jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Masaaki Araidai | Kazuaki Kuwahata | Masanori Tachikawa | Tomofumi Tada (1674688)
Source: Science and Technology of Advanced Materials Methods (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Physically informed neural network potential for a multi-element system Various machine-learning potentials have been proposed for deeper understanding of complex concerted phenomena that involve too many atoms and molecules to be analyzed by first-principles electronic states calculations. However, the learning processes are purely mathematical, making their transferability to unknown structures problematic in some cases. Recently, the physically informed (PI) neural network (NN) potential has been proposed to resolve this issue. This approach uses well-known interatomic potential functions based on physical intuition, similar to an empirical potential with a few parameters, and the parameter sets are predicted by NN instead of predicting the function values, which improves the transferability. However, creating PINN potentials of multi-element systems is difficult because the appropriate parameter sets are difficult to know in advance. In the present study, we present a method to create the PINN potentials of a LiNiO [Formula: see text] bulk crystal as a multi-element system. LiNiO [Formula: see text] is considered to be formed mainly by the attractive interactions between Li and O atoms and those between O and Ni atoms; the other interactions are considered to be small. The resulting PINN potentials well reproduced such behaviors. As an application of the developed PINN potentials, we performed path integral molecular dynamics simulations. We found that the simulations operated stably enough to evaluate the radial distribution function for each element pair, and that the nuclear quantum effects became more pronounced as the elements became lighter. Transferability ; Function (biology) ; Artificial neural network ; Molecular dynamics ; Interatomic potential ; Complex system ; Element (criminal law) ; Crystal (programming language) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Shoeb Athar | Adrien Mecibah | Philippe Jund
Source: ArXiv.org (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Robust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study presents a robust workflow, applied to the half-Heusler (hH) structural prototype, for figure of merit (zT) prediction, to improve the generalizability of ML models. To resolve challenges in dataset handling and feature filtering, we first introduce a rigorous PCA-based splitting method that ensures training and test sets are unbiased and representative of the full chemical space. We then integrate Bayesian hyperparameter optimization with k-best feature filtering across three architectures-Random Forest, XGBoost, and Neural Networks - while employing SISSO symbolic regression for physical insight and comparison. Using SHAP and SISSO analysis, we identify A-site dopant concentration (xA'), and A-site Heat of Vaporization (HVA) as the primary drivers of zT besides Temperature (T). Finally, a high-throughput screening of approximately 6.6x10^8 potential compositions, filtered by stability constraints, yielded several novel high-zT candidates. Breaking from the traditional focus of improving test RMSE/R^2 values of the models, this work shifts the attention on establishing the test set a true proxy for model generalizability and strengthening the often neglected modules of the existing ML workflows for the data-driven design of next-generation thermoelectric materials. Generalizability theory ; Hyperparameter ; Artificial neural network ; Workflow ; Feature (linguistics) ; Test set ; Hyperparameter optimization ; Test data Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Yi Ye | Hongwei Hou | Lei Yang | Jinghong Li
Source: TrAC Trends in Analytical Chemistry (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Machine-learning assisted 2D field-effect transistors for intelligent biosensing and analysis Biosensor ; Neuromorphic engineering ; Key (lock) ; Transistor ; SIGNAL (programming language) ; Process (computing) ; Signal processing 2D Materials and Applications ; Nanowire Synthesis and Applications ; Graphene research and applications
Authors: Deyani Nocedo-Mena | Estefania Ascencio | Shan He | Oxana V. Kharissova | Romeo Selvas | Eduardo Perez-Tijerina | Orestes Boffill Beltrán | Sonia Arrasate | Humberto González-Díaz
Source: Materials & Design (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Machine-learning-guided design and experimental validation of biofunctionalized nanodiamonds • A predictive design strategy is introduced to guide the selection of nanodiamond functionalization interfaces within large combinatorial design spaces. • Model-guided selection enables the synthesis and experimental validation of high-performance hybrid nanodiamond systems. • The approach reduces empirical trial-and-error and accelerates rational design of advanced functional nanodiamonds. • Practical design rules linking nanodiamond core properties, surface chemistry and application response are established. The rational design of biofunctionalized nanodiamonds (NDs) remains challenging due to the vast combinatorial space arising from ND core properties and surface functionalization strategies. Here, we introduce ND.PTML, a perturbation-theory-based machine learning framework developed to guide the selection and prioritization of functionalized nanodiamond systems using descriptors of both the ND core and functionalizing agents. A curated dataset of 426 experimentally reported nanosystems was used to train interpretable classification models. The optimized Decision Tree achieved the best predictive performance (training: Sp = 95.1%, Sn = 71.6%; validation: Sp = 78.1%, Sn = 71.4%) with AUROC values above 0.80, indicating robust classification capability. Model-guided screening generated 103 virtual configurations and supported the experimental synthesis of six representative nanosystems incorporating synthetic linkers and plant-derived extracts. Surface functionalization was confirmed by FTIR, Raman, and XPS analyses, while DLS and ζ-potential measurements demonstrated improved colloidal stability relative to pristine nanodiamonds. Optoelectronic and cytotoxicity assays validated predicted performance trends. These results establish an experimentally validated machine-learning-assisted materials-by-design strategy that reduces empirical trial-and-error and enables interface-engineered functional nanomaterials. Design of experiments ; Nanoparticle ; Nanodiamond ; Carbon nanotube Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Graphene and Nanomaterials Applications
Authors: Satyabrata Aich | Sudipta Mohapatra | Shrabani Nanda | Taqdees Khan | Chea Senghuy | Hajra Sultana | Okpete Uchenna Esther Ada | Ayushi Bharti | Umashankari Kalaiarsan | Proloy Kumar Mondal | Yong-Ki Lee
Source: Preprints.org (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

A Two-Stage Deep Learning Framework for Automated Corrosion Detection and Severity Estimation in High-Resolution SEM Images Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presents a two-stage deep learning pipeline tailored for high-resolution scanning electron microscopy images. The framework combines instance-level corrosion segmentation using the YOLOv8-seg architecture with subsequent severity classification performed by EfficientNet-B0 and ResNet18. In the segmentation stage, models are trained using both manually annotated and automatically generated binary masks, enabling robust instance mask prediction through prototype-based mask decoding. The classification stage assesses the severity of corrosion by analyzing localized regions based on morphological features, leveraging convolutional neural networks optimized for binary output. Experimental results demonstrate strong performance: the segmentation model trained on manual annotations achieves a mean Intersection-over-Union (mIoU) of 89.91, a mask mAP@50 of 98.6, and an ROC-AUC of 94.69. For severity classification, EfficientNet-B0 achieves an accuracy of 93.75% and an F1-score of 93.29, outperforming ResNet18. These findings underscore the potential of combining lightweight segmentation and classification models to enable precise and automated corrosion analysis, supporting integration into real-time inspection workflows and intelligent material diagnostics in industrial settings. Convolutional neural network ; Deep learning ; Segmentation ; Workflow ; Pipeline (software) ; Pattern recognition (psychology) ; Binary classification ; Artificial neural network Corrosion Behavior and Inhibition ; Infrastructure Maintenance and Monitoring ; Advanced Neural Network Applications
Authors: Mohammad Bagheri | Pekka Koskinen
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Lateral Graphene-Metallene Interfaces at the Nanoscale Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and practical applications. However, their stabilization can still be achieved by suspending them in the pores of two-dimensional template materials, making the properties of lateral interfaces of metallenes scientifically relevant. Here, we combined density-functional theory and universal machine-learning interatomic potentials to study lateral interfaces between graphene and 45 metallenes with various profiles. We optimized the interfaces and analyzed their energies, electronic structures, and stabilities at room temperature, defect formations, and structural deformations. While broad trends were identified using machine-learning analysis of all interfaces, density-functional theory was the main tool for studying the microscopic properties of selected elements. We found that the interfaces are the most stable energetically and with respect to lattice mismatch, defect formation, and lateral strain when their profiles were geometrically smooth. The most stable interfaces are found for transition metals. In addition, we demonstrate how universal machine-learning interatomic potentials now offer the accuracy required for the modeling of graphene-metallene interfaces. By systematically expanding the understanding of metallenes' interface properties, we hope these results guide and accelerate their synthesis to enable future applications and benefit from metallenes' appealing properties. Nanoscopic scale ; Interatomic potential ; Interface (matter) ; Graphene ; Isotropy ; Lattice (music) Machine Learning in Materials Science ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Mahbir Ahmed Maheen
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

Accelerated Discovery and Stability Prediction of Lead-Free Halide Perovskites via Physics-Informed Machine Learning Metal halide perovskites have emerged as promising candidates for advanced photovoltaic and optoelectronic devices; however, their commercial deployment is significantly limited by the toxicity and chemical instability of lead-based compositions . All-inorganic lead-free alternatives, particularly cesium tin mixed-halide perovskites, (CsSn(ClxBryI1−x−y)3) provide a viable pathway to environmentally benign and thermally stable materials . However, identifying experimentally realizable compositions remains challenging due to the vast combinatorial compositional space and the persistent discrepancy between theoretical stability and practicalsynthesis.This study develops a physics-informed and explainable machine learning framework to predict the synthesizability and thermodynamic stability of mixed-halide tin perovskites . A Positive-Unlabeled learning strategy, utilizing transductive bagging and decision tree classifiers, estimates synthesis probability based on literature-derived data [5]. A 76-dimensional descriptorset, incorporating compositional, elemental, structural, and density functional theory (DFT)-derived thermodynamic parameters, is constructed . Gradient boosting regression modelsfurther predict macroscopic properties, while SHAP analysis provides physical interpretability of the prediction.The model identifies a limited subset of compositions with a high probability of successful synthesis and demonstrates that the advanced tolerance factor (𝜏), B-site atomic characteristics, and local lattice distortion are dominant stability descriptors [9, 10]. Machine-learned atomic potentials enable effective mapping of temperature-driven structural phase properties, overcoming the limitations of ab initio molecular dynamics . This framework bridges computational predictions and experimental feasibility, establishing a general data-driven methodology that accelerates the discovery pipeline for stable, non-toxic, lead-free perovskite materials. Interpretability ; Perovskite (structure) ; Boosting (machine learning) ; Decision tree ; Stability (learning theory) ; Density functional theory ; Ab initio ; Chemical space ; Tin Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Roda Bounaceur | Francisco Carlos Paes | Jean-Noël Jaubert | Romain Privat
Source: Chemical Engineering Journal Advances (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Accurate prediction of ideal gas heat capacity C(T) using machine learning: data augmentation and ensemble learning for robust thermodynamic modeling • Flexible framework using Mordred descriptors and neural networks for Cp prediction. • Cp normalized by infinite-temperature limit improves dataset balance. • High accuracy achieved (R² > 0.999) with bagged neural network ensemble. • Model generalizes well across chemical space, including complex molecules. Reliable predictions of ideal-gas molar heat capacities at constant pressure are a cornerstone of thermodynamic modeling. In the absence of a reliable model or correlation for the estimation of the ideal-gas heat capacity, an equation of state (EoS) alone cannot ensure thermodynamically consistent calculations of state-function variations, nor can it provide real-fluid heat capacities, since it only yields residual contributions. Changes in internal energy, enthalpy, entropy, exergy, and real-fluid heat capacities require the integration of the ideal-gas contribution which depends solely on the temperature-dependent ideal-gas heat capacity. Thus, coupling an EoS with an ideal-gas heat capacity model enables full thermodynamic property evaluation in process simulators. In this paper, we propose a deep-learning framework for continuous prediction of temperature-dependent ideal-gas molar heat capacities at constant pressure ( C p ) via artificial neural networks (ANNs) trained using molecular descriptors computed with Mordred and a high-quality experimental database comprising 1,471 compounds. The dataset was augmented using experimentally validated correlations, generating 100 C p data points per molecule over their corresponding temperature range and several ANN models were trained in a bagging ensemble configuration. The resulting model achieves excellent predictive accuracy (R² > 0.999 for both training and test sets) while demonstrating strong generalization to external datasets, indicating that the framework captures transferable structure-thermodynamic relationships. By explicitly treating temperature as an input variable, the proposed model provides smooth and continuous ideal-gas C p ( T ) predictions while preserving physically consistent thermodynamic trends across temperature ranges relevant to industrial applications. The developed prediction tool is available in a web interface (link). Ideal (ethics) ; Heat capacity ; Ensemble learning ; Work (physics) ; Ensemble forecasting ; Ideal gas ; Volumetric heat capacity Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Model Reduction and Neural Networks
Authors: Mahbir Ahmed Maheen
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

Accelerated Discovery and Stability Prediction of Lead-Free Halide Perovskites via Physics-Informed Machine Learning Metal halide perovskites have emerged as promising candidates for advanced photovoltaic and optoelectronic devices; however, their commercial deployment is significantly limited by the toxicity and chemical instability of lead-based compositions . All-inorganic lead-free alternatives, particularly cesium tin mixed-halide perovskites, (CsSn(ClxBryI1−x−y)3) provide a viable pathway to environmentally benign and thermally stable materials . However, identifying experimentally realizable compositions remains challenging due to the vast combinatorial compositional space and the persistent discrepancy between theoretical stability and practicalsynthesis.This study develops a physics-informed and explainable machine learning framework to predict the synthesizability and thermodynamic stability of mixed-halide tin perovskites . A Positive-Unlabeled learning strategy, utilizing transductive bagging and decision tree classifiers, estimates synthesis probability based on literature-derived data [5]. A 76-dimensional descriptorset, incorporating compositional, elemental, structural, and density functional theory (DFT)-derived thermodynamic parameters, is constructed . Gradient boosting regression modelsfurther predict macroscopic properties, while SHAP analysis provides physical interpretability of the prediction.The model identifies a limited subset of compositions with a high probability of successful synthesis and demonstrates that the advanced tolerance factor (𝜏), B-site atomic characteristics, and local lattice distortion are dominant stability descriptors [9, 10]. Machine-learned atomic potentials enable effective mapping of temperature-driven structural phase properties, overcoming the limitations of ab initio molecular dynamics . This framework bridges computational predictions and experimental feasibility, establishing a general data-driven methodology that accelerates the discovery pipeline for stable, non-toxic, lead-free perovskite materials. Interpretability ; Perovskite (structure) ; Boosting (machine learning) ; Decision tree ; Stability (learning theory) ; Density functional theory ; Ab initio ; Chemical space ; Tin Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Mas Klein | Irina Rudenko | Evgeny A. Pidko | Иван С. Бушмаринов
Source: ChemRxiv (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

ConforFormer: representation for molecules through understanding of conformers Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on benchmark datasets. However, current approaches require updating the weights of the entire model during the fine-tuning procedure for each prediction task. While this enables state-of-the-art performance, it limits practical deployment, as real-world datasets are often too small to support the stable retraining of large models. Importantly, the 3D geometry of a molecule holds crucial information for predicting its properties, but a single molecular graph usually corresponds to several 3D geometries, called conformers, introducing ambiguity into the inference process. Typical solutions rely on molecular graphs, but this approach is not easily generalizable beyond organic molecules. Here, we present ConforFormer, a method that explicitly accounts for the diversity of 3D conformations of a molecule to derive a task-agnostic and conformation-agnostic vector representation. This model serves as a foundational framework, producing embeddings that can be generated once and directly applied to downstream tasks, including property prediction and structural similarity, without extensive fine-tuning. Molecular graph ; Representation (politics) ; Inference ; Ambiguity ; Focus (optics) ; Property (philosophy) ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: S. Saito | Keisuke TAKEUCHI | Hiroaki NAKAMURA | Yasuhiro Oda | K Sawada | K. Hoshino | Yuki HOMMA | S. Yamoto | Yuki Uchida
Source: Plasma and Fusion Research (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning of Hydrogen Trapping Sites in Tungsten for Atomistic Plasma-Wall Simulations Understanding hydrogen trapping in tungsten is crucial for accurate modeling of plasma-wall interactions in fusion devices. In this study, we developed a deep learning model to predict hydrogen trapping sites, which serve as essential input parameters for kinetic Monte Carlo (kMC) simulations. The model employs a U-Net–based convolutional neural network that directly maps three-dimensional potential energy distributions to trapping site positions. Training data were generated from atomistic calculations using the embedded atom method (EAM) potential, and ground-truth trapping sites were systematically identified by force relaxation. The trained model achieved an F1 score of approximately 0.76, with most predicted sites coinciding with the true minima within ± 1 voxels (1 voxel = 0.1 Å per side). Visual comparisons confirmed the ability of the model to capture both global and local features of the potential energy landscape. In terms of efficiency, the proposed approach reduced prediction time by more than three orders of magnitude compared with conventional force-relaxation searches, enabling predictions in less than one second on a GPU. These results demonstrate that deep learning provides an accurate and computationally efficient method for identifying trapping sites. Future extensions include incorporating more complex defect structures and integrating the model into molecular dynamics-kMC hybrid frameworks for large-scale plasma-wall interaction simulations. Tungsten ; Trapping ; Deep learning ; Hydrogen Fusion materials and technologies ; Vacuum and Plasma Arcs ; High-pressure geophysics and materials
Authors: Roda Bounaceur | Francisco Carlos Paes | Jean-Noel Jaubert | Romain Privat
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Ideal Gas Heat Capacity Cp(T) Using Machine Learning: Data Augmentation and Ensemble Learning for Robust Thermodynamic Modeling Heat capacity ; Robustness (evolution) ; Generalizability theory ; Artificial neural network ; Ensemble forecasting ; Consistency (knowledge bases) ; Ensemble learning ; Isobaric process Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure