jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Zhang | Yong Zhong | Nhat Huy Tran | Shuyi Li | Kyuho Lee | Yonghun Lee | Tiffany C. Wang | Harold Y. Hwang | Zhi-Xun Shen | Chunjing Jia
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the quantitative extraction of Hamiltonian parameters from these high-dimensional spectra remains a significant challenge, currently relying on labor-intensive, expert-dependent analysis rather than standardized workflows. Here, we introduce a deep learning framework based on implicit neural representations to accelerate the retrieval of Hamiltonian parameters in two types of transition-metal oxides: perovskite nickelates and manganites. Our approach outperforms traditional analytical fitting procedures, yielding superior agreement with experimental Fermi surface topologies and energy-momentum dispersions. This work highlights the potential of deep learning tools to bridge the gap between theory and experiment, paving the way for high-throughput, autonomous discovery pipelines in quantum materials. Electronic structure ; Hamiltonian (control theory) ; Photoemission spectroscopy ; Artificial neural network ; Spectroscopy ; Angle-resolved photoemission spectroscopy ; Quantum ; Deep learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum many-body systems
Authors: Linh La | Yugam Aggarwal | Truyen Tran | Svetha Venkatesh | Sherif Abdulkader Tawfik
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

When Three-Body Interactions Become Redundant: Scaling Graph Neural Network Potentials Graph neural networks (GNN) are currently the vehicle for large scale molecular dynamics at quantum accuracy, but their practical scalability remains limited by the rapidly increasing size of graph representations required for large atomistic systems. In particular, the construction of higherorder interactions such as triplets leads to substantial memory and computational overhead during both training and inference. In this work, we investigate strategies to improve the scalability of GNNbased potentials while preserving predictive accuracy. We show that pruning triplet interactions during training and inference has minimal impact on regression accuracy for formation energies, band gaps, and elastic moduli. For molecular dynamics simulations, we find that pruning triplets during inference preserves structural fidelity while significantly reducing computational cost. This allows for upscaling of GNNs to large structures (beyond 30k atoms). To further extend scalability, we introduce a spatial chunking scheme that decomposes large atomistic systems into padded subgraphs, enabling efficient evaluation of forces for systems containing hundreds of thousands of atoms. Using this approach, we perform molecular dynamics simulations of a 400,000-atom LGPS supercell on a single GPU using the M3GNet and DIEP models. Our results demonstrate that fully trained manybody GNN potentials contain significant redundancy that can be exploited to improve scalability, providing a practical pathway toward large-scale atomistic simulations with universal machinelearning potentials. Scalability ; Inference ; Graph ; Scaling ; Redundancy (engineering) ; Pruning ; Computational complexity theory ; Encoding (memory) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Balamurugan Deivendran | Vishnu Swaroopji Masampally | Utkarsh Sinha | Venkataramana Runkana
Source: Industrial & Engineering Chemistry Research (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Modeling and Optimization of a Chemical Vapor Deposition Reactor for Depositing 2D Transition Metal Dichalcogenides Two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising materials for electronics, energy storage, and solar energy systems. Chemical vapor deposition (CVD) offers a scalable bottom-up route for synthesizing high-quality TMD films; however, optimization of operating conditions is challenging due to strongly coupled mass, momentum, and heat transfer phenomena. To address these complexities, a detailed CFD model incorporating transport equations and reaction kinetics is developed and validated by using published experimental data. A systematic design of experiments is then applied to generate a data set linking the operating temperature, pressure, and reactant flow rates to key film characteristics, including the deposition rate and uniformity. Surrogate machine learning models have been developed to reduce the computational cost associated with CFD simulations. Among several algorithms evaluated, Extreme Gradient Boosting (XGBoost) provides the highest predictive accuracy for MoS2 film growth behavior. Model interpretability is further enhanced using SHapley Additive exPlanations (SHAP), which reveal how reactor variables interact and contribute to deposition outcomes. Finally, the XGBoost model is integrated with the nondominated sorting genetic algorithm II (NSGA-II) to simultaneously maximize film thickness and uniformity in a cold-wall horizontal CVD reactor. The resulting optimal operating windows are visualized through parallel coordinate plots, providing practical guidelines for improving MoS2 film quality and enhancing CVD process efficiency. Interpretability ; Chemical vapor deposition ; Deposition (geology) ; Scalability ; Computational fluid dynamics ; Transition metal ; Sorting ; Process (computing) ; Heat transfer ; Mass transfer 2D Materials and Applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Tsung-Hsuan Yang | Jianping Zhao | Peter Ventzek
Source: Applied Surface Science (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Adsorption and reaction pathways of borazine on defected and hydrogenated boron nitride surfaces: a DFT study Borazine ; Boron nitride ; Adsorption ; Boron ; Nitride Boron and Carbon Nanomaterials Research ; Adsorption and biosorption for pollutant removal ; Plant Micronutrient Interactions and Effects
Authors: Chao Deng | Bo CHEN | Jiayu Dai
Source: SHILAP Revista de lepidopterología (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Translational-Rotational Decoupling Dynamics of High-Pressure Liquid Water under Quasi-Isentropic Compression The ubiquitous presence of water, from Earth and planetary bodies to interstellar space, renders its phase behavior across an extensive thermodynamic range fundamental to understanding key scientific phenomena such as biochemical reactions, climate dynamics, and planetary evolution. Nevertheless, although liquid water exhibits distinct anomalous behaviors under extreme pressure, research has been hampered by experimental limitations and computational complexity, resulting in scarce atomic-scale data and hindered understanding of its microscopic mechanisms. To address this, our study employed a deep learning interaction model trained on high-precision ab initio data. Employing molecular dynamics simulations, we compressed liquid water isentropically to tens of thousands of atmospheres. Systematic analysis of its structural and dynamic properties revealed that elevated pressure significantly disrupts the inherent tetrahedral local coordination of water molecules, enhancing their rotational mobility. Conversely, translational mobility is severely suppressed in this highly condensed state. The mean squared displacement of water molecules under high pressure exhibits a characteristic three-stage behavior which is typical of glassy systems: ballistic transport, a plateau, and diffusion. Macroscopically, this reduced translational mobility manifests as a substantial increase in shear viscosity. A critical finding is that, unlike supercooled water under ambient pressure where translational and rotational motions are strongly coupled, liquid water under dynamic high pressure exhibits an intrinsic decoupling of these motions. The insights from this work are expected to offer significant microscopic understanding for crucial scientific questions, including the response of materials under dynamic loading and the solidification of metastable liquids. Supercooling ; Decoupling (probability) ; Metastability ; Molecular dynamics ; Liquid water ; Ambient pressure ; Rotational dynamics ; Water model ; Work (physics) Material Dynamics and Properties ; High-pressure geophysics and materials ; Phase Equilibria and Thermodynamics
Authors: K. Vijayakumar | S. Gopinath | U. Arun Kumar | Raman Dhivya | N. Saranya
Source: Sensing and Imaging (article, 2026-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid MXene–Graphene Terahertz Metasurface with Copper-Assisted Plasmonic Confinement for Broadband, Ultra-Sensitive Salinity Sensing Using Machine Learning Multiphysics ; Plasmon ; Terahertz radiation ; Resonator ; Refractive index ; Sensitivity (control systems) ; Figure of merit ; Metamaterial ; Convolutional neural network Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Yu Zhang | Yong Zhong | Nhat Huy Tran | Shuyi Li | Kyuho Lee | Yonghun Lee | Tiffany C. Wang | Harold Y. Hwang | Zhi-Xun Shen | Chunjing Jia
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the quantitative extraction of Hamiltonian parameters from these high-dimensional spectra remains a significant challenge, currently relying on labor-intensive, expert-dependent analysis rather than standardized workflows. Here, we introduce a deep learning framework based on implicit neural representations to accelerate the retrieval of Hamiltonian parameters in two types of transition-metal oxides: perovskite nickelates and manganites. Our approach outperforms traditional analytical fitting procedures, yielding superior agreement with experimental Fermi surface topologies and energy-momentum dispersions. This work highlights the potential of deep learning tools to bridge the gap between theory and experiment, paving the way for high-throughput, autonomous discovery pipelines in quantum materials. Electronic structure ; Hamiltonian (control theory) ; Photoemission spectroscopy ; Artificial neural network ; Spectroscopy ; Angle-resolved photoemission spectroscopy ; Quantum ; Deep learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum many-body systems
Authors: Tianjiao Gao | Hairui Zhou | Yuchen Zhou | Zhiqi Chen | Bowen Liu | Lanze Xiao | Minliang Lai | Xiaolin Liu | Lin Peng | Jia Lin
Source: Applied Physics Letters (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

A physically informed and transferable framework for predicting cubic crystal lattice constants The lack of transferable machine learning (ML) models across different material classes remains a fundamental obstacle to the predictive design of heterostructures and advanced materials. This is because conventional ML approaches, trained on single material systems, often learn system-specific, and hence nontransferable, feature–property relationships. Here, we break this paradigm by developing a feature selection strategy that explicitly prioritizes feature consensus—the agreement of descriptors across structurally distinct material families. Using lattice constant (LC) prediction as a case study, we validate this approach on cubic perovskites and spinels, which share chemical similarities but differ in geometry. By creating a unified model that accurately predicts LC for both families, we demonstrate its transferability. Crucially, through symbolic regression, we distill the consensus feature set into a simple, interpretable analytical formula that captures the underlying physics of LCs. This physically intuitive formula achieves accuracy comparable to black-box ML models, revealing that the LC is governed by a balanced interplay between ionic packing and bond coordination. Our work presents a framework that transcends system-specific models and extends to the prediction of key properties such as formation energy, demonstrating excellent cross-property transferability and opening a pathway for structure–property prediction in heterostructure and multicomponent materials design. Transferability ; Lattice (music) ; Lattice constant ; Heterojunction ; Feature (linguistics) ; Set (abstract data type) ; Feature selection ; Crystal structure prediction ; Ionic bonding Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Fei Shuang | Daniel Ocampo | Reza Namakian | Arman Ghasemi | Poulumi Dey | Wei Gao
Source: Communications Materials (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Kinetics of vacancy-assisted reversible phase transition in monolayer MoTe2 Phase transitions between the 2H and $${1{{{\rm{T}}}}}^{{\prime} }$$ phases in monolayer MoTe2 show promise for many semiconductor applications, yet the atomistic pathways for nucleation and growth remain unclear. Here, we investigate these kinetic mechanisms using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of $${1{{{\rm{T}}}}}^{{\prime} }$$ is initiated by the coalescence of neighboring Te monovacancies into mobile divacancies, which interact with other Te vacancies to form small triangular $${1{{{\rm{T}}}}}^{{\prime} }$$ islands. Islands then grow by consuming pre-existing vacancies, when depleted, by absorbing migrating divacancies; beyond a critical size they can advance without vacancies but with a higher barrier. Removing the stimulus triggers a rapid, diffusionless $${1{{{\rm{T}}}}}^{{\prime} }$$ to 2H transition that reorganizes Te vacancies into three spoke-like lines at the island center. Subsequent cyclic and reversible transitions between $${1{{{\rm{T}}}}}^{{\prime} }$$ and 2H are diffusionless, requiring only mild stimuli and no additional vacancies. Phase transitions between the 2H and 1T’ phases in monolayer MoTe2 show promise for semiconductor applications, yet the atomistic pathways for nucleation and growth remain unclear. This paper investigates these kinetic mechanisms using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data. Nucleation ; Monolayer ; Phase transition ; Kinetic energy ; Coalescence (physics) ; Interatomic potential ; Kinetics 2D Materials and Applications ; Topological Materials and Phenomena ; Advanced Thermoelectric Materials and Devices
Authors: Adam Hložný | Jozef Haniš | M. Gmitra | Marko Milivojević
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors Identifying the pairing symmetry in unconventional superconductors is essential for reliably characterizing their superconducting states and for enabling their integration into realistic quantum devices. Here, we introduce a machine-learning-guided strategy to determine pairing symmetry from quasiparticle interference (QPI) data, which integrates first-principles calculations, tight-binding modeling, and symmetry-based classification of the superconducting pairing function. We demonstrate the approach on monolayer NbSe2 as an experimentally accessible probe of superconductivity in real materials, within a single scalar-impurity Bogoliubov-de Gennes framework. Our analysis shows that the QPI-to-parameter inverse problem can be solved with high accuracy for most superconducting pairing channels in this setting, indicating that QPI carries rich, learnable information about the superconducting gap structure. Taken together, these results demonstrate that machine-learning-assisted QPI analysis provides a promising pathway for precise learning of superconducting pairing functions in quantum materials. Pairing ; Superconductivity ; Quasiparticle ; Symmetry (geometry) ; Homogeneous space ; Ising model ; Topology (electrical circuits) Machine Learning in Materials Science ; Iron-based superconductors research ; Physics of Superconductivity and Magnetism
Authors: Yu. D. Fomin
Source: Open MIND (preprint, 2026-02-27)
Field: Materials Science — Materials Chemistry

The origin of complex behavior of liquid carbon: an insight from computer simulation In the present paper we perfomrm molecular dynamics simulation of liquid carbon with a machine-learning potential GAP-20. We show that within the framework of this model carbon demonstrates a relatively low critical temperature, which can affect the results of experimental measurements of melting point of graphite. Molecular dynamics ; Point (geometry) ; Carbon fibers ; Dynamics (music) ; Melting point ; Complex system Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties
Authors: Sherman W. Marcus | Natanel Nissan | Vinay K. Killamsetty | Ravi Kant Yadav | Dan Raviv | Giryes, Raja | Ariel Epstein
Source: arXiv (Cornell University) (article, 2026-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part I - Field-based Semianalytical Synthesis Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metallic sheets, whose design often requires time-consuming full-wave optimization. Here, we extend analytical models originally developed for sparse loaded-wire metagratings to accurately describe densely packed Jerusalem-cross meta-atoms embedded in standard printed circuit board (PCB) dielectric stacks. The model captures both near- and far-field coupling within and between layers, enabling efficient prediction of the dual-polarized response. Using this framework, we identify highly transmissive meta-atoms whose phase is controlled by the leg lengths of the Jerusalem crosses (microscopic design stage). This (phase)-(leg-length) "lookup table" allows rapid synthesis of Huygens' metasurfaces (macroscopic design stage), demonstrated through a full-wave-validated metalens exhibiting low-reflection beam manipulation. Notably, we implement a judicious scaling method to further extend the model to predict wideband meta-atom responses. In the companion paper (Part II), a hybrid machine-learning approach leverages this semianalytical framework to enhance accuracy without requiring the conventional exhaustive full-wave training, enabling ultrafast inverse design across the full parameter space. Overall, the presented methodology -- the standalone semianlytical scheme (Part I) and the machine-learning enhanced version (Part II) -- establishes an effective open-source toolkit for versatile, rapid, and highly accurate synthesis of fabrication-ready dual-polarized transmissive Huygens' meta-atoms and metasurfaces. Planar ; Microwave ; Coupling (piping) ; Inverse ; Wideband ; Scaling ; Design tool ; Circuit design ; Printed circuit board Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Thijs Rudolf ter Rele | René van Roij | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Machine-learned many-body potentials for charged colloids reveal gas–liquid spinodal instabilities only in the strong-coupling regime of primitive models Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like-charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency, where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas-liquid and gas-solid phase separation observed in primitive-model simulations at low charge numbers. Extending the ML-based simulations to higher valencies, where primitive-model simulations become prohibitively slow, also reveals like-charge attractions and gas-liquid spinodal instabilities, however, only in the regime of strongly coupled electrostatic interactions and not in the weakly coupled Poisson-Boltzmann regime of the experimental observations of colloidal like-charge attractions. Spinodal ; Colloid ; Colloidal particle ; Spinodal decomposition ; Phase (matter) ; Electrolyte ; Electrostatics ; Charge (physics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electrostatics and Colloid Interactions
Authors: Jo S. Kurian | Ankit Mahajan | Sandeep Sharma
Source: arXiv (Cornell University) (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Nuclear gradients from auxiliary-field quantum Monte Carlo and their application in geometry optimization and transition state search In this article, we present a method for computing accurate and scalable nuclear forces within the phaseless auxiliary-field quantum Monte Carlo (AFQMC) framework. Our approach leverages automatic differentiation of the energy functional to obtain nuclear gradients at a computational cost comparable to that of energy evaluation. The accuracy of the method is validated against finite difference calculations, showing excellent agreement. We then explore several machine learning (ML) strategies for learning noisy AFQMC data. These ML potentials are subsequently used to perform geometry optimizations and nudged elastic band (NEB) calculations, successfully identifying the transition state of the formamide-formimidic acid tautomerization. The resulting transition state geometry and barrier heights are in close agreement with coupled-cluster reference values. This work paves the way for highly accurate geometry optimization, molecular dynamics, or reaction path calculations. Quantum Monte Carlo ; Energy minimization ; Monte Carlo method ; Work (physics) ; State (computer science) ; Path (computing) ; Quantum ; Energy (signal processing) Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced NMR Techniques and Applications