jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kakhandiki, Pranav | Chitturi, Sathya | Ratner, Daniel | Gasiorowski, Sean
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution The discovery of a minimum energy pathway (MEP) between metastable states is crucial for scientific tasks including catalyst and biomolecular design. However, the standard nudged elastic band (NEB) algorithm requires hundreds to tens of thousands of compute-intensive simulations, making applications to complex systems prohibitively expensive. We introduce Neural Network Bayesian Algorithm Execution (NN-BAX), a framework that jointly learns the energy landscape and the MEP. NN-BAX sequentially fine-tunes a foundation model by actively selecting samples targeted at improving the MEP. Tested on Lennard-Jones and Embedded Atom Method systems, our approach achieves a one to two order of magnitude reduction in energy and force evaluations with negligible loss in MEP accuracy and demonstrates scalability to >100-dimensional systems. This work is therefore a promising step towards removing the computational barrier for MEP discovery in scientifically relevant systems, suggesting that weeks-long calculations may be achieved in hours or days with minimal loss in accuracy. Scalability ; Artificial neural network ; Metastability ; Energy (signal processing) ; Bayesian probability ; Reduction (mathematics) ; Deep neural networks ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: K T Thilagham K T | Swapnaja Amol Ubale | Rajiv Gandhi K | Sushant Chamoli | Jyotsna Pandit | N Doddabasappa
Source: SSRN Electronic Journal (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Predicting and Detecting Wear Properties of Arecanut Fiber Reinforced Carbon Composites Using Artificial Neural Networks Taguchi methods ; Artificial neural network ; Orthogonal array ; Materials science ; Composite number ; Wear resistance ; Composite material ; Test data ; Fiber ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Lubricants and Their Additives
Authors: Kamyab Kashmarizad | Farzad Alavi | Mir Masoud Seyyed Fakhrabadi
Source: The European Physical Journal Plus (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Comparative analysis of classical and machine learning models for predicting the mechanical properties of pristine and defective 2D coinage metals Copper ; Ultimate tensile strength ; Interatomic potential ; Nanosheet ; Hexagonal crystal system ; Young's modulus ; Gaussian ; Elastic modulus Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Zongqing Hu | Shaojie Li | Jianfeng Jin | Yuping Ren | Rui Hou | Lei Yang | Gaowu Qin
Source: Materials Genome Engineering Advances (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Data‐driven designing on mechanical properties of biodegradable wrought zinc alloys Abstract A small dataset of ∼300 datapoints of zinc (Zn) alloys were collected and 125 entries containing alloying elements, extrusion parameters (temperature (ET), speed (ES) and ratio (ER)), and mechanical properties (yield strength (YS), ultimate tensile strength (UTS), and final elongation (EL)) were selected. Machine learning models were applied to predict mechanical properties, in which random forest (RF) model exhibited the best performance and further validated by a new experimental sample of Zn‐0.05Mg‐0.5Mn, with the mean absolute percentage error (MAPE) less than 10%. [Correction added on 13 May 2025, after first online publication: In the preceding sentence, the value ‘12%’ has been changed to ‘10%’.]. Moreover, an empirical formula was induced by the clustering model (CL). Control over strain softening/hardening behavior was achieved through only process parameter adjustment. Finally, by combining multi‐objective genetic algorithm and RF models, the optimization alloy composition and extrusion parameters was carried out, targeting high‐strength, strength/plasticity synergy, and high plasticity for biodegradable purpose. A notable optimized scheme for strength/plasticity synergy in Zn‐0.20Mg‐0.60Mn (wt.%) achieves the YS of 303 MPa, UTS of 354 MPa, and EL of 25.1% with the MAPE less than 10%, and exhibits the strain‐hardening response, associated with ER of 16, ET of 170°C, and ES of 3.21 mm/s. [Correction added on 13 May 2025, after first online publication: In the preceding sentence, the value ‘345 MPa’ has been changed to ‘354 MPa’. and the value ‘3.33 mm/s’ has been changed to ‘3.21 mm/s’]. Zinc ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties
Authors: Kakhandiki, Pranav | Chitturi, Sathya | Ratner, Daniel | Gasiorowski, Sean
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution The discovery of a minimum energy pathway (MEP) between metastable states is crucial for scientific tasks including catalyst and biomolecular design. However, the standard nudged elastic band (NEB) algorithm requires hundreds to tens of thousands of compute-intensive simulations, making applications to complex systems prohibitively expensive. We introduce Neural Network Bayesian Algorithm Execution (NN-BAX), a framework that jointly learns the energy landscape and the MEP. NN-BAX sequentially fine-tunes a foundation model by actively selecting samples targeted at improving the MEP. Tested on Lennard-Jones and Embedded Atom Method systems, our approach achieves a one to two order of magnitude reduction in energy and force evaluations with negligible loss in MEP accuracy and demonstrates scalability to &gt;100-dimensional systems. This work is therefore a promising step towards removing the computational barrier for MEP discovery in scientifically relevant systems, suggesting that weeks-long calculations may be achieved in hours or days with minimal loss in accuracy. Scalability ; Artificial neural network ; Metastability ; Energy (signal processing) ; Bayesian probability ; Reduction (mathematics) ; Deep neural networks ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ArXiv.org (preprint, 2025-09-09)
Field: Chemistry — Inorganic Chemistry

Benchmarking Universal Interatomic Potentials on Zeolite Structures Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the development of multiple new universal IPs that cover wide ranges of the periodic table, their applicability to target chemical systems should be carefully investigated. We benchmark several universal IPs using equilibrium zeolite structures as testbeds. We select a diverse set of universal IPs encompassing two major categories: (i) universal analytic IPs, including GFN-FF, UFF, and Dreiding; (ii) pretrained universal machine learning IPs (MLIPs), comprising CHGNet, ORB-v3, MatterSim, eSEN-30M-OAM, PFP-v7, and EquiformerV2-lE4-lF100-S2EFS-OC22. We compare them with established tailor-made IPs, SLC, ClayFF, and BSFF using experimental data and density functional theory (DFT) calculations with dispersion correction as the reference. The tested zeolite structures comprise pure silica frameworks and aluminosilicates containing copper species, potassium, and organic cations. We found that GFN-FF is the best among the tested universal analytic IPs, but it does not achieve satisfactory accuracy for highly strained silica rings and aluminosilicate systems. All MLIPs can well reproduce experimental or DFT-level geometries and energetics. Among the universal MLIPs, the eSEN-30M-OAM model shows the most consistent performance across all zeolite structures studied. These findings show that the modern pretrained universal MLIPs are practical tools in zeolite screening workflows involving various compositions. Zeolite ; Aluminosilicate ; Benchmark (surveying) ; Benchmarking ; Set (abstract data type) ; Density functional theory ; Workflow Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhihua Lv | Zhancheng Dou | Ziyang Dai | Maoxuan Wang | Zhenguo Li | Yuankai Shao | Chen Wang
Source:  (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Interval Type-2 Fuzzy Neural Network Based emission Prediction of SCR with Varying Degrees of Hydrocarbon Poisoning For the purpose of complying with the more stringent emission regulations for the next stage, the dual-injection selective catalytic reduction (SCR) system has become the most promising technical route for diesel engines, in which the performance throughout the entire life of the close coupled SCR(ccSCR) is crucial for NOx emission reduction. Without the protection of diesel oxidation catalysts(DOC), the ccSCR suffers from complex exhaust gas such as unconverted hydrocarbons and high concentration of particulate matter. To evaluate the negative impact on emission, this paper proposes an interval type-2 fuzzy neural network (IT2FNN) to predict emission of aftertreatment system. An DPF regeneration condition is adopted for accelerating hydrocarbon aging. The NOx conversion performance of ccSCR has declined after different durations due to hydrocarbon poisoning. In the following, the emission of WHTC are estimated by the NOx conversion performance data. Finally, the IT2FNN is adopted for predict emission. The comparative results show that the IT2FNN is superior to the least square method used in the CHINA VI emission NOx ; Diesel fuel ; Hydrocarbon ; Interval (graph theory) ; Artificial neural network ; Diesel particulate filter ; Particulates ; Reduction (mathematics) Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Biodiesel Production and Applications
Authors: Prachi Garg | Karthick Gothandapani | Baishakhi Mazumder
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Revealing Degradation Mechanisms in YSZ Ceramics Through Machine Learning-Guided Aging and Multiscale Characterization Characterization (materials science) ; Yttria-stabilized zirconia ; Degradation (telecommunications) ; Materials science ; Ceramic ; Psychology ; Computer science ; Nanotechnology ; Composite material ; Engineering ; Electronic engineering ; Cubic zirconia Advanced ceramic materials synthesis ; Advancements in Solid Oxide Fuel Cells ; Recycling and utilization of industrial and municipal waste in materials production
Authors: N. I. Vorobyov | L. A. Ilyina | G. Yu. Laptev | Marina V. Selina | A. A. Guselnikova
Source: International Journal of Veterinary Medicine (article, 2025-01-17)
Field: Chemistry — Analytical Chemistry

NEURAL NETWORK ANALYSIS OF SILAGE FORAGE QUALITY BASED ON THE BIOCONSOLIDATION INDEX OF LACTIC ACID BACTERIA BIOSYSTEMS In this study, using the original neural network information technology, it was possible to perform an intelligent analysis of molecular genetic data of silage microflora and evaluate the influence of silage starters and preservatives on the directionality of microbiological biochemical processes in silage biomass. The output product of the intelligent analysis of fractal profiles of silage microflora was the CSIgaZ and CSImU indices, chosen to represent quantitatively, in terms of microbiological data, the intensity of silage outgassing and the amount of lactic and acetic acids in silage. According to the CSImU index it was possible to evaluate the increase of lactic acid and decrease of acetic acid in silage, which signals the slowing down of development of putrefactive microorganisms in silage, and according to the CSIgaZ index - the decrease of silage gas emissions, which indicates favourable anaerobic conditions in which microbiological biochemical processes in silage take place. The laboratory experiment on silage of hedgehog silage conducted in the molecular genetic laboratory of ‘Biotrof’ Ltd. showed that ‘Biotrof-111’ (based on Bacillus subtilis, produced by ‘Biotrof’ Ltd.) creates the best anaerobic conditions in the silaged plant biomass, where the microflora of silage bioconsolidates in the biosystem of lactic acid bacteria. LLC ‘Biotrof’) creates the best anaerobic conditions in the silage plant biomass, in which the silage microflora bioconsolidates into a biosystem of lactic acid bacteria with a minimum value of CSIgaZ index = 3.1 and a maximum value of CSImU index = 6.8, that is, in this variant of silage silage hedgehog silage microflora of silage manages to group in such a way, that simultaneously provides a relatively low intensity of silage gas emissions, accelerated formation of lactic acid and slow formation of acetic acid in silage, and thus in these conditions the processes will be completed with the best quality nutritional indicators of silage. Silage ; Lactic acid ; Forage ; Index (typography) ; Bacteria ; Quality (philosophy) ; Biology ; Food science ; Agronomy ; Computer science ; World Wide Web ; Philosophy ; Genetics Spectroscopy and Chemometric Analyses
Authors: Humaira Seema | Muhammad Arshad | Arslan Maqbool | Sultan Zeb | Hamid Ali | Muhammad Umar | Sajjad Hussain | Hammad Khan
Source: Journal of Water Process Engineering (article, 2025-09-18)
Field: Chemistry — Organic Chemistry

Sustainable wastewater treatment via nitrogen/sulfur co-doped graphene composite: Mechanistic insights &amp; process optimization Adsorption ; Endothermic process ; Response surface methodology ; Graphene ; Aqueous solution ; Central composite design ; Wastewater ; Process optimization Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Graphene and Nanomaterials Applications
Authors: Ling Yan | Shihui Zhu
Source:  (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Intelligent Optimization of Nanomaterial Synthesis Process by Integrating Multimodal Data and Large Language Models With the rapid development of nanotechnology, it has achieved remarkable results in synthesis, characterization and application, but it also faces challenges such as high data complexity, high experimental costs, and difficulty in design optimization. The integration of multimodal data and large language models provides a novel approach to addressing these challenges. At present, the application of artificial intelligence in nanotechnology is in the development stage, with problems such as low method integration, lack of systematic research, and difficulty in effectively guiding the design and preparation of nanomaterials. This paper first introduces the research background and significance, and then sorts out the related research work and points out the shortcomings. In the method part, an AI model construction method integrating multimodal data with large language models is proposed, which specifically includes a nanomaterial structure prediction method based on deep learning, a method for optimizing the synthesis process of nanomaterials using reinforcement learning, and a nanotechnology knowledge reasoning method combined with knowledge graphs to achieve intelligent guidance for nanomaterials from design to preparation. The experimental results show that after the 50th iteration, the yield is basically stable at a high level. Compared with the traditional synthesis process, the optimized synthesis process has significantly improved the yield. The average yield of the traditional process is 65%, while the average yield of the optimized process is 85%, an increase of 20 percentage points. The research results have built an efficient artificial intelligence application framework and improved the efficiency and success rate of nanomaterial research and development. The experimental survey results show that the proposed method is superior to the traditional method in terms of the accuracy of nanomaterial performance prediction and the optimization effect of the synthesis process, which verifies its effectiveness. Process (computing) ; Nanomaterials ; Relation (database) ; Work in process ; Process design ; Design process ; Yield (engineering) Machine Learning in Materials Science ; Advanced Technologies and Applied Computing ; Applied Advanced Technologies
Authors: Akio MORO | Ashish Sutar | Ani Abraham | Eric Mayhew | Kenneth Brezinsky | Patrick T. Lynch
Source:  (article, 2025-01-03)
Field: Chemistry — Organic Chemistry

Sensing Fuel Enthalpy of Vaporization From IR Spectra: Comparison of Model Approaches Jet fuels are inherently complex, and their physicochemical properties vary significantly, particularly with the integration of sustainable aviation fuels (SAFs). This variability poses challenges for using such fuels in compression ignition engines, especially as key properties, like derived cetane number (DCN) and enthalpy of vaporization (Hv), are not specified for jet fuels. The enthalpy of vaporization impacts ignition characteristics, spray behavior, and evaporative cooling, which in turn affects fuel efficiency, knock resistance, and the air-fuel ratio distribution in various engine types. Traditional methods for quantifying these properties can be time-consuming and require significant fuel quantities. However, recent advancements in chemometric models and machine learning have shown potential for correlating infrared spectra with fuel properties, offering a promising alternative to traditional methods. This study reviews methods for predicting the enthalpy of vaporization of pure components and mixtures, comparing direct spectral correlation, chemical functional group (CFG)-based predictions, and surrogate mixture generation using CFGs. Preliminary machine learning models achieve an R² of up to 0.82 when using actual UNIFAC CFGs, although errors increase when using predicted CFGs, emphasizing the need for improved prediction generalizability. These findings highlight the potential of spectroscopic approaches but underscore the need for expanded datasets and refined methodologies to enhance predictive accuracy and reliability. Vaporization ; Enthalpy ; Enthalpy of vaporization ; Spectral line ; Materials science ; Thermodynamics ; Physics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Phase Equilibria and Thermodynamics
Authors: Jie Li | Dai-Xin Yu | Meng-ting Yin | Ya Yang | Jingyi Liu | Hui Yan | Feng Liu | Jin‐Ao Duan | Sheng Guo
Source: Journal of Food Composition and Analysis (article, 2025-07-15)
Field: Chemistry — Analytical Chemistry

Quality assessment and prediction of Lycium ruthenicum Murr. from different geographical regions based on chroma–component–antioxidant analysis and FT-NIR Food composition data ; Lycium ; Food science ; Geography ; Biology ; Medicine Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Nikolaos Patsalidis | Mohsen Doust Mohammadi | Somnath Bhowmick | George Biskos | Vagelis Harmandaris
Source: ChemRxiv (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models We propose an active learning (AL) framework to develop force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, physics-aware potentials to achieve quantum-level accuracy for complex interfacial systems. Our approach trains physics-aware potentials, with incorporated flexibility and smoothness, on actively sampled Density Functional Theory (DFT) data to describe interactions between undercoordinated atomic silver (Ag) clusters and gaseous pollutants (CO$_2$, CO, SO$_2$), relevant for environmental applications like sensing. The AL process follows three stages: (1) FFs are trained using adaptable physics aware potentials of semi-empirical descriptors, optimized via a Pareto analysis scheme; (2) refined FFs generate candidate structures through Metropolis Hastings Monte Carlo (MHMC) or stochastic molecular dynamics (sMD); (3) a subset of candidates is selected for DFT labeling based on an outlier score (OS), which utilizes the existing data descriptor distributions, ensuring diverse PES exploration. This framework produces FFs capable of capturing cohesive, physisorption, and chemisorption interactions with accuracy comparable to \textit{ab initio} methods and advanced machine learning models, while retaining the efficiency of semi-empirical potentials. Our methodology is highly versatile, easily accommodating various choices of descriptors, model basis sets, and sampling techniques. Energy (signal processing) ; Physics ; Engineering physics ; Nanotechnology ; Chemical physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Dan Lin | Anqi Liu | Lei Pan | Fan Yang | Liang Yang | Changlong Jiang
Source: Microchemical Journal (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Aggregation-induced fluorescence switching of melamine-tailored carbon dots: Dual-recognition strategy coupled with deep learning for sensitive salbutamol assay Fluorescence ; Salbutamol ; Förster resonance energy transfer ; Analytical Chemistry (journal) ; Detection limit ; Fluorescence spectroscopy Carbon and Quantum Dots Applications ; Melamine detection and toxicity ; Pharmacological Effects and Assays
Authors: Xiaoying Sun
Source: Scientific Reports (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions Simulating the performance of optoelectronic materials under complex and variable environmental conditions in laboratory settings presents a significant challenge, as laboratory environments often fail to accurately replicate real-world conditions. This limitation hinders the reliability of performance assessments for optoelectronic materials in practical applications. To address this limitation, this research introduces EcoCurrentNet, an innovative model that integrates deep neural networks (DNN) with convolutional layers and residual blocks, combined with a CatBoost regression layer, to effectively capture spatial and nonlinear dependencies. The convolutional component learns the interactions between material features and 12 environmental variables, while residual blocks enhance training stability and gradient flow across deeper layers. A global average pooling and fully connected layer compress the learned features before they are passed to the CatBoost regressor, which iteratively refines the final output. The model is designed based on principles of thermodynamics and material science, particularly the complex relationships between material properties and external factors such as temperature, pressure, and light intensity, which can be described by physical laws governing material behavior. The model achieves a remarkable R<sup>2</sup> score of 99.68%, demonstrating its capability to provide accurate assessments of material behavior beyond controlled laboratory conditions. This hybrid architecture illustrates the potential of combining deep residual learning and gradient boosting for modeling complex physical systems, offering a more reliable and efficient approach to material design and paving the way for future innovations in the field. Residual ; Boosting (machine learning) ; Stability (learning theory) ; Pooling ; Convolutional neural network ; Nonlinear system ; Deep learning ; Reliability (semiconductor) Machine Learning in Materials Science ; Thermal Radiation and Cooling Technologies ; Thin-Film Transistor Technologies