jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenggang Wu | Mingqing Liao | Xinyi Zhou | Jinwei Chen | Xiangrui Qian | Yuehua Wang | Fei Zhou | Fengjiang Wang
Source: Journal of Materials Informatics (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Exploring the Pareto front of strength-conductivity trade-off: interpretable machine learning for property prediction and composition design in high-strength high-conductivity Cu alloys High ultimate tensile strength (UTS) and high electrical conductivity (EC) copper alloy is a key material in upgrading traditional manufacturing industries. Since a trade-off exists between UTS and EC, the design of new alloys balancing the two properties is a significant challenge. In this paper, to expedite the design of copper alloys with target properties, we integrated feature engineering, machine learning (ML), and swarm intelligence algorithms to construct a paradigm that includes model construction, property prediction, and composition inverse design. By introducing and selecting relevant physical features, a high-quality ML model relating composition, physical features, and properties in copper alloys was constructed, with an error of less than 9%. Based on the well-constructed model, the physical features and elements that significantly affect the UTS and EC of copper alloys are identified by the Shapley additive explanations model. In addition, we investigated the alloying effects on the UTS and EC within the Cu-Cr-Zr-Mg system. Finally, using the multi-objective grey wolf optimizer algorithm, we designed several new alloys showing high UTS and high EC copper alloys and explored the Pareto frontier between UTS and EC, which provides a guide for developing new high-performance copper alloys. Copper ; Property (philosophy) ; Particle swarm optimization ; Multi-objective optimization ; Ultimate tensile strength ; Composition (language) ; Pareto optimal ; Alloy ; Inverse Machine Learning in Materials Science ; Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties
Authors: Dr.Srinagalakshmi Nammi Sangat Naik
Source: Open MIND (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Physics-Guided Machine Learning Framework for Defect Prediction and Mechanical Property Modeling in WAAM of Nitinol Shape Memory Alloy Wire Arc Additive Manufacturing (WAAM) has emerged as a promising technique for fabricating large-scale metallic components; however, process instability and defect formation remain critical challenges, particularly for thermally sensitive materials such as Nitinol shape memory alloys (SMAs). In this study, a physics-guided machine learning framework is developed to predict defect occurrence and mechanical performance in WAAM-fabricated Nitinol. Ten experimental cases were produced under varying current, voltage, and torch travel speed conditions, and the corresponding heat input was calculated using thermodynamic energy balance principles. Defect observations, hardness values (before and after heat treatment), and ultimate tensile strength (UTS) were experimentally evaluated. A hybrid modeling strategy integrating process parameters with physics-derived heat input was implemented using classification and regression approaches. Leave-One-Out Cross Validation (LOOCV) was employed to ensure robust evaluation given the limited dataset. Results indicate that inclusion of heat input as a derived physical feature significantly improves prediction consistency and interpretability. An optimal process stability window was identified around intermediate heat input levels, corresponding to defect-free builds and stable mechanical response. The proposed physics-guided learning framework demonstrates the potential of integrating metallurgical understanding with data-driven modeling for intelligent process control in additive manufacturing of shape memory alloys. Process (computing) ; Shape-memory alloy ; Consistency (knowledge bases) ; Stability (learning theory) ; Ultimate tensile strength ; Process window ; Energy (signal processing) ; Titanium alloy ; Property (philosophy) Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Côme Cattin | Thomas Plé | Olivier Adjoua | Nicolaï Gouraud | Louis Lagardère | Jean-Philip Piquemal
Source: The Journal of Physical Chemistry Letters (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network, where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 Å cutoff distilled model is sufficient to capture the fast-varying forces, i.e., mainly bonded interactions, from the accurate potential, allowing its use in a reversible reference system propagator algorithm (RESPA)-like formalism. The approach conserves accuracy, preserving both static and dynamic properties, while enabling us to evaluate the costly model only every 3 to 6 fs depending on the system. Consequently, large simulation speedups over standard 1 fs integration are observed: nearly 4-fold in homogeneous systems and 3-fold in large solvated proteins through leveraging active learning for enhanced stability. Such a strategy is applicable to any neural network potential and reduces the performance gap with classical force fields. Artificial neural network ; Distillation ; Molecular dynamics ; System dynamics ; Homogeneous ; Foundation (evidence) ; Cutoff ; Scheme (mathematics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Huang Qiao | Yucheng Cao | Atwakyire Moses | Hao Wang | Wenhao Li | Ce Jiao | Chen Ding
Source: Journal of Materials Science (article, 2026-01-08)
Field: Materials Science — Biomaterials

Machine learning with local outlier factor for predicting the electrochemical corrosion behavior of rare-earth-doped Mg alloys in chloride ions Corrosion ; Support vector machine ; Random forest ; Outlier ; Grid ; Generalization ; Alloy ; Hyperparameter optimization Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: M. L. Pereira Junior | M. G. E. da Luz | P. Cesana | A. L. da Rosa | M. J. Piotrowski | D. Guedes-Sobrinho | T. A. S. Pereira | E. A. Moujaes | A. C. Dias | R. M. Tromer
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey over 4000 Materials Adequate characterization of two-dimensional materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the C2DB. The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The equilibrium distance between the impurity and the 2D surface is not calculated from a tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. But we also are able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization. Chalcogen ; Impurity ; Adsorption ; Valence (chemistry) ; Scalability ; Surface (topology) ; Scaling ; Valence electron 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Baptiste Martin | Shukai Yao | Chunyu Li | Anthony Bocahut | Matthew Jackson | Alejandro Strachan
Source: Macromolecules (preprint, 2026-01-09)
Field: Materials Science — Materials Chemistry

Predictive Models for Strain Energy in Condensed Phase Reactions Molecular modeling of thermally activated chemistry in condensed phases is essential to understand polymerization, depolymerization, and other processing steps of molecular materials. Current methods typically combine molecular dynamics (MD) simulations to describe short-time relaxation with stochastic descriptions of predetermined chemical reactions. Possible reactions are often selected on the basis of geometric criteria, such as the capture distance between reactive atoms. Although these simulations have provided valuable insight, the approximations used to determine possible reactions often lead to significant molecular strain and unrealistic structures. We show that the local molecular environment surrounding the reactive site plays a crucial role in determining the resulting molecular strain energy and, in turn, the associated reaction rates. We develop a graph neural network capable of predicting the strain energy associated with a cyclization reaction from the prereaction, local, molecular environment surrounding the reactive site. The model is trained on a large data set of condensed-phase reactions during the activation of polyacrylonitrile (PAN) obtained from MD simulations and can be used to adjust relative reaction rates in condensed systems and advance our understanding of thermally activated chemical processes in complex materials. Molecular dynamics ; Chemical reaction ; Molecule ; Relaxation (psychology) ; Polyacrylonitrile ; Potential energy ; Activation energy ; Molecular descriptor ; Energy profile Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Polymer crystallization and properties
Authors: Y. Sakai | Munekazu Ohno
Source: Acta Materialia (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Machine-learning prediction for solid–liquid interfacial properties from micrographs in Al–Cu and Fe–Mn alloys Micrograph ; Microstructure ; Anisotropy ; Alloy ; Convolutional neural network ; Linkage (software) ; Surface energy Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Yan Chen | Boyuan Gou | Yang Gao | Shuai Xu | Xiangdong Ding
Source: Advanced Materials (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Detecting Deformation Mechanisms in Alloys Under Uniaxial Loading via Acoustic Emission and Machine Learning Deciphering the complex acoustic emission (AE) signals of coexisting deformation mechanisms is critical for advancing the fundamental understanding and predictive health monitoring of metals and alloys. While AE technology is uniquely suited for this dynamic, non-invasive probing, the superposition and intertwining of signals from multiple active mechanisms present a formidable challenge for reliable identification. This review highlights how emerging synergies between physical statistical models of deformation mechanisms and advanced machine learning (ML) techniques are forging new pathways to deconvolute these deformation mechanisms under uniaxial loading. We first establish the foundational correlation between specific AE features and underlying deformation processes under uniaxial loading. We then critically evaluate ML-driven strategies for mechanism discrimination, assess progress in real-time monitoring and failure prognostics, and conclude by outlining key challenges and future opportunities aimed at transitioning this methodology from laboratory insight to engineered application. Acoustic emission ; Deformation mechanism ; Deformation (meteorology) ; Superposition principle ; Mechanism (biology) ; Forging Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; Machine Fault Diagnosis Techniques
Authors: Md. Efatuzzaman Efat | Sayed Md Fazle Rabbi | Md. Saiful Islam
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data Driven Explainable Machine Learning for Predicting Mechanical Properties of Copper-Based Multi-Principal Element Alloys Intermetallic ; Data-driven ; Ultimate tensile strength ; Precipitation hardening ; Boosting (machine learning) ; Alloy ; Annealing (glass) ; Microstructure ; Grain size ; Curse of dimensionality Machine Learning in Materials Science ; Microstructure and mechanical properties ; Metallurgy and Material Science
Authors: M. L. Pereira Junior | M. G. E. da Luz | P. Cesana | A. L. da Rosa | M. J. Piotrowski | D. Guedes-Sobrinho | T. A. S. Pereira | E. A. Moujaes | A. C. Dias | R. M. Tromer
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey over 4000 Materials Adequate characterization of two-dimensional materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the C2DB. The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The equilibrium distance between the impurity and the 2D surface is not calculated from a tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. But we also are able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization. Chalcogen ; Impurity ; Adsorption ; Valence (chemistry) ; Scalability ; Surface (topology) ; Scaling ; Valence electron 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Lucio Peña-Zarate | Alberto Vela | Jorge Tiburcio
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and machine learning potential Engineering molecular machines requires a precise knowledge of the mechanisms involved in programmed motions. Among artificial molecular machines, rotaxanes have emerged as a prominent model due to their ability to perform diverse and controlled motions, such as threading, shuttling, and pirouetting. In this work, we present a reliable theoretical framework to describe the threading motion during the assembly of rotaxane-like complexes. Our approach combines the climbing image nudged elastic band method with the ANI-1ccx neural network potential, trained with gold-standard data. Energetic and structural variations along a normalized displacement coordinate allowed an accurate atomistic description of the threading process of different dumbbell-shaped molecules (axles) through the cavity of two different macrocyclic hosts (tori). Using the methodology proposed herein, two key steps are identified: stabilization through hydrogen bonds, which we call the claw mechanism, and the expansion of the macrocycle. An energy decomposition analysis, performed by single-point calculations on selected structures, allows an analysis of the role of steric and electrostatic effects in the structural stabilization of supramolecular assemblies. We find that, although ANI-1ccx was not explicitly trained for charged systems, this neural network potential effectively discriminates among various charged states. Furthermore, calculated potential energy barriers are in good agreement with reported experimental free energy barriers. The featured methodology has the potential to become a fundamental artificial-intelligence-based tool for the study of diverse motions observed in supramolecular systems. Artificial neural network ; Supramolecular chemistry ; Threading (protein sequence) ; Process (computing) ; Molecular machine ; Energy (signal processing) ; Potential energy ; Molecular dynamics Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Surface Chemistry and Catalysis
Authors: Tong Yan | Huamei Yin | Lu Wang | Xiaolong Ao | Jinxiao Zhang
Source: Surfaces and Interfaces (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

An interpretable machine learning framework for predicting small-molecule adsorption on Ni@g-C₃N₄ from vibrational spectra Adsorption ; Pattern recognition (psychology) ; Spectral line ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced Chemical Physics Studies
Authors: Deqing Xue | Yizhen Li | Yan Pan | G.J. Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

λ₂-Guided Machine Learning Design of Low-Hysteresis Ti–Ni–Cu–Zr Shape Memory Alloys Shape-memory alloy ; Boosting (machine learning) ; Hysteresis ; Transformation (genetics) ; Compatibility (geochemistry) ; Interval (graph theory) Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Fulin Shao | Weiying Li | Zhiwei Liang | Changping Wang | Ting S. Li | Zhongqing Wei | Xingzhong Xu
Source: Environmental Research (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Molreac-Oxi: An end-to-end deep learning–quantum chemistry platform for •OH reactivity (kOH), pathways, and active-site insight Interpretability ; Workflow ; Fidelity ; Residual ; Inference ; Linear model ; Resolution (logic) ; Molecular descriptor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: I. Dhifallah | Boubaker Askri
Source: X-Ray Spectrometry (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

A Neural Network‐Based Numerical Approach for X‐Ray Diffraction Analysis: Application to Spinel Materials ABSTRACT This work presents a neural‐network‐based numerical approach for analyzing x‐ray diffraction (XRD) patterns of spinel materials. The open‐source CERN ROOT toolkit was used to implement the workflow. Four multilayer perceptron (MLP) neural networks were designed to predict key structural parameters: the lattice parameter, crystallite size, oxygen position and inversion degree. For each CoAl 2 O 4 and MgAl 2 O 4 spinel, the networks were trained using artificial spectra generated from a single CIF file, based on a theoretical framework describing the XRD response. Training was completed within a reasonable time for all MLPs and prediction is instantaneous once the models are trained. Single‐phase spinel samples were considered in this study. A ROOT coded procedure was developed to remove background, identify diffraction peaks and assign Miller indices in experimental spectra. The trained MLPs achieved prediction accuracies of ~95% for the lattice parameter, crystallite size and oxygen position and ~80% for the inversion degree. Validation on experimental XRD data of CoAl 2 O 4 and MgAl 2 O 4 showed good agreement with Rietveld refinement results and with values reported in the literature, while offering a substantial reduction in computation time thanks to the instantaneous prediction capability of the trained MLP models. Diffraction ; Crystallite ; Spinel ; Artificial neural network ; Multilayer perceptron ; Computation ; X-ray crystallography ; Position (finance) X-ray Diffraction in Crystallography ; Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties
Authors: Fu-Sheng Yang | L. Chen | Chao-Ching Ho
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-01-08)
Field: Materials Science — Surfaces, Coatings and Films

Artificial neural network-driven diffraction imaging for nanoscale optical critical dimension metrology in semiconductor microstructures Metrology ; Critical dimension ; Diffraction ; Dimensional metrology ; Interferometry ; Microscopy ; Semiconductor device fabrication ; Nanoscopic scale ; System of measurement Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advancements in Photolithography Techniques