jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaxuan Ma | Jie Xiong | Shuai Xu | Tianyi Zhou | Shuai Chen
Source: cScience (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Physics‐Guided Transfer Learning for Predicting Stress–Strain Curves With Limited Experimental Data ABSTRACT Many measurements in materials science are expressed as full response curves rather than single scalar values. Nevertheless, predicting entire curves reliably from limited experimental data remains challenging. Here, we address this problem for hot‐deformation stress–strain curves using a physics‐guided transfer learning (PGTL) framework that integrates constitutive modeling with deep learning. The strain‐compensated Sellars model serves as a data engine that generates synthetic curves over a wide range of temperatures and strain rates. These simulations are used to pretrain a bidirectional long short‐term memory encoder that maps processing parameters and strain history to the flow stress. Subsequently, only the output layer is fine‐tuned on a small set of experimental curves. For a nickel‐based FGH98 superalloy, the PGTL model reduces the percentage error to approximately 2%–4% in leave‐one‐curve‐out validation compared with 9%–19% for a purely data‐driven network, and it remains accurate under independent test conditions beyond the training domain. When applied to a Mg–3Sn–1Mn alloy and a Ti–Al intermetallic, the same PGTL framework captures distinct curve shapes while maintaining low errors. Collectively, these results demonstrate that PGTL generalizes across both processing spaces and alloy families, providing a reusable surrogate for hot‐forming process design and a template for modeling other curve‐type responses, including battery voltage profiles, impedance spectra, and polarization curves. Experimental data ; Encoder ; Curve fitting ; Range (aeronautics) ; Test data ; Set (abstract data type) ; Data point ; Process (computing) ; Data set ; Design of experiments Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Metallurgy and Material Forming
Authors: SM Ravikumar | JM Prabhudass | A Adinaryanan | N Yuvaraj | S Dinesh | A Elakkiya
Source: Materiali in tehnologije (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

DEEP LEARNING-BASED PREDICTION AND EXPERIMENTAL EVALUATION OF MECHANICAL PROPERTIES IN BASALT-FIBER-SiC-REINFORCED HYBRID EPOXY COMPOSITES This study investigates the predictive capabilities of deep neural network (DNN) models in estimating the mechanical properties of basalt-fiber-reinforced hybrid composites (BFRHCs). A total of 27 composite samples were fabricated using the compression-molding technique, incorporating varying numbers of basalt-fiber layers, fiber orientation angles (0°, 45°, 90°), and silicon carbide (SiC) filler contents (0, 3 and 6 w/%). The fabricated specimens were subjected to mechanical characterization by ASTM standards to evaluate their tensile strength, flexural strength, impact strength, hardness, shear strength, and interdelamination resistance. A DNN model was developed and trained on the experimental dataset to capture the complex, nonlinear relationships between input fabrication parameters and output mechanical properties. Model performance was assessed using the coefficient of determination (R²), mean absolute error (MAE), and root-mean-square error (RMSE). The DNN achieved high predictive accuracy with R² values exceeding 0.95 for most properties, demonstrating its effectiveness in forecasting composite performance. The results confirm that deep-learning frameworks such as DNNs offer a powerful and reliable approach to predicting the behavior of hybrid composites, reducing the need for extensive experimental trials and supporting efficient material design and optimization in structural applications. Ultimate tensile strength ; Composite number ; Silicon carbide ; Artificial neural network ; Flexural strength ; Epoxy ; Fabrication ; Fiber Machine Learning in Materials Science ; Natural Fiber Reinforced Composites ; Composite Material Mechanics
Authors: Xinwei Wang | Irea Mosquera‐Lois | Aron Walsh
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the semiconductor Sb2Se3. We introduce global defect charge embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict charge-transition levels in quantitative agreement with direct quantum mechanical calculations, at a fraction of the computational cost. Charge (physics) ; Interatomic potential ; Point (geometry) ; Energy (signal processing) ; Electron ; Quantum ; Crystallographic defect ; Semiconductor Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Felipe Hawthorne | Leandro Dias Seixas | James M. Almeida | Cristiano F. Woellner | Raphael M. Tromer
Source: arXiv (Cornell University) (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training-set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. These insights provide a transparent physical interpretation of the learned models and establish a natural pathway toward active learning-driven exploration of complex nanoparticle configurational spaces. Interpretability ; Stability (learning theory) ; Ranking (information retrieval) ; Representation (politics) ; Curse of dimensionality ; Weighting ; Divide and conquer algorithms ; Nanoparticle ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Eunchi Kim | Thomas Kirchartz
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends on underlying assumptions such as the choice of one-dimensional drift-diffusion model and selection of free material parameters. To validate the inferred parameters, we perform predictive modeling of light-intensity-dependent current-voltage (JV) characteristics. Well-known physical effects, including the influence of external resistance and recombination dynamics on illumination-dependent device performance, are reflected in parameter estimation and prediction workflow. We show that correct treatment of dark shunt resistance and emphasizing shifted current (J + Jsc) during fitting enhances prediction accuracy at low to intermediate illumination level. Additionally, we analyze the information content of various input JV curve combinations, demonstrating that including at least one illuminated JV, preferably not under high illumination due to series resistance effects, is critical for reliable parameter estimation and device performance prediction. Estimation theory ; Reliability (semiconductor) ; Bayesian probability ; Model selection ; Model parameter ; Selection (genetic algorithm) ; Estimation ; Curve fitting Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Thin-Film Transistor Technologies
Authors: D. Fioravanti | E. Van der Giessen | F. Maresca
Source: Acta Materialia (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Dislocation glide and cracks in HCP Zn revealed by Atomic Cluster Expansion potential Plasticity and fracture of zinc are complex and highly anisotropic, because of the unusually large c/a ratio of the hexagonal-closed-packed (HCP) structure. Traditional semi-empirical interatomic potentials fail to capture both HCP stability and c/a ratio, thus they are not suitable to study plastic deformation. Recent machine learning-based potentials, including Rapid Artificial Neural Network (RANN) and Moment Tensor Potential (MTP), address this issue, however they struggle to accurately reproduce the Generalized Stacking Fault Energy (GSFE) surfaces, which control dislocation structure and glide. Also, these potentials do not accurately reproduce the basal traction-separation (T-S) curve, which determines crack propagation. In this work, we develop an Atomic Cluster Expansion (ACE) potential for Zn, trained on an extensive and well-converged DFT database. The training data is optimized using the recent HyperActive Learning (HAL) algorithm, enabling to achieve very low RMSE, accurate phonons for molecular dynamics simulations, and good transferability, assessed using model uncertainty measures, to extended defects such as dislocations and cracks. The validated potential is then used to clarify key observations of crack and dislocation slip behaviour in Zn. First, basal slip activates at a critical resolved shear stress below 0.5 MPa, consistent with single crystal experiments. Second, we find that pyramidal II slip is the next easier slip system, and it shows a pronounced compression/tension asymmetry in line with some experimental findings. Finally, we reveal why prismatic slip does not occur in Zn: screw cores are unstable and dissociate into basal dislocations, confirming previous DFT-based calculations. Our work demonstrates how carefully validated machine learning potentials can be used to unravel atomic-scale mechanisms of slip, that are beyond reach of DFT calculations. Dislocation ; Cluster (spacecraft) ; Cluster expansion ; Interatomic potential ; Deformation (meteorology) ; Microstructure Microstructure and mechanical properties ; Copper Interconnects and Reliability ; Metallurgical and Alloy Processes
Authors: Christopher A. Schuh | Changle Li | Sourabh Bhagwan Kadambi | Boopathy Kombaiah | Nutth Tuchinda
Source: Research Square (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Why does Re anti-segregate from the grain boundaries in Ni? Grain boundary ; Ferromagnetism ; Crystallite ; Paramagnetism ; Alloy ; Density functional theory ; Magnetic domain ; Magnetization Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and mechanical properties
Authors: Zhongyao Wang | Taoyong Cui | Jiawen Zou | Shufei Zhang | Bo Yan | Wanli Ouyang | Weimin Tan | Mao Su
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Equivariant Evidential Deep Learning for Interatomic Potentials Uncertainty quantification (UQ) is critical for assessing the reliability of machine learning interatomic potentials (MLIPs) in molecular dynamics (MD) simulations, identifying extrapolation regimes and enabling uncertainty-aware workflows such as active learning for training dataset construction. Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance. Evidential deep learning (EDL) provides a theoretically grounded single-model alternative that determines both aleatoric and epistemic uncertainty in a single forward pass. However, extending evidential formulations from scalar targets to vector-valued quantities such as atomic forces introduces substantial challenges, particularly in maintaining statistical self-consistency under rotational transformations. To address this, we propose \textit{Equivariant Evidential Deep Learning for Interatomic Potentials} ($\text{e}^2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly by representing uncertainty as a full $3\times3$ symmetric positive definite covariance tensor that transforms equivariantly under rotations. Experiments on diverse molecular benchmarks show that $\text{e}^2$IP provides a stronger accuracy-efficiency-reliability balance than the non-equivariant evidential baseline and the widely used ensemble method. It also achieves better data efficiency through the fully equivariant architecture while retaining single-model inference efficiency. Uncertainty quantification ; Extrapolation ; Deep learning ; Covariance ; Inference ; Workflow ; Tensor (intrinsic definition) ; Reliability (semiconductor) ; Equivariant map Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Carlos Bornes | Chiheb Ben Mahmoud | Volker L. Deringer | Christopher J. Heard | Lukáš Grajciar
Source: ArXiv.org (article, 2026-03-23)
Field: Chemistry — Spectroscopy

An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such as zeolites. Synergistic support from computational modelling is vital to interpret experimental spectra, and relate ss-NMR to atomistic models. Nevertheless, computational predictions are hindered by the high expense of calculating magnetic shielding (MS) and electric field gradient (EFG) tensors from first principles. In this work, we leverage a novel tensorial machine learning approach to train a general model for predicting complete NMR tensors. We demonstrate the utility of the approach for a diverse dataset of zeolitic materials and NMR-active nuclei ($^{27}$Al, $^{29}$Si, $^{17}$O, $^{23}$Na and $^{1}$H), predicting all NMR observables to a high degree of precision. These observables are then translated into predictions of the full $^{27}$Al and $^{29}$Si ss-nMR spectra for the exemplary zeolite RTH. Thus, this work opens a pathway to accurate, high-throughput NMR simulation for large-scale and realistic models of chemically complex zeolites. Observable ; Leverage (statistics) ; Zeolite ; Spectral line ; NMR spectra database ; Work (physics) ; Solid-state nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy ; Electromagnetic shielding Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; NMR spectroscopy and applications
Authors: Joanna Zou | Youssef Marzouk
Source: ArXiv.org (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly quantum mechanical methods. Through experiments with hafnium oxide data, we show that DPPs are competitive with existing approaches to constructing compact but diverse training sets by utilizing kernels of molecular descriptors, leading to improved accuracy and robustness in machine learning representations of molecular systems. Our work identifies promising directions to employ DPPs for unsupervised training data curation with heterogeneous or multimodal data, or in online active learning schemes for iterative data augmentation during molecular dynamics simulation. Bottleneck ; Robustness (evolution) ; Training set ; Data point ; Task (project management) ; Point (geometry) ; Interatomic potential ; Process (computing) ; Data curation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum Computing Algorithms and Architecture
Authors: Anoj Aryal | Weiyi Gong | Huta Banjade | Qimin Yan
Source: Open MIND (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

Materials design based on a material-motif network and heterogeneous graphs Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bonding but often do not include local coordination environments across chemically diverse crystals. Recurring structural motifs provide a motif level description of crystalline solids and can serve as interpretable descriptors for structure property learning. To analyze the motif connectivity in materials, we construct a bipartite material motif network from 131,548 Materials Project entries, with materials and motifs as the two node sets. Edges connect materials to their constituent motifs and are weighted by motif distortion, which quantifies the strength of each material motif association. Network connectivity is analyzed to identify motif-defined material clusters that capture recurring local geometries relevant to structure property trends. Most shared motifs act as hubs that connect otherwise disconnected regions of the network, enabling motif guided screening by expanding from known motifs to nearby materials in the same neighborhoods. A network embedding step converts this weighted connectivity into vector representations of materials. Using these motif informed embeddings, property prediction yields a formation energy mean absolute error (MAE) of 0.157 eV per atom and a bandgap MAE of 0.601 eV. These results indicate that motif connectivity provides a compact, interpretable representation that complements existing descriptors for scalable screening and structure property modeling. Motif (music) ; Motif (music) ; Network motif ; Network motif ; Bipartite graph ; Bipartite graph ; Embedding ; Embedding ; ENCODE Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Yunzhi Li | Zibo Wu | Bohan Zhang | Wei-Hai Fang | Zhendong Li
Source: arXiv (Cornell University) (preprint, 2026-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-adapted neural network backflow for strongly correlated electrons Accurately describing strongly correlated electrons in systems such as transition metal complexes requires strict adherence to spin symmetry, a feature largely absent in modern neural-network-based variational wavefunctions. This deficiency can lead to severe spin contamination in simulating systems with near-degenerate spin states. To resolve this limitation, we present a spin-adapted neural network backflow (SA-NNBF) ansatz, formulated in second quantization for fermionic lattice models and ab initio quantum chemistry. Our approach constructs a fully antisymmetric wavefunction by combining a neural-network backflow spatial component with a spin eigenfunction expressed in a sum-of-products form. To address the computational complexity of spin adaptation, we introduce a tensor compression algorithm for spin eigenfunctions, and a more compact wavefunction representation based on the particle-hole duality in second quantization. These advancements enable variational Monte Carlo calculations using SA-NNBF for challenging molecular systems with more than one hundred electrons, including the FeMo-cofactor (FeMoco) in nitrogenase. Applications to prototypical strongly correlated molecules demonstrate that SA-NNBF consistently outperforms standard NNBF with a similar number of parameters. Furthermore, it surpasses the accuracy of the state-of-the-art spin-adapted density matrix renormalization group (SA-DMRG) algorithm for FeMoco with a significantly reduced computational resource. Our work establishes a foundational framework for exploring fully symmetry-preserving neural-network quantum states for interacting fermion problems. Wave function ; Antisymmetric relation ; Quantum ; Density matrix ; Spin (aerodynamics) ; Backflow ; Electron ; Density matrix renormalization group ; Tensor (intrinsic definition) Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies