jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cihat Güleryüz | Sajjad Hussain Sumrra | Abrar U. Hassan | Ayesha Mohyuddin | Sadaf Noreen | Ashraf Y. Elnaggar
Source: Materials Today Communications (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation Materials science ; Urea ; Crystal (programming language) ; Machine learning ; Propensity score matching ; Artificial intelligence ; Biological system ; Computer science ; Statistics ; Biology ; Biochemistry Crystallization and Solubility Studies ; Metabolomics and Mass Spectrometry Studies ; Enzyme Structure and Function
Authors: Xiao Zhang | Xitao Wang | Shunbo Hu
Source: Applied Sciences (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion This study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databases and initial models in traditional methods. By integrating E3NN with atomic cluster expansion (ACE) techniques, a dual-tower contrastive learning model has been developed, mapping crystal structures and XRD patterns to a continuous embedding space. The EACNN model retains hierarchical features of crystal systems through symmetry-sensitive encoding mechanisms and utilizes relationship mining via contrastive learning to replace rigid classification boundaries. This approach reveals gradual symmetry-breaking patterns between monoclinic and orthorhombic crystal systems in the latent space, effectively addressing the recognition challenges associated with low-symmetry systems and small sample space groups. Our investigation further explores the potential for model transfer to experimental data and multimodal extensions, laying the theoretical foundation for establishing a universal structure–property mapping relationship. Cluster (spacecraft) ; Cluster expansion ; Materials science ; Crystallography ; Computer science ; Physics ; Chemistry ; Thermodynamics ; Computer network X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Ruihuan Ge | Mona Faraji Niri | Adam M. Boyce | Robert Heymer | James Marco | Paul R. Shearing | Denis Cumming | Rachel M. Smith
Source: Chemical Engineering Journal (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Particle-scale design of lithium-ion battery cathodes for manufacture by coupling physics-based modelling with machine learning interpretation Battery (electricity) ; Electrode ; Calendering ; Process (computing) ; Workflow ; Coupling (piping) ; Cathode ; Artificial neural network Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Jiayi Zhang | Xue Jia | Hao Li | Fukui Xiao | Qiang Wang | Ning Zhao
Source: ACS Sustainable Chemistry & Engineering (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Application of an Explainable Machine Learning to CO<sub>2</sub> Methanation for Optimal Design Nickel-Based Catalysts The hydrogenation of carbon dioxide to produce clean fuels is one of the important means to achieve carbon neutrality, among which CO2 methanation has attracted much attention due to its thermodynamic advantages and environmental friendliness. However, the large-scale application of the technology still faces bottlenecks such as insufficient low-temperature activity of the catalyst and poor resistance to carbon deposition. The multiscale catalyst design method based on machine learning (ML) provides an innovative way to break through the limitations of traditional trial and error methods and achieve precise control of the energy conversion. In this work, reliable methods are first employed to remove the data with excessive outliers and missing values. Subsequently, a cross-validation method based on catalyst composition-stratified sampling is proposed to avoid overfitting. Finally, ensemble learning methods are utilized to construct ML models. The categorical boosting (CatBoost) model demonstrates superior performance with R2 values of 0.77 and 0.75 for CO2 conversion and CH4 selectivity, respectively. The results show that descriptors based on the catalyst performance and reaction conditions have a significant effect on the prediction of CO2 conversion and CH4 selectivity. By analyzing the most influential descriptors, the optimal reaction conditions were determined as T of 250–350 °C, gas hourly space velocity below 15,000 cm3 g–1 h–1, BET specific surface area between 50 and 200 m2 g–1, and CNi of more than 5%. This study provides new perspectives and methods for the development and optimization of CO2 methanation catalysts. Methanation ; Nickel ; Catalysis ; Process engineering ; Computer science ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Octavio Rosales-Martínez | E. E. Granda-Gutiérrez | René Arnulfo García-Hernández | R. Alejo | A. A. Flores-Fuentes
Source: Modelling—International Open Access Journal of Modelling in Engineering Science (article, 2025-09-29)
Field: Chemistry — Analytical Chemistry

Spectral Derivatives Improve FTIR-Based Machine Learning Classification of Plastic Polymers Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 cm−1. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Preprocessor ; Pattern recognition (psychology) ; Multilayer perceptron ; Linear discriminant analysis ; Second derivative ; Random forest ; Polyvinyl chloride ; Test set Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Faguo Hou | Hong‐Hui Wu | Dexin Zhu | Jinyong Zhang | Liudong Hou | Shuize Wang | Guilin Wu | Junheng Gao | Jing Ma | Xinping Mao
Source: npj Materials Degradation (article, 2025-02-19)
Field: Materials Science — Metals and Alloys

Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning Austenitic stainless steel is renowned for its exceptional corrosion and mechanical properties, yet it remains susceptible to localized corrosion, such as pitting in the presence of aggressive ions or/and extreme environmental conditions. Critical pitting temperature (CPT) serves as a key metric for evaluating the susceptibility to pitting corrosion, and its accurate prediction is essential for engineering pitting-resistant alloys. In this work, through optimized feature selection processes, three critical features − standard reduction potential ( $${E}_{\max }$$ ), valence electron ( $${{VEC}}_{{sum}}$$ ), and geometric parameters ( $$\lambda$$ ) − are identified as crucial descriptors for CPT. Utilizing interpretable machine learning techniques, a predictive model for CPT is developed and confirmed via cross-validation, demonstrating superior predictive accuracy. This work not only deepens our understanding of the underlying factors affecting pitting but also facilitates the development of austenitic stainless steel with improved resistance to pitting corrosion. Austenite ; Metallurgy ; Materials science ; Pitting corrosion ; Austenitic stainless steel ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Shi-min Ai | Dezhen Fang | Yang Guo | Jie Ye | Xiaoping Lin
Source: Journal of Alloys and Compounds (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of microstructure-mechanical property relationships in Mg-Al alloys Microstructure ; Materials science ; Metallurgy ; Property (philosophy) ; Philosophy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Shiyun Dong | Chunhui Fan | Hong Luo | Hongxu Cheng | Xuefei Wang
Source: Electrochimica Acta (article, 2025-06-09)
Field: Materials Science — Metals and Alloys

Machine learning driven multi-objective optimization and interpretability for corrosion-resistant multi-principal element alloys Interpretability ; Corrosion ; Element (criminal law) ; Principal (computer security) ; Materials science ; Computer science ; Principal component analysis ; Artificial intelligence ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies ; Welding Techniques and Residual Stresses
Authors: Fabian Weber
Source: ChemRxiv (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

ArchOnML; an open-source python package for machine-learning assisted molecular design The python package ArchOnML ("Archive-On-Machine-Learning") is introduced, which can perform virtual screening projects covering up to millions of structural derivatives through the use of Kernel Ridge Regression models. It supports the full workflow of setting up calculation inputs for external quantum chemistry program packages and post-processesing their outputs for training and predictions. An example screening project for over 1.3 million anthraquinone derivatives is presented, where excitation energies and oscillator strengths of the first two excited singlet and triplet states are predicted from descriptors based on semi-empirical quantum chemistry results. Compared to a non-ML calculation protocol, ArchOnML achieves a speed-up factor of over 400 with mean absolute errors for excitation energies of 0.1 eV. Due to ArchOnML's modular application programming interface, new descriptors, models and interfaces to other external quantum chemistry programs can be added in a straightforward way. Python (programming language) ; Open source ; Computer science ; Programming language ; Software engineering ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jaemin Wang | Shengda Jin | Young‐Kook Lee | Jae-Hyeok Shim | Byeong‐Joo Lee
Source: Acta Materialia (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Discovering elemental effects on hydrogen storage in TiMn2-type alloys using explainable machine learning Materials science ; Hydrogen storage ; Type (biology) ; Hydrogen ; Metallurgy ; Alloy Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: FengYing Guo | DianZheng Zhuang
Source: Food Bioscience (article, 2025-01-05)
Field: Chemistry — Analytical Chemistry

Real-time monitoring and prediction of glucose content in enzymatic reactions: Application of a deep neural network mode Artificial neural network ; Content (measure theory) ; Mode (computer interface) ; Chemistry ; Computer science ; Enzyme ; Artificial intelligence ; Biological system ; Biochemistry ; Mathematics ; Biology ; Operating system Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Machine Learning in Bioinformatics
Authors: Ali Kassab | Pravansu Mohanty | Zhen Hu | Georges Ayoub
Source: Computational Materials Science (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Graph neural network-driven surrogate modeling for accelerated parameter identification in semicrystalline polymers Crystallinity ; Artificial neural network ; Identification (biology) ; Surrogate model ; Biological system ; Computer science ; Materials science ; Graph ; Polymer ; Artificial intelligence ; Machine learning ; Composite material ; Theoretical computer science ; Biology Machine Learning in Materials Science ; Polymer crystallization and properties ; Injection Molding Process and Properties
Authors: Lihong Lin | Jin‐Jin Li | Yun‐Xiang Pan | Fangyou Yan | Zheng‐Hong Luo | Yin‐Ning Zhou
Source: ACS Applied Materials & Interfaces (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Accelerating Polyester Intelligence: Machine-Learning-Assisted Prediction of Glass Transition Temperature and Virtual Molecules Screening Rapid development of the economy and society has resulted in a need for polyesters that are tailored to diverse performance requirements. Unfortunately, the innovation of polyester materials is mainly dependent on experience and intuitive guidance. Herein, we propose various interpretable quantitative-structure-property relationship (QSPR) models based on machine-learning-assisted approaches, which can accurately predict polyesters' glass transition temperatures (<i>T</i><sub>g</sub>) and facilitate the exploration of novel polyesters. Initially, 695 polyesters with <i>T</i><sub>g</sub> values are collected to establish multiple QSPR models using three different algorithms, which undergo both internal and external validation. The relative coefficient (<i>R</i><sup>2</sup>) values of the best deep neural network (DNN) model on the training set and testing set reach 0.9588 and 0.9314, respectively, which is among the better levels in related studies. The use of Morgan fingerprint with frequency (MFF) descriptors and associated Shapley Additive Explanations analysis does reveal a couple of interesting physical trends associated with variation of <i>T</i><sub>g</sub> with the substructure beyond what was reported before. To better widen the chemical space of the existing polyester material family, a virtual polyester library is constructed using a retrosynthetic strategy. Furthermore, this workflow identifies 20 novel polyesters with low synthetic complexity by high-throughput screening and validates these polyesters through molecular dynamics simulations, which show an average absolute error of 9.42 °C between the model-predicted and MD-simulated values. Machine-learning-assisted approach not only improves the efficiency of polyester material discovery but also provides a promising perspective for understanding the thermal properties of polyesters from a microscopic chemical structural viewpoint. Polyester ; Glass transition ; Set (abstract data type) ; Workflow ; Chemical space ; Substructure ; Molecule ; Characterization (materials science) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Thijs ter Rele | Gerardo Campos-Villalobos | René van Roij | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2025-10-22)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning many-body potentials for charged colloids in primitive 1:1 electrolytes Effective interactions between charged particles dispersed in an electrolyte are most commonly modeled using the Derjaguin-Landau-Verwey-Overbeek potential, where the ions in the suspension are coarse-grained out at mean-field level. However, several experiments point to shortcomings of this theory, as the distribution of ions surrounding colloids is governed by nontrivial correlations in regimes of strong Coulomb coupling (e.g., low temperature, low dielectric constant, high ion valency, and high surface charge). Insight can be gained by explicitly including the ions in simulations of these colloidal suspensions, even though direct simulations of dispersions of highly charged spheres are computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to generate density-dependent ML potentials that accurately describe the effective colloid interactions at given system parameters. These ML potentials enable fast simulations and make large-scale simulations of charged colloids in suspension possible, opening the possibility for a systematic study of their phase behavior, in particular gas-liquid and fluid-solid coexistence. Colloid ; Electrolyte ; Suspension (topology) ; Ion ; Coulomb ; Coupling (piping) ; Charged particle ; SPHERES Electrostatics and Colloid Interactions ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Xuan Zhang
Source: Analytica Chimica Acta (article, 2025-06-11)
Field: Chemistry — Inorganic Chemistry

Separation of formaldehyde, N2, and O2 in MOFs: Crystal graph convolutional neural network, machine learning, and experimental Chemistry ; Convolutional neural network ; Formaldehyde ; Graph ; Crystal (programming language) ; Artificial neural network ; Artificial intelligence ; Organic chemistry ; Theoretical computer science Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Rizvi Syed Aal E Ali | Jiaolong Meng | Xuefeng Jiang
Source: Chemistry - An Asian Journal (review, 2025-08-28)
Field: Materials Science — Materials Chemistry

Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis The integration of machine learning (ML) and high-throughput experimentation (HTE) is rapidly transforming research practices in synthetic chemistry. Traditional trial-and-error methods, historically slow and labour-intensive, are being replaced by automated, predictive workflows that significantly accelerate the optimization of chemical reactions. This review highlights the foundational principles and recent advancements in ML and HTE, while emphasizing automation, parallelization, and miniaturization across different systems and their adaptation in autonomous laboratories. Case studies illustrate successful application of ML and HTE in synthetic chemistry, underscoring the enhanced efficiency, and precision through this synergy. The review concludes by addressing current challenges and future directions, outlining how ongoing developments in automation, robotics, and AI/ML-driven experimentation will shape the future landscape of chemistry research. Throughput ; Computer science ; Artificial intelligence ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: A. Hente | Behrouz Arash | Maximilian Jux | Raimund Rolfes
Source: Materials Today Communications (article, 2025-07-17)
Field: Materials Science — Polymers and Plastics

Enhancement of fracture properties of amorphous polymers by nanoparticles: A machine-learning assisted coarse-grained model Polymer nanocomposites, formed by incorporating nanoparticles into epoxy matrices, exhibit exceptional thermo-mechanical and fracture properties, making them ideal for advanced engineering applications. This study explores the enhancement of fracture properties of epoxies by nanoparticles and develops a coarse-grained (CG) model to enable this investigation. We present a novel artificial neural network (ANN)-assisted optimization framework to calibrate CG molecular simulation models. The algorithm integrates particle swarm optimization with ANN predictions, where ANN accelerates parameter optimization by minimizing errors between CG simulation results and all-atom reference data. This process significantly reduces computational cost while ensuring accurate predictions of critical properties, such as yield stress and elastic modulus, over a wide temperature range, demonstrating excellent temperature transferability of the model. Large-scale CG simulations facilitated the analysis of nanoparticle agglomeration effects on fracture behavior, a challenge infeasible for all-atom simulations. Simulation outcomes were qualitatively compared with experimental findings, offering valuable insights into the influence of nanoparticle distribution on fracture properties. This integrated approach provides a robust pathway for designing and optimizing polymer nanocomposites for real-world applications. Materials science ; Amorphous solid ; Nanoparticle ; Polymer ; Fracture (geology) ; Nanotechnology ; Composite material ; Crystallography Polymer crystallization and properties ; Injection Molding Process and Properties ; Material Dynamics and Properties