 PC and SG1 pre-treatment (r = 0.93; RPD = 2.29; consistency = 83.78%). The best carbohydrate prediction was obtained with No1SG1 pre-treatment and 5 PC (r = 0.93; RPD = 2.73; consistency = 108.85%). The results indicate that NIR spectroscopy can be used to predict accurately the chemical content of Pagar Alam robusta coffee beans. Artificial neural network ; Infrared spectroscopy ; Spectroscopy ; Infrared ; Content (measure theory) ; Biological system ; Chemistry ; Biology ; Artificial intelligence ; Computer science ; Mathematics ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses
Authors: G.L. Chen | Xuanyu Liu | Yue Zhang | D. Y. Lin | Pingli Mao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Leveraging Molecular Dynamics and Machine Learning to Predict Impact Performance in Polycrystalline Magnesium Alloys Crystallite ; Magnesium ; Dynamics (music) ; Molecular dynamics ; Materials science ; Magnesium alloy ; Computer science ; Artificial intelligence ; Metallurgy ; Psychology ; Chemistry ; Computational chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Xibao Gao | Sichao Wang | Bo Liu | Xiao Liu | Shuai Qu | Chen Guan | Qinglan Wang
Source: IEEE Photonics Technology Letters (article, 2025-06-17)
Field: Materials Science — Polymers and Plastics

Machine Learning Enhanced Single Crystal Fiber Fabrication via Laser Heated Pedestal Growth This study introduces a machine learning (ML)-assisted framework for optimizing Proportional-Integral-Derivative (PID) coefficients, enabling stable, long-length single crystal fiber (SCF) fabrication via the Laser Heated Pedestal Growth (LHPG) technique. Compared with traditional curve-fitting methods, the proposed ML approach significantly enhances computational efficiency and achieves up to 20 dB improvement in prediction accuracy, leading to notable improvements in SCF uniformity and achievable fiber length. The fabricated sapphire SCF demonstrates a length exceeding 3 m and an average diameter of 150 μm with fluctuations below ±3 μm, validating the effectiveness of the ML model. Additionally, fundamental guidelines for optimizing PID coefficients in LHPG systems are provided. These results offer valuable insights for high-quality SCF fabrication and are expected to facilitate the production of improved fibers suitable for harsh environment sensing applications. Pedestal ; Materials science ; Fabrication ; Optoelectronics ; Fiber laser ; Photonic-crystal fiber ; Fiber ; Laser ; Optics ; Composite material ; Mechanical engineering ; Physics ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Rui Tang | Ting Yu | Zi Li | Junru Wu | Xiaoming Zheng | Leiqing Pan | Yang Chen | Kun Duan | Hui Dong | Weijie Lan
Source: Food Control (article, 2025-08-28)
Field: Chemistry — Analytical Chemistry

Infrared spectroscopy combined with deep learning to describe the textural properties of cooked rice from raw materials: revealing spectral variations and internal correlations during processing Raw material ; Infrared spectroscopy ; Spectroscopy ; Food science ; Materials science ; Infrared ; Chemistry ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Jipin Peter | Sakshi Vijay | Tanu Choudhary | Raju K. Biswas
Source: ChemistrySelect (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Electronic Bandgap Prediction of Functionalized MX <sub>2</sub> ‐based Materials Using Machine Learning‐aided and First‐principles Studies Abstract Owing to the increasing interest and inherent properties, two‐dimensional (2D) transition metal dichalcogenides (TMDs) emerged as suitable candidates for diverse material applications. The precise characterization of a material toward a desired property is often governed by its bandgap. The accurate prediction of bandgap remains critical due to the limitation of conventional approaches, where experiment demands a lot of resources and the density functional theory faces constraints due to the in‐built estimation errors. To address this, the present study leverages on developing a robust machine learning model, harnessing both structural and compositional features to predict the accurate bandgap of 2D TMDs. The advancement in the accuracy of TMDs bandgap is reinforced by the selection of a suitable database screened out via hierarchical down selection methodology, incorporating bandgaps from both experimental and hybrid functional, which significantly improves the prediction capabilities. This is evidenced by the exceptional statistical regression metrics observed for the model across the training and test datasets. Moreover, to validate the predictive capability and versatility of the developed model, we predicted the bandgap of six materials that are unknown to the learning algorithm. Notably, the predicted bandgaps are found to be very close to the previously reported experimental as well as hybrid functional values. These findings underscore the adeptness of the model to accurately predict the bandgap, making it applicable for the fine‐tuning of a broad spectrum of 2D TMDs toward their desired applications. Band gap ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Optoelectronics MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Yumeng Li | Yingmin Jin | Tianfeng Zhang | Mengyu Fu | Ruifan Lin | Yuxin Gong | Guanshu Li | Yueping Xiong
Source:  (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

AI-Driven Predictions of Electrochemical CO2 Reduction Catalysts: Insights from In Situ Spectroscopy The electrochemical CO 2 reduction reaction is crucial for reducing atmospheric CO 2 and achieving carbon neutrality. Recent researches have predominantly focused on the development of high-performance catalysts, evaluating their performance remains time-consuming. Concurrently, in situ spectroscopic techniques have been instrumental in elucidating catalytic mechanisms and fundamental reaction pathways. In this study, we employ constant potential ab initio molecular dynamics simulations to precisely model the adsorption behavior of CO 2 molecules on Cu-Ag alloy surfaces, with a particular focus on tracking variations in the bond angles of the adsorbed CO 2. By generating 2600 unique spectral datasets, we leverage convolutional neural networks to extract key spectral features and train a deep learning model to predict the bond angles of adsorbed CO 2 based on the corresponding spectral information. Given that the bond angle of adsorbed CO 2 serves as a crucial descriptor of a catalyst’s ability to activate CO 2 molecules, our approach demonstrates the efficacy of AI in predicting catalytic activity. Furthermore, we extend this model’s applicability to Cu-Au and Cu-Zn alloys, establishing the potential for AI-driven preliminary catalyst screening based on spectral data. This methodology has the capacity to significantly accelerate the catalyst development pipeline by reducing the reliance on analyzing conventional experimental results manually. In situ ; Electrochemistry ; Catalysis ; Spectroscopy ; Reduction (mathematics) ; Materials science ; Chemistry ; Chemical engineering ; Electrode ; Physical chemistry ; Engineering ; Physics ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Jingzhuo Zhou | Zhengyu Gu | Xiaobing Wang | Xiuyun Zhang | Yixin Ouyang | Li Shi
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Correction: Screening of single-atom catalysts for CO<sub>2</sub> electroreduction to CH<sub>4</sub> using DFT calculations and machine learning Correction for ‘Screening of single-atom catalysts for CO 2 electroreduction to CH 4 using DFT calculations and machine learning’ by Jingzhuo Zhou et al. , J. Mater. Chem. A , 2025, 13 , 24073–24083, https://doi.org/10.1039/D5TA03431K. Catalysis ; Density functional theory ; Artificial neural network ; Feature (linguistics) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Bangsheng Chen | Maomao Li | Yi Gu | Wenzhu Lou | Shuaishuai Huang | Feiyan Mao | Tan Lian | Zhiyan Wang
Source: Research Square (preprint, 2025-05-28)
Field: Chemistry — Analytical Chemistry

Unraveling the Carcinogenic Mechanisms of Food contaminants through Network Toxicology, Machine Learning, and Molecular Docking Carcinogen ; Docking (animal) ; Toxicology ; Computational biology ; Computer science ; Chemistry ; Biology ; Biochemistry ; Medicine Dye analysis and toxicity
Authors: Howard Hu | Mengxian Zhao | Cun Chen
Source: Big Data and Information Analytics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-objective optimization design of process parameters for rare earth hard magnetic materials based on machine learning In the present work, combining machine learning and optimization theory, a novel framework is designed to optimize the process parameters of rare earth hard magnetic materials Nd7Fe67.5B21Nb2.5Zr2 with multi-objective performance. Considering the small dataset problem, the interpolation and extrapolation technique was introduced for data augmentation. Based on the expanded dataset, predictive models for coercivity (Hcj) and maximum magnetic energy product ((BH)max) concerning heat treatment parameters (annealing temperature and magnetic field strength) are established. Based on the enhanced data, the prediction accuracy of the model for Hcj and (BH)max reaches 0.980 and 0.997, respectively. After that, the NSGA-II algorithm is utilized for multi-objective optimization; the top 12 optimal process parameter combinations are recommended with higher coercivity and maximum magnetic energy product. This study provides a new way for the performance prediction and process optimization of rare earth hard magnetic materials, which can accelerate the design of materials with multi-objective performance. Extrapolation ; Coercivity ; Process (computing) ; Interpolation (computer graphics) ; Energy (signal processing) ; Magnet ; Rare earth Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic Properties and Synthesis of Ferrites
Authors: Changhao Zhang
Source:  (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Optimization of Solid-State Electrolyte Material Design and Performance Prediction Based on Machine Learning and Computational Simulation Solid-state electrolytes—key to next-gen safe, high-energy batteries-present multi-scale design hurdles now tackled by data-driven ML. Integrating Materials Project data with RF and GCNs boosts screening speed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}^{\mathbf{3}}-\mathbf{1 0}^{\mathbf{5}} \times$</tex> and reliably predicts conductivity and interfacial stability. The paper reviews feature engineering and multi-scale constraint modeling, links crystal descriptors to properties, and notes remaining issues of generalizability, small-sample learning, and cross-scale linkage, while outlining algorithmic routes to electrolytes that are both highly conductive and mechanically robust, aiming to accelerate solid-state battery commercialization through materials science-electrochemistry-AI synergy. Performance prediction ; Computational learning theory ; Predictive modelling ; Computational simulation ; Support vector machine ; Artificial neural network Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Thermal Expansion and Ionic Conductivity
Authors: Laurent Gornet
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning based predictions of Wöhler curve using physics-informed neural networks: self-heating determination of fatigue limits for laminated composite materials International audience Deep learning ; Deep drawing ; Artificial neural network ; Composite number ; Deep neural networks Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Lili Guo | Dingling Kong | Maoliang Hu | Meng Tang | Qingyou Zhang | Yanmei Zhou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crystal Density Prediction of Energetic Materials of Chlorides and Fluorides Based on Machine Learning Methods Crystal (programming language) ; Materials science ; Crystal structure prediction ; Computer science ; Crystal structure ; Mineralogy ; Chemistry ; Crystallography ; Programming language Machine Learning in Materials Science
Authors: Jamie Holber | Krishna Garikipati
Source: ArXiv.org (preprint, 2025-02-25)
Field: Materials Science — Materials Chemistry

Physics- and data-driven Active Learning of neural network representations for free energy functions of materials from statistical mechanics Accurate free energy representations are crucial for understanding phase dynamics in materials. We employ a scale-bridging approach to incorporate atomistic information into our free energy model by training a neural network on DFT-informed Monte Carlo data. To optimize sampling in the high-dimensional Monte Carlo space, we present an Active Learning framework that integrates space-filling sampling, uncertainty-based sampling, and physics-informed sampling. Additionally, our approach includes methods such as hyperparameter tuning, dynamic sampling, and novelty enforcement. These strategies can be combined to reduce MSE,either globally or in targeted regions of interest,while minimizing the number of required data points. The framework introduced here is broadly applicable to Monte Carlo sampling of a range of materials systems. Monte Carlo method ; Artificial neural network ; Energy (signal processing) ; Hyperparameter ; Sampling (signal processing) ; Novelty ; Active learning (machine learning) ; Range (aeronautics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Hyojin Kim | Dong Yun Shin | Jong Hyeak Choe | Gayoung Cheon | Hongryeol Yun | Jung‐Hoon Lee | Chang Seop Hong
Source: Small (article, 2025-12-29)
Field: Chemistry — Inorganic Chemistry

Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials Sigmoidal adsorption in flexible metal-organic frameworks (MOFs) can be achieved through a dramatic phase transition from narrow to large pores; however, such a unique phenomenon, which is not induced by abrupt phase changes, has rarely been reported. We report a series of flexible Hofmann-type MOFs, CoNi-Pipe, NiNi-Pipe, and CoPd-Pipe, pillared with a dimensional (3D) piperazine linker. Owing to the rotational flexibility and 3D dimensionality of the piperazine linker, CoNi-Pipe and CoPd-Pipe exhibited Xe-induced gate opening and the corresponding sigmoidal adsorption isotherms. CoNi-Pipe revealed not only excellent Xe/Kr separation but also Xe recovery performance, attributed to the greatest linker rotation and flexibility, along with moderate Xe binding strength, as validated by the machine learning potential-based molecular dynamics simulations and van der Waals-corrected DFT calculations. Thus, this work provides an effective approach for gaining a deeper understanding of the structural dynamics in flexible frameworks and expanding the repertoire of computational studies. Molecular dynamics ; Adsorption ; Flexibility (engineering) ; Sigmoid function ; Curse of dimensionality ; Linker ; Phase (matter) ; Piperazine Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Kavitha Thangavelu | J. Joseph | Tahseen Kamal | Malik Abdul Rub | Mazhar Ul‐Islam
Source: BioNanoScience (article, 2025-05-19)
Field: Materials Science — Polymers and Plastics

Synthesis, Characterization and Antibacterial Evaluation of MoS₂–Cellulose Acetate Nanocomposite with Neural Network Prediction Nanocomposite ; Cellulose acetate ; Characterization (materials science) ; Cellulose ; Artificial neural network ; Antibacterial activity ; Materials science ; Chemistry ; Organic chemistry ; Composite material ; Computer science ; Nanotechnology ; Artificial intelligence ; Bacteria Conducting polymers and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Jeetha Raj j | C. Justin Dhanaraj | T. Rasappan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Molecular Gradient Evolution Optimizer (Mogeo) – Based Artificial Neural Network (Ann) Optimization, Dft, Molecular Docking and Pharmacokinetic Studies of Ruthenium(Iii) and Vanadium(V) Complexes Ruthenium ; Vanadium ; Artificial neural network ; Docking (animal) ; Molecular dynamics ; Computational chemistry ; Chemistry ; Computer science ; Combinatorial chemistry ; Catalysis ; Artificial intelligence ; Organic chemistry ; Medicine Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods
Authors: Namrata Mishra | A. Baradeswaran
Source: Wear (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

AA8086/TiB2-MoS2 hybrid nanocomposites: Ultrasonic synthesis and machine learning prediction of wear behaviour Tribology ; Ultimate tensile strength ; Casting ; Tensile testing ; Dispersion (optics) ; Characterization (materials science) ; Nanocomposite ; Ultrasonic sensor Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Advanced materials and composites