jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Zhong | Xiwen Li | Xingao Gong | HongJun XIANG
Source: arXiv (Cornell University) (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition of the electrostatic energy, deriving a variationally consistent mapping from the electron density matrix to effective atomic charges. We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation. Benchmarks demonstrate that physics-based long-range corrections are essential: purely data-driven attention mechanisms fail to capture macroscopic electrostatic potentials. Benchmarks on polar ZnO slabs, CdSe/ZnS heterostructures, and GaN/AlN superlattices show two- to threefold error reductions and robust transferability to systems far beyond training sizes, eliminating the characteristic staircase artifacts that plague short-range models in the presence of built-in electric fields. Coulomb ; Hamiltonian (control theory) ; Polar ; Electrostatics ; Matrix (chemical analysis) ; Electric field ; Matrix multiplication ; Basis (linear algebra) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Hristo Nedelchev
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

The Equation Reduction Model: A Comprehensive Framework for Algebraic Synthesis, Analysis, and Reductio This paper presents the Equation Reduction Model (ERM), a pioneering discrete system designed to bridge the gap between continuous algebraic complexity and discrete logical stability. By mapping variables to a minimal state space $\{-1, 0, 1\}$, the model provides a universal "Sieve" for testing equation validity and a "Generator" for synthesizing new algebraic structures. The methodology introduces a bidirectional approach: Forward Mapping for reducing complex equations to their discrete skeleton and Inverse Synthesis for generating balanced mathematical forms from stable discrete states. We provide rigorous validation through permutation symmetry testing (confirming 100% structural invariance), Shannon entropy analysis (demonstrating 91.8% information efficiency), and continuous-to-discrete mapping. The results prove the model's efficiency as a fundamental tool for computational mathematics, automated hypothesis testing, and machine learning pre-processing. The ERM offers a novel, computationally efficient language for understanding algebraic balance and symmetry. "Latest validation includes a 900-point stress test on a continuous energy surface, where the model achieved a 100% integrity score in identifying stable states." Algebraic number ; Algebra over a field ; Reduction (mathematics) ; Entropy (arrow of time) ; Bridging (networking) ; Algebraic equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum Computing Algorithms and Architecture
Authors: Xinyu Chang | Yuchen Xie | Qi Peng | Yu Long | Zhiwan Hu | KangWen YANG
Source: Optics Communications (article, 2026-04-10)
Field: Chemistry — Spectroscopy

Non-resonant background removal for broadband CARS spectroscopy under extreme low-SNR conditions Broadband ; Spectroscopy ; Stray light ; Photothermal spectroscopy ; Reflectivity Spectroscopy and Laser Applications ; NMR spectroscopy and applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sangeeta Yadav | Aditi Sarkar | Saurabh Shivalkar | Fiza Fatima | Siddharth Kumar Thakur | Ankita R. Chaudhary | Sintu Kumar Samanta | Amaresh Kumar Sahoo
Source: Colloids and Surfaces B Biointerfaces (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Recent advances in metal-organic framework-based nanozymes for cancer theranostics driven by synthetic innovation and machine learning design Biocompatibility ; Cancer therapy ; Cancer treatment ; Nanomaterials ; Cancer ; Rational design Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Christian Ebere Enyoh | QINGYUE WANG
Source: Journal of environmental chemical engineering (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Machine learning–guided thermal stability and degradation kinetics of ultra-fine polyamide-derived carbon quantum dots Activation energy ; Kinetics ; Thermal stability ; Kinetic energy ; Thermogravimetric analysis ; Decomposition ; Thermal decomposition ; Degradation (telecommunications) ; Thermal Carbon and Quantum Dots Applications ; Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes
Authors: Feng Tao | Xiaoliang Zhang | Dawei Tang | S. Maruyama | Ya Feng
Source: Nano Letters (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Bose–Einstein Phonon Statistics Explain Drastic Thermal Conductivity Reduction in SWCNT Bundles The drastic reduction in thermal conductivity observed in bundled single-walled carbon nanotubes (SWCNTs) has eluded accurate theoretical prediction. We show that Bose-Einstein phonon statistics are essential to reproduce this effect. Using a machine-learning-driven quantum-statistical framework, combining a neuroevolution potential with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) calculations, we quantitatively predict an 81% reduction for a 5 μm seven-SWCNT bundle, matching experiments. The reduction originates from two distinct mechanisms enabled by quantum statistics: (1) the breaking of rotational symmetry, which directly quenches high-conductivity, symmetry-sensitive phonon modes like the twist mode; and (2) the introduction of abundant intertube phonon modes, which expands the three-phonon scattering phase space and elevates scattering rates universally. These insights, inaccessible to classical simulations, resolve a long-standing experimental-theoretical discrepancy and provide a predictive framework for designing SWCNT-based thermal materials. Phonon ; Thermal conductivity ; Boltzmann equation ; Anharmonicity ; Scattering ; Phonon scattering ; Carbon nanotube ; Reduction (mathematics) ; Thermal Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Carbon Nanotubes in Composites
Authors: Takeru Nakashima | Yuki Tsujikawa | Takahiro Kondo | Takanori Koitaya | I. Matsuda | Yasunobu Ando
Source: The Journal of Physical Chemistry C (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Role of Quasi-One-Dimensional B Chains in the Catalytic Properties of CuB/Cu(111) The electron deficiency of boron leads to rich polymorphism and a high degree of freedom in material design. This research focuses on the newly reported CuB/Cu(111) surface and elucidates its catalytic properties. To assess its catalytic activity, this research investigates elementary steps relevant to CO2 hydrogenation─specifically hydrogen, oxygen, and formate adsorption on this surface─by using a combination of first-principles calculations (OpenMX) and machine learning potentials (Matlantis). Our calculations verify five key points: (i) charge transfer occurs from copper atoms to boron chains, (ii) hydrogen atoms are observed to be adsorbed along the boron chain, (iii) hydrogen hopping between boron-tops shows three-center two-electron bonding in the transition state, (iv) surface oxidation can disrupt the boron chains, and (v) boron chains promote the adsorption of formate. In conclusion, the unique boron chains in CuB/Cu(111) can play a key role in the catalytic reactivity of this surface. Boron ; Catalysis ; Formate ; Adsorption ; Copper ; Reactivity (psychology) Boron and Carbon Nanomaterials Research ; Advanced Chemical Physics Studies ; Organoboron and organosilicon chemistry
Authors: Xu Zhang | C. Li | Mingjian Sun
Source: International Journal of Biological Macromolecules (article, 2026-02-01)
Field: Materials Science — Biomaterials

Gellan gum/ artemisia sphaerocephala Krasch gum composite films integrated with machine learning for real-time freshness assessment Food packaging ; Active packaging ; Gellan gum ; Contact angle ; Shelf life ; Nanocellulose ; Oxygen permeability Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Microplastics and Plastic Pollution
Authors: Jinhwa Park | Hye Young Kim | HyoRin Kim | Seoyoon Shin | Ga-Ae Ryu
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Graph Neural Network-Based Composition Recommendation for Solid Oxide Fuel Cells Using Full-Cycle Data The design of high-performance solid oxide fuel cell (SOFC) materials remains challenging due to the complex coupling between composition, processing conditions, and electrochemical performance. In this study, a data-driven composition design framework based on a graph neural network (GNN) is proposed using full-cycle experimental data. Here, full-cycle data refer to an integrated dataset linking raw material composition, processing conditions (mixing, coating, and heat treatment), and electrochemical performance. A dataset was constructed from LaFeO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub>-based SOFC anode materials measured under different cell configurations and operating temperatures (700–900 °C). Based on this dataset, a GNN-based composition recommendation model was developed, in which compositional variables were represented using a K-nearest neighbor graph structure. The model was trained to recommend suitable anode compositions for given operating conditions specified by the target electrochemical performance. For prospective validation, the proposed model was applied to seven operating conditions, and 21 recommended anode compositions were successfully fabricated and tested. The experimentally measured maximum power densities exhibited an average deviation of 9.35% from the target performance values. These results indicate that the proposed GNN-based framework provides a practical data-driven tool for supporting SOFC composition design under limited experimental data. Anode ; Solid oxide fuel cell ; Graph ; Electrochemistry ; Artificial neural network ; Oxide ; Raw material ; Power density Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Txai Sibley | Elizabeth A. Holm | Kevin G. Field
Source: Computational Materials Science (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Evaluating and enhancing Segment Anything Model transferability for microstructural image analysis in nuclear materials Segmentation approaches in niche materials domains, such as in nuclear applications, can be hindered by limited data for neural network-based methodologies and inconsistent manual or algorithmic methods. In contrast, foundation models like Meta’s Segment Anything Model (SAM) offer potential zero-shot solutions. In this study, we evaluate SAM’s performance on three publicly available datasets relevant to nuclear materials research, spanning multiple microstructural features (Pt nanoparticles, dislocation loops with black dots, and cavities) that were imaged with varying electron microscopy modalities. Results from the SAM ViT-H model show a systematic tendency toward over-segmentation, which creates an opportunity for domain-informed segmentation filter development. Dataset-specific post-processing , including morphological filtering for complex features, enables segmentation results with F1 scores between 0.63 and 0.86, underscoring the role of domain knowledge in optimizing the segmentation transferability of this foundation model. This work identifies the opportunities and challenges of applying foundation models to microstructural image analysis in nuclear materials specifically and recommends the development of standardized filtering workflows to support broader community adoption. • SAM shows dataset-specific microstructural feature detection without fine-tuning. • SAM over-segmentation is corrected with post-processing and morphology filtering. • SAM transferability is assessed across electron microscopy imaging modalities and feature type. • SAM segmentation quality, with or without post-processing, affects property analysis. Transferability ; Image (mathematics) ; Microstructure ; Image processing ; Digital image analysis Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Medical Image Segmentation Techniques
Authors: Yan Chen
Source: Traitement du signal (article, 2026-02-28)
Field: Chemistry — Organic Chemistry

Integrated Multimodal Image Fusion and Deep Learning for 3D Reconstruction and Intelligent Quantitative Analysis of Wood Microstructure High-precision three-dimensional (3D) reconstruction and quantitative analysis of wood microstructure are critical in the fields of wood science and materials characterization, with significant academic value and practical applications in wood property improvement and biomass material development.Conventional single-modality imaging techniques have inherent limitations: Micro-Computed Tomography (Micro-CT) can provide 3D spatial structural information but suffers from insufficient axial resolution, whereas Scanning Electron Microscope (SEM) captures nanoscale ultra-fine details but lacks continuous 3D spatial information.Existing multimodal fusion, 3D reconstruction, and quantitative analysis methods generally face challenges such as incomplete modality fusion, inadequate microstructural detail recovery, and strong reliance on manual annotation, making highprecision analysis difficult to achieve.To address these issues, we propose an integrated approach based on multimodal image fusion and deep learning for precise 3D reconstruction and intelligent quantitative analysis of wood microstructure.A dual-stream attention fusion network is designed, where two encoding branches adapt to differences between modalities.By combining channel-spatial hybrid attention with contrastive learning, cross-modal feature fusion and consistency enhancement are achieved.A 3D super-resolution generative adversarial network (GAN) is constructed, incorporating deep residual channel attention modules and a multi-component joint loss function to overcome the axial resolution limitations of Micro-CT, enabling isotropic high-resolution 3D reconstruction.Furthermore, a weakly supervised multi-task segmentation and quantification network is proposed, which integrates self-supervised pretraining with guided attribute regression, reducing annotation requirements while achieving accurate segmentation and simultaneous multi-parameter quantification of wood microstructures.This end-to-end collaborative framework effectively addresses the core limitations of current methods, providing an efficient and precise imaging tool for wood microstructure analysis and enriching the application of multimodal fusion and 3D reconstruction techniques in materials science. Deep learning ; Fusion ; 3D reconstruction ; Quantitative analysis (chemistry) ; Image fusion ; Pattern recognition (psychology) ; Iterative reconstruction Wood and Agarwood Research ; Advanced Neural Network Applications ; Industrial Vision Systems and Defect Detection
Authors: Qing Tang | Yuping Chen | Xiaoyan Wu | Jingjing Liu | Zhou Huang | Qiong Luo | Luming Meng
Source: Angewandte Chemie (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

HALOM Method: An Efficient and Accurate Neutron Diffraction‐Independent Approach for Determining Hydrogen Locations in Metal Hydride Nanoclusters Abstract Metal hydride nanoclusters play important roles in catalytic transformations and energy conversion. However, accurate localization of hydrogen atoms in these nanoclusters remains a grand challenge. Traditional techniques face inherent limitations that single‐crystal X‐ray diffraction cannot localize hydrides effectively, while neutron diffraction requires large single crystals and has limited accessibility. To address this challenge, we developed an innovative machine‐learning‐assisted hydrogen atom locator for metal hydrides (HALOM) algorithm for rapid and accurate hydrogen position prediction. This approach systematically searches potential hydrogen atom locations using a predefined potential function and refining these locations to minimize the total energy, which operates independently of neutron diffraction data and avoids exhaustive exploration of the potential energy surface (PES). In terms of prediction accuracy, HALOM fully reproduces neutron diffraction (ND)‐identified hydride sites and aligns perfectly with density functional theory (DFT) models, and even yields lower‐energy structures than literature DFT placements in some key systems (e.g., 2.86 eV lower for Cu 81 H 32 ). HALOM also demonstrates superior computational efficiency, which generates low‐energy hydrogen configurations in only seconds, compared to the expensive PES exploration for hours. Overall, HALOM offers an efficient and accurate tool for hydrogen localization in metal hydride nanoclusters to advance their structure‐property research. Nanoclusters ; Neutron diffraction ; Hydride ; Hydrogen ; Hydrogen storage ; Diffraction ; Density functional theory Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Jaekyun Hwang | Taehun Lee | Yonghyuk Lee | Su‐Hyun Yoo
Source: Computational Materials Science (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Fine-tuning bulk-oriented universal interatomic potentials for surfaces: accuracy, efficiency, and forgetting control Accurate prediction of surface energies and stabilities is essential for materials design, yet first-principles calculations remain computationally expensive and most existing interatomic potentials are trained only on bulk systems. Here, we demonstrate that fine-tuning foundation machine learning potentials (MLPs) significantly improves both computational efficiency and predictive accuracy for surface modeling. While existing universal interatomic potentials (UIPs) have been solely trained and validated on bulk datasets, we extend their applicability to complex and scientifically significant unary, binary, and ternary surface systems. We systematically compare models trained from scratch, zero-shot inference, conventional fine-tuning, and multi-head fine-tuning approach that enhances transferability and mitigates catastrophic forgetting. Fine-tuning consistently reduces prediction errors with orders-of-magnitude fewer training configurations, and multi-head fine-tuning delivers robust and generalizable predictions even for materials beyond the initial training domain. These findings offer practical guidance for leveraging pre-trained MLPs to accelerate surface modeling and highlight a scalable path toward data-efficient, next-generation atomic-scale simulations in computational materials science. • Fine-tuning bulk-trained universal interatomic potentials improves surface predictions. • Unary, binary, and ternary surfaces benchmark four training strategies. • Fine-tuning reduces energy and force errors using fewer structures than training from scratch. • Multi-head fine-tuning prevents catastrophic forgetting and maintains high accuracy for both bulk and surfaces. Transferability ; Interatomic potential ; Benchmark (surveying) ; Surface (topology) ; Forgetting ; Ternary operation ; Scalability Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Electron Microscopy Techniques and Applications
Authors: Hosein Alavi-Rad | Mahyar Hassani-Vasmejani | Meysam Bagheri Tagani
Source: Journal of Physics Condensed Matter (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Accelerating quasiparticle physics: A review on the synergy of machine learning and density functional theory for excitons, polaritons, and plasmons Quasiparticles such as excitons, polaritons, and plasmons are central to understanding the optical and electronic properties of modern materials, with profound implications for optoelectronics, renewable energy, and quantum information science. The theoretical investigation of these many-body phenomena heavily relies on computationally intensive methods like Density Functional Theory (DFT) and its extensions. However, the high computational cost and poor scaling of these methods present a significant bottleneck for studying large, complex systems or performing high-throughput material screening. This review discusses the emerging and transformative role of Machine Learning (ML) in overcoming these challenges. We begin by providing a foundational overview of the physics of excitons, polaritons, and plasmons. We then outline the standard DFT-based approaches used to model them, highlighting their capabilities and limitations. The core of this review is dedicated to exploring the multifaceted ways in which ML is being integrated with first-principles calculations. We categorize the role of ML into several key paradigms: (i) acting as surrogate models to accelerate the prediction of quasiparticle properties, (ii) building size-transferable Hamiltonians to bridge nano and meso-scales, (iii) classifying complex phases and patterns in many-body systems, and (iv) solving the inverse problem of determining system parameters from experimental observables. By surveying recent cutting-edge research, we present case studies demonstrating how this synergy is providing unprecedented insights into quasiparticle dynamics and properties. Finally, we discuss the current challenges and present a forward-looking perspective on how the continued fusion of ML and quantum mechanics is set to redefine the landscape of materials discovery and condensed matter physics. Quasiparticle ; Bottleneck ; Plasmon ; Density functional theory ; Key (lock) ; Set (abstract data type) Machine Learning in Materials Science ; Strong Light-Matter Interactions ; 2D Materials and Applications
Authors: An-Jun Liu | Bryan K. Clark
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Neural network backflow for ab-initio solid calculations Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but face issues with scalability. In this work, we successfully extend the neural network backflow (NNBF) approach to ab-initio solid-state materials. Building on our scalable optimization framework for molecules [Liu et al., PRB 112, 155162 (2025)], we introduce a two-stage pruning strategy to manage the massive configuration space expansions: by utilizing a computationally cheap, physics-informed importance proxy, we devote exact NNBF amplitude evaluations solely to the most relevant determinants, significantly improving optimization efficiency, energy estimation, and convergence. Our framework achieves state-of-the-art accuracy across diverse solid-state benchmarks. For 1D hydrogen chains, NNBF matches or surpasses DMRG and AFQMC, remains robust in strongly correlated bond-breaking regimes where coupled-cluster methods fail, and smoothly extrapolates to the thermodynamic limit. We further demonstrate its scalability by computing ground-state potential energy curves for 2D graphene and 3D silicon. Finally, ablation studies validate the computational savings of our pruning strategy and highlight the dependence of the NNBF energies on basis sets. Scalability ; Artificial neural network ; Floquet theory ; Pruning ; Quantum ; Backflow ; Wave function ; Energy (signal processing) ; Optimization problem Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies