jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng Ma (351144) | HanLe Lin (22521422) | Changfei Huang (10672774) | Chengcheng Tian (1922686) | Yayun Zhang (3800788)
Source: Figshare (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Low Temperature Pyrolysis of Plastics Based on SmallSample Machine Learning: Product Prediction, Optimization Conditions,and Model Interpretability Analysis This study proposes a novel data-driven strategy that integrates machine learning (ML) with low-temperature catalytic pyrolysis to optimize the selection of plastic feedstocks and catalyst designs. Using a custom-built experimental data set based on polyethylene (PE) model compounds and zeolite ZSM-5 catalysts, we trained and evaluated three ML modelsTabPFN, CatBoost, and XGBooston a small-sample data set of 105 orthogonally designed experiments. TabPFN outperformed conventional gradient boosting models in both classification and regression tasks, achieving an <i>R</i><sup>2</sup> of 0.982 and RMSE of 4.37 on test data, with strong generalization capacity. SHAP analysis and Pearson correlation jointly revealed that temperature and the Si/Al ratio were the dominant factors influencing C<sub>2</sub>–C<sub>6</sub> olefins selectivity, with the latter exhibiting an inverted U-shaped influence on olefin selectivity. The trained TabPFN model successfully identified optimal reaction conditions, which were experimentally validated with a high C<sub>2</sub>–C<sub>6</sub> olefin yield of 82.5% and minimal coke formation. This work demonstrates the potential of ML-based small-sample frameworks for rapidly optimizing catalytic systems, and the analysis of SHAP interpretation provides possible insights into the reaction pathway. Interpretability ; Olefin fiber ; Yield (engineering) ; Pyrolysis ; Boosting (machine learning) ; Test set ; Feature selection ; Generalization ; Set (abstract data type) ; Polyethylene Machine Learning in Materials Science ; Thermochemical Biomass Conversion Processes ; Catalysis and Oxidation Reactions
Authors: Deqing Xue | Qian Zuo | Zhiyong Zhang | Zhao Shang | Bueryi Shen | Ruihao Yuan
Source: Journal of Materials Science (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Active learning-assisted search for thermal storage used TiNi shape memory alloys Materials science ; Shape-memory alloy ; Solid mechanics ; Metallurgy ; Thermal ; Composite material ; Thermodynamics Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Suchona Akter | Mohammad R. Momeni
Source: The Journal of Physical Chemistry C (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Mechanism of Charge Transport in Mixed-Valence 2D Layered Hybrid Organic–Inorganic Bronze Materials Two-dimensional layered hybrid organic–inorganic bronze (HOIB) materials are a new class of mixed-valence hybrid metal-oxides that demonstrate great potential as advanced functional materials for next-generation electronics. Recently, new hybrid vanadium bronze materials, (EV)V8O20 and (MV)V8O20, EV = ethyl viologen and MV = methyl viologen, have been introduced, with EV having ≈3 orders of magnitude higher electrical conductivity than the MV system. Given their stoichiometrically similar inorganic V–O layers and close reduction potentials, the observed significant difference in electrical conductivities is puzzling. Here, through accurate first-principles electronic structure calculations coupled with MACE machine learning molecular dynamics (MD) simulations validated by accurate ab initio MD data, we provide mechanistic molecular-level insights into dominant charge transport and electrical conductivity pathways in these materials. Our detailed structural and electronic properties data identify factors contributing to this significant difference in the electrical conductivity of these materials. Our findings in this work offer clues and provide valuable insights into improving the electrical conductivity of hybrid bronze and similar materials, suggesting new ways to guide the design of next-generation materials with enhanced properties for electronic and energy conversion applications. Bronze ; Valence (chemistry) ; Materials science ; Mechanism (biology) ; Chemical physics ; Charge (physics) ; Chemical engineering ; Chemistry ; Metallurgy ; Physics ; Organic chemistry Graphene research and applications ; Advanced Memory and Neural Computing ; Advancements in Battery Materials
Authors: Julian
A. Hueffel (21743274) | Mathilde Rigoulet (4092445) | Sebastian Wellig (16504745) | Theresa Sperger (3228054) | Jas S. Ward (1502479) | Kari Rissanen (1282326) | Franziska Schoenebeck (1266411)
Source: Figshare (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Discovery of Ni<sup>(I)</sup> Complexes for CO<sub>2</sub> Insertion Enabled by a MachineLearning-Computational-SelectionSequence The fate of a catalyst or catalytic intermediate, i.e., its speciation, <i>in situ</i> is a critical aspect of the efficiency of a catalyst as well as the overall reactivity and selectivity of the catalyzed transformation. However, the precise factors that dictate catalyst speciation are rarely understood and trial-and-error approaches frequently prevail. To address this challenge and develop predictive tools to guide ligand selection for a desired metal speciation in a catalytically relevant context, we evaluated the feasibility of machine learning combined with computational activation barrier predictions to achieve CO<sub>2</sub> insertion at room temperature for the sterically least hindered (and most vulnerable) Ni<sup>(I)</sup>–Ph complexes, which constitute key catalytic intermediates. Following an in depth computational rationalization on the origin of reactivity difference of Ni<sup>(I)</sup> versus Ni<sup>(II)</sup> toward CO<sub>2</sub> insertion, we subsequently pursued machine learning to identify ligands that favor the critical Ni<sup>(I)</sup> oxidation state. To this end, a descriptor database was constructed <i>in silico</i>. Subsequent application of machine learning led to the prediction of numerous ligands that favor the more reactive Ni<sup>(I)</sup>–Ph intermediate and oxidation state, which were subsequently filtered for candidates that also show desired room temperature reactivity through the calculation of activation barriers. Ultimately, a set of representative candidates was synthesized and experimentally tested for CO<sub>2</sub> insertion, confirming their reactivity and alignment with computational predictions. This work offers a blueprint for creating and analyzing virtual databases to predict ligands–including never synthesized ones–that control metal complex oxidation state, nuclearity, and reactivity. Reactivity (psychology) ; Catalysis ; Steric effects ; Blueprint ; Rationalization (economics) ; Ligand (biochemistry) ; Computational model Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Carbon dioxide utilization in catalysis
Authors: Tian‐Li Pu | Jingxiang Zhao | Xuan Zhao | Qinglang Ma | Li Yang | Qian‐You Wang
Source: Coordination Chemistry Reviews (article, 2025-10-26)
Field: Chemistry — Organic Chemistry

Design of metal-coordinated hypergolic materials: recent advances and perspectives Propellant ; Propulsion ; Flexibility (engineering) ; Field (mathematics) Fullerene Chemistry and Applications ; Energetic Materials and Combustion ; Supramolecular Chemistry and Complexes
Authors: Yonghui Gao (102566) | Limin Shang (412524) | Jing Liu (38537) | Zhiling Zhu (3116931)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Machine LearningAccelerated Discovery of AntimicrobialInorganic Nanomaterials The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials present significant challenges in identifying new agents that are both efficient and cost-effective with broad-spectrum activity. In response, this study applied machine learning for the first time to discover antimicrobial inorganic nanomaterials. Information on over 2,000 antimicrobial nanomaterials was extracted from more than 8,000 papers. An unsupervised machine learning analysis was conducted to assess data distribution and explore the relationships between material features and antimicrobial activity in high-dimensional space. A series of machine learning models were trained. Through the evaluation of six performance metrics, five key features were identified from 27 dimensions. To further quantify the structure–activity relationships, a genetic programming-symbolic classification model was employed to generate a precise mathematical formula with a prediction accuracy of 0.83. Using this formula, 43 new antimicrobial inorganic nanomaterials were predicted. Of these, four nanomaterials were synthesized and their antibacterial properties were experimentally validated. This work not only provides a next generation approach for designing antimicrobial inorganic nanomaterials but also opens new avenues for applying machine learning in materials science. Antimicrobial ; Nanomaterials ; Unsupervised learning ; Antimicrobial peptides Machine Learning in Materials Science ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Jamie Holber | Krishna Garikipati
Source: arXiv (Cornell University) (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Equivariant graph neural network surrogates for predicting the properties of relaxed atomic configurations Density functional theory (DFT) calculations determine the relaxed atomic positions and lattice parameters that minimize the formation energy of a structure. We present an equivariant graph neural network (EGNN) model to predict the outcome of DFT calculations for structures of interest. Cluster expansions are a well established approach for representing the formation energies. However, traditional cluster expansions are limited in their ability to handle variations from a fixed lattice, including interstitial atoms, amorphous materials, and materials with multiple structures. EGNNs offer a more flexible framework that inherently respects the symmetry of the system without being reliant on a particular lattice. In this work, we present the mathematical framework and the results of training for lithium cobalt oxide (LCO) at various compositions of lithium and arrangements of the lithium atoms. Our results demonstrate that the EGNN can accurately predict quantities outside the training set including the largest atomic displacements, the strain tensor and energy, and the formation energy providing greater insight into the system being studied without the need for more DFT calculations. Equivariant map ; Density functional theory ; Artificial neural network ; Tensor (intrinsic definition) ; Cluster expansion ; Cluster (spacecraft) ; Topology (electrical circuits) ; Lattice (music) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advancements in Battery Materials
Authors: Norhashila Hashim | Maimunah Mohd Ali
Source: Journal of Food Science (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

A Comparative Analysis on the Classification of Pineapple Varieties Using Thermal Imaging Coupled With Transfer Learning Advanced intelligent systems are becoming a significant trend, especially in the classification of tropical fruits due to their unique flavor and taste. As one of the most popular tropical fruits worldwide, pineapple (Ananas comosus) has a great chemical composition and is high in nutritional value. A non-destructive method for the determination of pineapple varieties was developed, which utilized thermal imaging and deep learning techniques. This study presents a comparative analysis of three deep learning models, including ResNet, VGG16, and InceptionV3, for the rapid classification of pineapple varieties using thermal imaging and transfer learning. The dataset comprises 3240 thermal images from three different pineapple varieties, including Moris, Josapine, and N36, under controlled temperature conditions (5°C, 10°C, and 25°C), resulting in a total of three classification classes. All convolutional neural network (CNN) architectures were fine-tuned, and data augmentation techniques were applied to improve model generalization. The efficiency of hyperparameters was evaluated to improve the model accuracy, whereas the data augmentation was carried out to avoid model overfitting. The highest classification accuracy of 99 % was achieved via InceptionV3. The precision, recall, and F1-score demonstrate promising results with the values higher than 0.85 for all pineapple varieties. This approach demonstrated that transfer learning with CNNs is significantly promising as a feature extraction method for the determination of physicochemical properties in pineapple fruit. An ablation study confirmed the added benefit of using both data augmentation and transfer learning. While model architecture innovation was not the primary goal, this work contributes by benchmarking established CNN models for agricultural thermal imaging applications. Convolutional neural network ; Transfer of learning ; Hyperparameter ; Pattern recognition (psychology) ; Deep learning ; Feature extraction ; Benchmarking ; Artificial neural network Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI
Authors: Taeyong Jo | JunTaek Oh | Jaehyeon Son | C.K. Yoon | Eun-Soo Hwang | Jinwoo Ahn | Jae‐Won Lee | Jin‐Soo Lee | Ji-Yong Shin | Donggun Lee | Seunga Lim | Jeongho Ahn | Younghoon Sohn | Myungjun Lee
Source: Research Square (preprint, 2025-01-30)
Field: Materials Science — Surfaces, Coatings and Films

Ultra-Wide-Field Imaging Mueller Matrix Spectroscopic Ellipsometry for Semiconductor Metrology Metrology ; Ellipsometry ; Mueller calculus ; Optics ; Semiconductor ; Matrix (chemical analysis) ; Materials science ; Field (mathematics) ; Optoelectronics ; Nanotechnology ; Physics ; Polarimetry ; Thin film ; Mathematics Optical Coatings and Gratings ; Optical Polarization and Ellipsometry ; Near-Field Optical Microscopy
Authors: Jaiming J. K. Chung | Bienfait Kabuyaya Isamura | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

CHQuant: A Protocol for Quantifying Conformational Sampling with Convex Hulls Conformational sampling is a key component of the workflow behind any machine learning force field. Existing methods for assessing the coverage of a conformational space can be ambiguous, reference-dependent, and qualitative due to their reliance on visual perception. In this work, we propose CHQuant, a protocol designed to <i>quantify</i> the extent of conformational space coverage. This approach is based on the observation that the movement of atoms defines point clouds that can be characterized by the volumes of the corresponding convex hulls. By applying our method to several small molecules and conformational sampling methods, we show that convex hull volumes behave according to chemical intuition at an atomic, functional group, and molecular level. Therefore, convex hull volumes are useful measures that can be used to compare different sampling methods or data sets. Among many applications, the CHQuant approach monitors the sampling achieved by any molecular dynamics sampler and compares existing conformational databases of the same system. Convex hull ; Sampling (signal processing) ; Intuition ; Regular polygon ; Workflow ; Protocol (science) ; Molecular dynamics ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Andrew S. Lee | V. Sampath | Samuel David Miller | Noah H. Paulson | Sunilkumar Khandavalli | Chance Parrish | Scott A Mauger | Sarah A. Berlinger | Kenneth Higa | Yuepeng Zhang
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Fuel Cell Catalyst Ink Formulation Fuel cell electrode catalyst layers are typically manufactured using colloidal ink casting and coating techniques such as slot-die coating or spray coating. While ink deposition and drying require careful optimization of their own, a crucial prerequisite is tuning the catalyst ink to have appropriate rheological and microstructural properties like viscosity, particle/cluster dispersion, and aggregate size and size distribution. Despite studies that have elucidated mechanisms for fine-tuning ink properties for subsequent coating and drying process control, formulating brand new inks with specific properties still requires slow trial-and-error studies. The difficulty of discovering new ink formulations lies within three main challenges: 1) fuel cell catalyst inks have many components (ionomer, catalyst, catalyst support, solvent), leading to a large formulation design space; 2) ink properties are determined by complex multi-scale physics that simultaneously involve almost all components; and 3) ink property optimization is multi-objective in nature, requiring one to make calculated property trade-offs. This work leverages uncertainty-aware machine learning (ML) models to correlate carbon-based fuel cell ink formulation parameters with experimentally measured ink properties. Statistical sampling is first used to define a design of experiments for an initial batch of experiments. This initial dataset is used to begin a multi-objective Bayesian Optimization campaign that designs new experiments to improve ML accuracy and obtain target ink properties. After iterations of experiment-ML development, we report our refined ML models with the elucidated ink material-property correlations. Inkwell ; Coating ; Process (computing) ; Design of experiments ; Catalysis ; Casting ; Rheology ; Bayesian optimization Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Fuel Cells and Related Materials
Authors: Yue Dong | Yingjie Zhao | Yuyuan Zhao
Source: Materials Today Communications (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Quantitative study on microstructure characteristic parameters of TC18 titanium alloy by high-throughput mapping technology Materials science ; Microstructure ; Titanium alloy ; Throughput ; Alloy ; Metallurgy ; Computer science Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Rohan Batra (21566466) | Yogesh M. Joshi (1636813) | Sachin Shanbhag (2414560)
Source: Figshare (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

NavigatingSmall Datasets with Machine Learning: GaussianProcess Modeling for Colloidal Gelation Small datasets, which are common in many traditional science and engineering disciplines, present a challenge for powerful machine learning methods that are better suited for large datasets. Through the example of spontaneous colloidal sol–gel transition, we present a data-driven framework based on Gaussian Processes (GPs) for modeling and inference with limited data. GPs are well-suited for small datasets and automatically provide uncertainty quantification for model predictions. We explore GPs with two different structures (single and multi-output) to map synthesis parameters to gelation characteristics. While both GP structures quantitatively capture the relationships between the input and output variables, we favor multioutput GP, which attempts to simultaneously learn correlations between different outputs, due to its superior uncertainty calibration. To address inverse problems, we introduce a Bayesian framework that embeds a GP model into a Markov Chain Monte Carlo sampling algorithm. It enables the discovery of multiple synthesis pathways for achieving desired material properties. Therefore, this work presents a robust methodology for data-constrained modeling, with broad applicability to material design in general and the colloidal domain in particular. Gaussian process ; Markov chain Monte Carlo ; Bayesian inference ; Inference ; Process (computing) ; Bayesian probability ; Sampling (signal processing) ; Monte Carlo method ; Uncertainty quantification ; Mixture model Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Material Dynamics and Properties
Authors: Yang Xu | Zhenhua Chai Zhenhua Chai | Fang Shan | Xuhui Meng
Source: Advances in Applied Mathematics and Mechanics (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Machine Learning Based Optimization of Tube Geometry for Capillary Rise The phenomenon of capillary rise is commonly observed in both nature and engineering fields. There has been growing interest in the study of the geometry effect on the capillary rising. In this work, we investigate the effect of tube geometry on the capillary rise, and explore the optimal tube geometry that corresponds to the maximum equilibrium height. To this end, we present two machine learning approaches that are able to optimize the tube shape to obtain maximum equilibrium height given a material. Specifically, the first one is based on the Gaussian process regression (GPR) in a purely data-driven manner, and the second one utilizes deep neural networks (DNNs) which are capable of encoding the physical constraint, e.g., the governing equation for the capillary rise in a tube. We apply both methods to study the capillary rise in the uniform cylindrical, trigonometric-shaped and quadratic-shaped tubes, and then determine the optimal geometries that achieve the maximum equilibrium heights. Our results demonstrate that the both approaches can effectively optimize complex capillary channels corresponding to the equilibrium height. Further, GPR is computationally more efficient than DNNs, while the DNNs with physical constraints are more promising for solving problems in which the tube geometry is parameterized at high dimensions. Capillary action ; Parameterized complexity ; Tube (container) ; Process (computing) ; Complex geometry ; Gaussian ; Artificial neural network Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Hydrogels: synthesis, properties, applications