jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chaorui Duan | Dazhong Wu
Source: arXiv (Cornell University) (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Predicting Stress-strain Behaviors of Additively Manufactured Materials via Loss-based and Activation-based Physics-informed Machine Learning Predicting the stress-strain behaviors of additively manufactured materials is crucial for part qualification in additive manufacturing (AM). Conventional physics-based constitutive models often oversimplify material properties, while data-driven machine learning (ML) models often lack physical consistency and interpretability. To address these issues, we propose a physics-informed machine learning (PIML) framework to improve the predictive performance and physical consistency for predicting the stress-strain curves of additively manufactured polymers and metals. A polynomial regression model is used to predict the yield point from AM process parameters, then stress-strain curves are segmented into elastic and plastic regions. Two long short-term memory (LSTM) models are trained to predict two regions separately. For the elastic region, Hooke's law is embedded into the LSTM model for both polymer and metal. For the plastic region, Voce hardening law and Hollomon's law are embedded into the LSTM model for polymer and metal, respectively. The loss-based and activation-based PIML architectures are developed by embedding the physical laws into the loss and activation functions, respectively. The performance of the two PIML architectures are compared with two LSTM-based ML models, three additional ML models, and a physics-based constitutive model. These models are built on experimental data collected from two additively manufactured polymers (i.e., Nylon and carbon fiber-acrylonitrile butadiene styrene) and two additively manufactured metals (i.e., AlSi10Mg and Ti6Al4V). Experimental results demonstrate that two PIML architectures consistently outperform the other models. The segmental predictive model with activation-based PIML architecture achieves the lowest MAPE of 10.46+/-0.81% and the highest R^2 of 0.82+/-0.05 arocss four datasets. Consistency (knowledge bases) ; Hardening (computing) ; Universal testing machine ; Strain hardening exponent ; Polymer ; Embedding ; Constitutive equation ; Polynomial ; Point (geometry) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Juan V. Alegre-Requena
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Raw Data for "Cost-Effective Quantum Mechanical Workflows for Molecular Machine Learning" Raw Data for "Cost-Effective Quantum Mechanical Workflows for Molecular Machine Learning" Workflow ; Quantum ; Raw data ; Work (physics) ; Quantum computer ; Raw material Machine Learning in Materials Science ; Scientific Computing and Data Management ; Mechanical and Optical Resonators
Authors: Nicolas Schmid | Marc Wanner | Giulia Fischetti | Andreas Henrici | Mohsen Meshkian | Simon Bruderer | Rudolf M Füchslin | Bjoern Heitmann | Jan Dirk Wegner | Roland K O Sigel | Dirk Wilhelm
Source: ChemRxiv (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra Accurate interpretation of one-dimensional proton nuclear magnetic resonance (1H NMR) spectra remains a rate-limiting step in molecular structure elucidation, particularly when severe signal overlap, strong spin coupling, and instrumental distortions mask key features. Existing automated approaches depend on computationally intensive and sensitive iterative quantum-mechanical fitting and still require expert oversight. Here we introduce MolDeTr, a chemistry-informed deep-learning framework derived from the detection-transformer architecture that unifies peak picking, multiplet identification, and extraction of chemical shifts, scalar coupling constants, relaxation-dependent decay times, and proton counts in a single-network pass. The method targets prototypical spin systems of single-component small molecules in 1D 1H NMR, with up to ten distinct groups of chemically equivalent spins (multiplets). MolDeTr is trained exclusively on synthetic spectra generated by spin-dynamics simulations and augmented with realistic experimental artefacts, enabling it to generalize to unseen compounds—including experimental spectra with crowded regions and strongly coupled multiplets—without reference standards or prior spin-system knowledge. Unlike structure-conditioned shift-prediction or calculation models, e.g., density functional theory (DFT), that assume the molecular structure is known, MolDeTr addresses the spectrum-conditioned inverse problem and extracts spin-system parameters directly from measured 1D 1H NMR spectra, thereby substantially improving chemical-shift prediction precision by one to two orders of magnitude compared to existing structure-conditioned approaches. Benchmarking against challenging experimental 1H NMR spectra of small molecules shows median absolute errors of 0.89 Hz for chemical shifts and 0.20 Hz for coupling constants, while absolute proton counts are predicted with 93.5% accuracy, outperforming state-of-the-art spectrum analysis software and experienced spectroscopists. Owing to its physics-guided, modality-agnostic design, MolDeTr can be extended to other spectroscopic and diffraction techniques. By eliminating iterative fitting and expert intervention, MolDeTr offers a scalable route to fully automated spectral analysis, accelerating molecular discovery across the chemical sciences. Spins ; Spectral line ; Multiplet ; Proton ; Chemical shift ; Spin (aerodynamics) ; Scalar (mathematics) ; NMR spectra database ; Coupling (piping) ; Undersampling Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Linfan Sun | Haoyu Zhang | Zhiduo Liu | Jun Cheng | Ge Zhou | Ximin Zang | Lijia Chen | Peter K. Liaw
Source: Figshare (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Physical-information machine learning for strength and ductility prediction of metastable <i>β</i> titanium alloys Machine learning for predicting metastable β titanium alloy properties suffers from limited generalisation, unreliable extrapolation beyond data boundaries, and inadequate modelling of nonlinear effects of process parameters. This study proposes a physics-informed machine learning approach incorporating intrinsic physical attributes and phase transformation kinetics. Using 496 samples, it achieves R<sup>2</sup> = 0.95 for ultimate tensile strength (UTS) and 0.90 for elongation (El), accurately predicting out-of-boundary alloys with similar heat treatment responses within 5.0% UTS and 2.5% El error. This approach reduces reliance on data completeness and complex algorithms, providing an accurate, generalisable path to UTS- and El-targeted titanium alloy design. Titanium alloy ; Extrapolation ; Ultimate tensile strength ; Ductility (Earth science) ; Titanium ; Metastability ; Alloy ; Elongation Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Zhao-Gang Ding | Fang Liu | Dan-Dan Xu | Ruoyu Li | Xing Chen
Source: Journal of Water Process Engineering (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Machine learning-assisted design of ozone catalyst: TiO₂/AC -based nanocomposites for BAA degradation as an example Nanocomposite ; Ozone ; Degradation (telecommunications) ; Nanoparticle Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced oxidation water treatment
Authors: Dayalan Gunasegaram | Najmeh Samadiani | Nathan G. March | Indrajeet Katti | David Howard | Mark Easton
Source: Metals (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Accelerating and Improving the Accuracy of Parameter Calibration in a Phenomenological Crystal Plasticity Model Through High-Volume Machine Learning Simulations Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations and the labour-intensive nature of parameter calibration, challenges that are amplified in additively manufactured materials with location-dependent properties. To address these obstacles, we first developed deep neural network (DNN) surrogate models of physics simulations to predict the stress–strain response of an additively manufactured AlSi10Mg alloy. Twenty-five experimentally derived scenarios (five microstructures × five sets of grain orientations) were used for training 25 separate DNNs, with datasets for validated material behaviour generated using the Düsseldorf Advanced Material Simulation Kit (DAMASK) platform and a Fast Fourier Transform (FFT)-based solver. Once trained, the DNNs produced stress–strain curves almost instantaneously, enabling an exhaustive grid-search exploration of a vast parameter space. Our approach yielded significant efficiency gains, which were comprehensively quantified. The best-fit CP parameters obtained through this approach are expected to be more accurate than those derived from conventional trial-and-error calibration, which is restricted to a limited number of candidate values. In addition, the minimum number of CP-FFT simulations required to train the DNNs with sufficient accuracy was identified, reducing the need for costly physics simulations in future studies. The proposed framework enhances the practical utility of CP models for simulation-informed materials engineering and optimisation and is broadly applicable to parameter identification in phenomenological models of other domains. Calibration ; Artificial neural network ; Identification (biology) ; Uncertainty quantification ; Estimation theory ; Phenomenological model ; Experimental data ; Fourier transform Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Additive Manufacturing Materials and Processes
Authors: Zihao Wang | Yinan Chai | Jingwen Gong | Wenbin Xie | Yidong Chen | Wei Gong
Source: Polymers (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

A Gas Production Classification Method for Cable Insulation Materials Based on Deep Convolutional Neural Networks As a non-invasive diagnostic technique, evolved gas analysis (EGA) holds significant value in assessing the insulation conditions of critical equipment such as power cables. Current analytical methods face two major challenges: insulation materials may undergo multiple aging mechanisms simultaneously, leading to interfering characteristic gases; and traditional approaches lack the multi-label recognition capability to address concurrent fault patterns when processing mixed-gas data. These limitations hinder the accuracy and comprehensiveness of insulation condition assessment, underscoring the urgent need for intelligent analytical methods. This study proposes a deep convolutional neural network (DCNN)-based multi-label classification framework to accurately identify the gas generation characteristics of five typical power cable insulation materials-ethylene propylene diene monomer (EPDM), ethylene-vinyl acetate copolymer (EVA), silicone rubber (SR), polyamide (PA), and cross-linked polyethylene (XLPE)-under fault conditions. The method leverages concentration data of six characteristic gases (CO<sub>2</sub>, C<sub>2</sub>H<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>), integrating modern data analysis and deep learning techniques, including logarithmic transformation, Z-score normalization, multi-scale convolution, residual connections, channel attention mechanisms, and weighted binary cross-entropy loss functions, to enable simultaneous prediction of multiple degradation states or concurrent fault pattern combinations. By constructing a gas dataset covering diverse materials and operating conditions and conducting comparative experiments to validate the proposed DCNN model's performance, the results demonstrate that the model can effectively learn material-specific gas generation patterns and accurately identify complex label co-occurrence scenarios. This approach provides technical support for improving the accuracy of insulation condition assessment in power cable equipment. Convolutional neural network ; Power cable ; Fault (geology) ; Artificial neural network ; Silicone rubber ; Reliability (semiconductor) ; Deep learning ; Power (physics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Mohammad Mohammadi | Yousef Bazargan-Lari | Farhang Daneshmand | Seyed Mohammad Reza Nazemosadat
Source: Carbon letters (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

AI-driven strategies for the design and functionalization of carbon nanotubes: a critical review Benchmarking ; Surface modification ; Robotics ; Automation ; Carbon nanotube ; Materials informatics ; Convergence (economics) ; Robot Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion
Authors: David Flores | Carolina Lanas | Gabriel Velez | Rafael Amaya | Nicolás Ratkovich | Maria Baldeon Calisto
Source: Applied Food Research (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Unveiling aroma: A machine learning approach to modeling aroma profiles from chemical components Accurately predicting the aroma of a molecule based on its chemical structure is a promising direction for advancing the development of flavors and fragrances in the food, cosmetic, and pharmaceutical industries. This study explores the potential of machine learning (ML) techniques to predict aroma descriptors from molecules encoded in SMILES format. A newly curated dataset was developed, comprising 5,855 molecules annotated with 44 standardized odor descriptors. Three models were evaluated on this dataset, namely Random Forest, XGBoost, and TabNet. Molecular feature extraction was conducted using Morgan Fingerprints, and the influence of fingerprint radius on predictive performance was assessed. To address the significant label imbalance, the MLSMOTE data augmentation strategy was also evaluated. Among the tested models, XGBoost trained with a fingerprint radius of 2 demonstrated the highest predictive performance. It achieved a mean AUROC of 0.92, a precision of 0.54, a recall of 0.63, and an F1-score of 0.58. In contrast, TabNet consistently underperformed across all configurations, with statistically lower F1 scores than the other two ML models. External validation on an independent cacao-specific molecular dataset further demonstrated the robustness of the XGBoost model, which achieved a low Hamming Loss, a moderately high AUROC, and a moderate Recall, indicating effective identification of true-positive aroma descriptors. These findings highlight the potential of machine learning models, particularly XGBoost, as a reliable, data-driven approach for predicting sensory attributes from molecular structure. Moreover, this approach offers a promising alternative for accelerating aroma design and reducing subjectivity in product development across sensory-driven industries. Component (thermodynamics) ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial neural network ; Training set Machine Learning in Materials Science ; Fermentation and Sensory Analysis ; Computational Drug Discovery Methods
Authors: Linfan Sun | Haoyu Zhang | Zhiduo Liu | Jun Cheng | Ge Zhou | Ximin Zang | Lijia Chen | Peter K. Liaw
Source: Open MIND (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Physical-information machine learning for strength and ductility prediction of metastable <i>β</i> titanium alloys Machine learning for predicting metastable β titanium alloy properties suffers from limited generalisation, unreliable extrapolation beyond data boundaries, and inadequate modelling of nonlinear effects of process parameters. This study proposes a physics-informed machine learning approach incorporating intrinsic physical attributes and phase transformation kinetics. Using 496 samples, it achieves R<sup>2</sup> = 0.95 for ultimate tensile strength (UTS) and 0.90 for elongation (El), accurately predicting out-of-boundary alloys with similar heat treatment responses within 5.0% UTS and 2.5% El error. This approach reduces reliance on data completeness and complex algorithms, providing an accurate, generalisable path to UTS- and El-targeted titanium alloy design. Titanium alloy ; Extrapolation ; Ultimate tensile strength ; Ductility (Earth science) ; Titanium ; Metastability ; Alloy ; Elongation Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Hongyu Wu | Ruoyu Wang | Xin Chen | Zhicheng Zhong
Source: AI for Science (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

A Pretrain-Finetune-Distill framework for machine learning force fields in doping engineering of solid-state electrolytes Doping engineering is a key strategy to improve ionic conductivity of solid ionic conductors, but the vast chemical composition space poses significant challenges for experimental optimization. Computational methods, such as ab initio or classical molecular dynamics, suffer from a critical trade-off between scale and accuracy when dealing with complex doping scenarios. To address this, we introduce a Pretrain-Finetune-Distill framework for developing machine learning force fields that simultaneously capable of faithfully capturing the characteristics of a doping domain and enable large-scale simulation, capabilities that conventional methods fail to combine. We apply this framework to the argyrodite-type $\text{Li}_{6-x}\text{PS}_{5-x}\text{ClBr}_x$ , generating a model achieves density functional theory-level accuracy cover various doping ratios. Using a statistical resampling method, we systematically analyze the dependence of ionic conductivity on Br doping concentration, with results consistent with experimental observations. Further we reveal significant discrepancies of conductivity behavior between high-temperature and room-temperature. Through in-depth analysis of large-scale simulation trajectories, we identify the root cause of these discrepancies: a cubic-to-orthorhombic structural phase transition that occurs exclusively at room temperature which caused by high Br doping levels. This study demonstrates our framework’s effectiveness in accelerating the discovery and optimization of solid-state electrolytes through accurate and scalable simulations. Doping ; Field (mathematics) ; Work (physics) ; Mechanism (biology) ; Artificial neural network Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly
Authors: Arlete Santos
Source: ChemRxiv (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Experimental design, machine learning, and genetic algorithm to support the production of conductive polymer nanocomposites The development of conductive polymer nanocomposites presents a complex challenge due to the nonlinear interactions between polymer matrices, carbon-based fillers, and processing conditions. In this study, we combine experimental design, machine learning (ML), and genetic algorithms (GA) to optimize the formulation of conductive filaments for additive manufacturing applications. A full factorial experimental design was employed to evaluate different polymer types, carbonaceous fillers, and processing parameters. The electrical conductivity of the resulting composites was measured and used to train predictive models-namely, a multilayer perceptron (MLP) and a random forest (RF)-whose hyperparameters were optimized using Optuna. Both models achieved satisfactory performance metrics, with Nash-Sutcliffe Efficiency (NSE) values of 0.47 for MLP and 0.43 for RF on the test set. The trained models were then integrated into a genetic algorithm to explore optimal formulations, which identified PA66 as the most promising matrix and single-walled carbon nanotubes as the most effective conductive filler. Model interpretability was assessed via SHapley Additive exPlanations (SHAP), which corroborated GA results, particularly highlighting the models' preference for longer processing times and lower/medium rotation speeds-conditions that align with known percolation and dispersion requirements in conductive nanocomposites. This integrated approach illustrates the potential of materials informatics to support the design and discovery of advanced functional materials. Electrical conductor ; Genetic algorithm ; Conductive polymer ; Interpretability ; Carbon nanotube ; Percolation (cognitive psychology) ; Nanocomposite ; Artificial neural network ; Polymer Carbon Nanotubes in Composites ; Advanced Sensor and Energy Harvesting Materials ; Polymer crystallization and properties
Authors: Gibeom Kim | Kyungchul Cho | Sang-Hum Kwon | Sung-Suk Jung | Chang-Hee Yim | Dae-Geun Hong
Source: Materials & Design (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Explainable deep learning-aided design of alloyed carbon steel with improved hot ductility for manufacturing scalability • Deep learning model predicts hot ductility from composition and casting conditions. • Model captures six ductility curve shapes with high accuracy (MAE = 5.11%) • Temperature is the strongest factor; niobium and carbon are influential • Explainability shows niobium lowers ductility; titanium–nitrogen balance matters. • Case study reduces surface cracks and supports industrial casting decision. This study proposes an explainable deep neural network model to predict the reduction of area (RA) behavior of diverse steel compositions under various casting conditions, and demonstrates its application to the design of new alloyed carbon steels that have increased castability. The model was trained on 4,528 RA data collected from in-house experiments and the literature. It incorporated 16 steel components and 6 thermal history variables. It achieved high prediction accuracy (RMSE = 7.16%, MAE = 5.11%, R2 = 0.91) and successfully described six distinct RA curve types. The use of Shapley Additive Explanations (SHAP) to evaluate the contribution of each input variable enabled metallurgical interpretation of individual RA predictions. The SHAP analysis revealed complex interactions among key elements (e.g., Nb, Ti, N), and these elements have a negative impact on RA value when their contents increase. A case study highlighted the model’s utility in the design of a new alloy for automotive applications. This approach can avoid the disadvantages of conventional trial-and-error methods and provides a scalable, cost-effective method for predicting hot ductility and designing alloys for industrial continuous casting. Ductility (Earth science) ; Carbon steel ; Carbon fibers ; Scalability ; Alloy steel Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses