jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Su | Jin Yan | Meiyang Yu | Chen Shen | Yuhao Fu | Tao Zhou | Lijun Zhang
Source: Physical Review Materials (article, 2026-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unveiling thermal transport properties of defective <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>β</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mi mathvariant="normal">G</mml:mi> </mml:mrow> <mml:msub> <mml:mi mathvariant="normal">a</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:msub> <mml:mi mathvariant="normal">O</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> through machine learning potentials The intrinsically low thermal conductivity of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mi>β</a:mi> <a:mrow> <a:mo>−</a:mo> <a:mi mathvariant="normal">G</a:mi> </a:mrow> <a:msub> <a:mi mathvariant="normal">a</a:mi> <a:mn>2</a:mn> </a:msub> <a:msub> <a:mi mathvariant="normal">O</a:mi> <a:mn>3</a:mn> </a:msub> </a:mrow> </a:math> poses a major challenge for high-power and high-frequency electronic applications. This issue becomes more severe in the presence of defects, which further suppress heat dissipation, exacerbate self-heating, and degrade device performance. In this work, we develop an efficient machine learning potential (MLP) based on a deep neural network model for accurately describing pristine and point-defective <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:mrow> <e:mi>β</e:mi> <e:mrow> <e:mo>−</e:mo> <e:mi mathvariant="normal">G</e:mi> </e:mrow> <e:msub> <e:mi mathvariant="normal">a</e:mi> <e:mn>2</e:mn> </e:msub> <e:msub> <e:mi mathvariant="normal">O</e:mi> <e:mn>3</e:mn> </e:msub> </e:mrow> </e:math> . Using equilibrium molecular dynamics (EMD) simulations, we quantify the reduction in thermal conductivity induced by intrinsic point defects. Our results demonstrate that Ga interstitials exert the strongest suppression, decreasing the thermal conductivity by 79.5%. Moreover, Ga-related defects generally have a more pronounced impact than O-related defects. This behavior originates from the enhanced vibrations of weakly bonded Ga atoms, increased phonon anharmonicity, and a substantial reduction in the group velocity of low-frequency phonons. These results provide atomic-level insight into thermal transport in defective <i:math xmlns:i="http://www.w3.org/1998/Math/MathML"> <i:mrow> <i:mi>β</i:mi> <i:mrow> <i:mo>−</i:mo> <i:mi mathvariant="normal">G</i:mi> </i:mrow> <i:msub> <i:mi mathvariant="normal">a</i:mi> <i:mn>2</i:mn> </i:msub> <i:msub> <i:mi mathvariant="normal">O</i:mi> <i:mn>3</i:mn> </i:msub> </i:mrow> </i:math> , offering guidance for thermal-management strategies and establishing a general workflow for investigating thermal physics in complex semiconductor materials. Thermal conductivity ; Phonon ; Reduction (mathematics) ; Thermal ; Workflow ; Semiconductor ; Point (geometry) ; Artificial neural network Ga2O3 and related materials ; Machine Learning in Materials Science ; GaN-based semiconductor devices and materials
Authors: Yajie Ji | Jiuyang Liang | Zhenli Xu
Source: The Journal of Chemical Physics (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Accurate learning of long-range interatomic potentials by coupling Cartesian atomic cluster expansion and sum-of-Gaussians neural networks Accurately capturing long-range interactions is critical for molecular dynamics simulations based on machine learning interatomic potentials. We recently proposed the sum-of-Gaussians neural network (SOG-Net), which learns long-range energy contributions directly from energy and force data such that the long-range tail of different decay rates can be well fitted. In this work, we incorporate the SOG-Net with a short-range descriptor of the Cartesian atomic cluster expansion, resulting in the CACE-SOG model, to show that the SOG-Net is a general module that can be coupled with different short-range descriptors. We also study new technical developments in the SOG-Net, including improved extrapolation accuracy, handling of different charge states, and faster convergence. We evaluated the CACE-SOG model across a diverse set of systems, including molecular dimers, aqueous salt solutions, charged ionic clusters, and liquid-vapor and Pt(111) interfacial water systems, and compared it with the CACE-based latent Ewald summation and the CACE-only methods. These results demonstrate that the SOG-Net is promising for accurately learning long-range interatomic interactions. Extrapolation ; Artificial neural network ; Interatomic potential ; Molecular dynamics ; Coupling (piping) ; Cluster (spacecraft) ; Energy (signal processing) ; Cartesian coordinate system Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Jigyasa Nigam | Tess Smidt | Geneviève Dusson
Source: arXiv (Cornell University) (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

Machine learning electronic structure and atomistic properties from the external potential Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most existing approaches learn a direct map from molecular geometries, typically represented as graphs or encoded local environments, to molecular properties or use ML as a surrogate for electronic structure theory by targeting quantities such as Fock or density matrices expressed in an atomic orbital (AO) basis. Inspired by the Hohenberg-Kohn theorem, in this work, we propose an operator-centered framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input. From this operator, we construct hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors. At the same time, the matrix-valued nature of the external potential provides a natural connection to equivariant message-passing neural networks. In particular, we show that successive products of the external potential provide a scalable route to equivariant message passing and enable an efficient description of long-range effects. We demonstrate that this approach can be used to model molecular properties, such as energies and dipole moments, from the external potential, or learn effective operator-to-operator maps, including mappings to the Fock matrix and the reduced density matrix from which multiple molecular observables can be simultaneously derived. Bottleneck ; Electronic structure ; Scalability ; Equivariant map ; Construct (python library) ; Dipole ; Artificial neural network ; Observable Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Ji-Young Choi | Yun‐Jeong Choi | Sung Jin Park | Mi-Ai Lee | Sung Hee Park
Source: LWT (article, 2026-03-11)
Field: Chemistry — Analytical Chemistry

Feature-optimized ResNet–BiLSTM model with hyperspectral imaging for non-destructive quality prediction of salted kimchi cabbage processed by various salting methods Short-wave infrared hyperspectral imaging (HSI) is a rapid, non-destructive approach for tracking physicochemical variations in food materials. Here, we present a novel framework integrating HSI with hybrid deep learning and spectral feature selection to evaluate the salt content and firmness of salted kimchi cabbage (SKC) prepared using three distinct salting methods (rotary agitation, vacuum, and combined rotary agitation–vacuum). To our knowledge, this is the first study to systematically compare the effects of multiple salting processes on SKC quality using a data-driven approach. The proposed residual neural network-bidirectional long short-term memory (ResNet–BiLSTM) architecture effectively captured spatial–spectral and sequential correlations, while uninformative variable elimination (UVE) and the successive projection algorithm (SPA) minimized data redundancy. The hybrid UVE–SPA–ResNet–BiLSTM achieved superior predictive performance (R p 2 > 0.98) with an 87% reduction in spectral variables, outperforming conventional regression models. Microstructural and elemental analyses further validated that ion redistribution and tissue deformation influenced reflectance patterns. Overall, the proposed framework provides a scalable and interpretable tool for real-time quality control in salted vegetable processing, advancing the practical application of deep learning–based HSI in food manufacturing. • Salting methods altered physicochemical traits, microstructure, and elemental contents. • ResNet-BiLSTM with UVE–SPA achieved the best performance (R p 2 > 0.98, RPD > 7.9). • The hybrid model reduced data volume by over 87% compared with full-band models. • Feature-selected hybrid deep learning outperformed regression models. • HSI enabled robust quality prediction across diverse salting treatments in SKC. Hyperspectral imaging ; Salting ; Quality (philosophy) ; Pattern recognition (psychology) ; Marination Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Jiang Jiale | Kang Yongwang | LUO Jun | Wang Zhimao | LI Gang | LIU Changkui | Nan Li
Source: SHILAP Revista de lepidopterología (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Research progress in metallic materials solidification based on synchrotron radiation imaging technology Solidification is crucial in the preparation of metal materials. The solidification process directly determines the microstructure and macroscopic properties of metal materials. When a metal is transformed from a liquid to a solid state，factors such as the formation and growth of grains，solidification rate，temperature gradient，and alloy composition lead to different microstructures，ultimately defining the final microstructure of the metal. Variations in these factors not only determine the mechanical properties of the metal but can also cause defects such as grain boundary defects， gas pores， and inclusions， etc.，thereby affecting its overall quality and service performance. Due to its high penetration and high spatiotemporal resolution，synchrotron radiation enables the real-time observation and tracking of microstructural evolution during metal solidification， leading to a deeper understanding of the underlying mechanisms. This paper reviews the latest progress of synchrotron radiation imaging technology in the study of the solidification process of metallic materials at home and abroad，with a focus on its application and research achievements in crystal nucleation and growth，the formation mechanism of solidification defects，and rapid solidification （welding and additive manufacturing）. By combining new methods such as phase field simulation and machine learning，researchers have made significant progress in grain refinement mechanisms，the formation laws of pores and hot tearing，and the control of rapid solidification modes. Finally，it is pointed out that improving the temporal and spatial resolution of synchrotron radiation，conducting multi-scale coupling characterization，integrating experiments with numerical simulations，and introducing data-driven intelligent analysis methods are important development directions for in-situ studies of the solidification process in the future. This will provide a more solid theoretical basis and technical support for the design of advanced materials and process optimization. Synchrotron radiation ; Nucleation ; Microstructure ; Synchrotron ; Grain boundary ; Phase (matter) ; Process (computing) ; Alloy Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Hossein Tahmasbi | Andreas Knüpfer | Thoams D. Kühne | Hossein Mirhosseini
Source: Research Square (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems Benchmarking ; Maxima and minima ; Universality (dynamical systems) ; Benchmark (surveying) ; Decoupling (probability) ; Generalization ; Chemical space ; Interatomic potential Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Quantum many-body systems
Authors: Luca Thiede | Abdulrahman Aldossary | Andreas Burger | Jorge Arturo Campos-Gonzalez-Angulo | Ning Wang | Alexander Zook | Melisa Alkan | Kouhei Nakaji | Taylor L. Patti | Jérôme F. Gonthier | Vakili, Mohammad Ghazi | Alán Aspuru-Guzik
Source: arXiv (Cornell University) (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-equilibrium geometries, despite being trained only on small equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches. Generalization ; Atomic orbital ; Complement (music) ; Wave function ; Set (abstract data type) ; Molecular orbital ; Density functional theory ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Jinhyun Choi | H.-J. Lee | Junsu Kim | Miju Park | Seungjoon Sun | Gye‐Hyeon Kim | Myung‐Ho Kwon | Hyeong‐Ryeol Park | Changhee Sohn
Source: Advanced Science (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

AI‐Optimized Vanadium Oxide Multilayers for More Than 20‐fold Enhancement in Bolometric Performance Phase-transition materials such as vanadium dioxide (VO<sub>2</sub>) inherently exhibit non-linear and hysteretic behavior, which limits their applicability in devices like infrared bolometric sensors that require linear and non-hysteretic responses. To circumvent this issue, nonstoichiometric VO<sub>x</sub> has been widely used in infrared bolometers despite its degraded phase transitions and resultant lower temperature coefficient of resistance (TCR) compared to stoichiometric VO<sub>2</sub>. Achieving both a high TCR and a linear, non-hysteretic response has therefore remained a major bottleneck in advancing microbolometer technology. In this study, we present a multilayer approach using machine-learning-optimized W<sub>x</sub>V<sub>1-x</sub>O<sub>y</sub> thin films with varying doping ratios to address these challenges. By stacking layers with different W doping levels and employing genetic algorithm optimization, we achieve tailored linear/flat TCR profiles and significantly reduced hysteresis. These multilayer systems simultaneously achieve a high TCR and low electrical noise even under complementary metal-oxide semiconductor (CMOS)-compatible growth conditions, resulting in a universal bolometric performance 23.6 times greater than that of commercial materials. This work demonstrates a general methodology for achieving both a large and linear response to external stimuli, which can be widely adopted not only for microbolometers but also for other technologies. Bolometer ; Microbolometer ; Stacking ; Temperature coefficient ; Resistive touchscreen ; Bottleneck ; Noise (video) ; Infrared ; Doping Transition Metal Oxide Nanomaterials ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Chikashi Shinagawa | So Takamoto | Daiki Shintani | Yong-Bin Zhuang | Yuta Tsuboi | Katsuhiko Nishimra | Kohei Shinohara | Shigeru Iwase | Yuta Tanaka | Ju Li
Source: ArXiv.org (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional theory (DFT). However, most existing uMLIPs are trained on Perdew--Burke--Ernzerhof (PBE) generalized gradient approximation (GGA) data and are therefore fundamentally limited by PBE-level accuracy. In this paper, we argue that better zero-shot predictions versus experiments must be an explicit design target for uMLIPs and present PFP v8, a uMLIP available on the Matlantis service that overcomes the inherent limitations of the PBE functional by being trained to reproduce the regularized-restored strongly constrained and appropriately normed (r2SCAN) meta-GGA potential-energy surface across a wide range of chemical domains. Without requiring domain-specific fine-tuning, PFP v8 delivers systematically improved agreement with experimental data or high-accuracy references for crystals, molecules, and surfaces, outperforming PBE-based DFT calculations. Crucially, in long-time molecular dynamics simulations that are computationally impractical with DFT, PFP v8 predicts melting points with an average error of approximately 130 K, halving the error relative to PBE-trained models. These results establish that uMLIPs can move beyond the limitations of their training approximations and achieve substantially improved agreement with experiment across diverse chemical domains, further narrowing the gap between simulation and reality. Range (aeronautics) ; Experimental data ; Density functional theory ; Surface (topology) ; Approximation error ; Training set Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Wei Han | Hang Zheng | Ke Huang | Chi‐Sing Lee
Source: Research Square (preprint, 2026-01-14)
Field: Materials Science — Biomaterials

Discovery of β-Sheet Peptide Assembly Codes via an Experimentally Validated Predictive Computational Platform Pipeline (software) ; Scalability ; Sequence (biology) ; Metric (unit) ; Stacking ; Encoding (memory) ; Limiting ; Peptide Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Alzheimer's disease research and treatments
Authors: Shuaicheng Zhu | Keyan Zhu | Hongtao Zhang | Xingzhi Zhao | Yihuan Cao | Huadong Fu | Jianxin Xie
Source: Materials & Design (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Feature screening and sample synthesis for enhancing generalization predictive performance on imbalanced small data: amorphous alloy design • A general machine learning strategy for imbalanced small-sample materials data. • B-SMOTE data augmentation reveals an optimal imbalance ratio. • Physically informed feature screening enhances interpretability and prediction. • Glass-forming ability predicted across wide amorphous alloy composition spaces. • Two new bulk metallic glasses designed and experimentally validated. In advanced materials systems, sample scarcity and class imbalance severely limit the generalization performance of data-driven models. To address this challenge, a strategy that combines data augmentation with dimensionality reduction of physicochemical features was proposed using amorphous alloys as a representative case. This approach enables high-precision prediction of glass-forming ability (GFA) in a high-dimensional compositional space and validates the feasibility of new alloy design. Specifically, we use the Borderline Synthetic Minority Oversampling Technique (B-SMOTE) to balance the dataset, while screening physicochemical factors to identify key alloy factors. A “key alloy factor–performance” model is then developed to predict GFA across the compositional space. The results demonstrate that a 2:1 majority-to-minority class ratio in the augmented data yields optimal model generalization. Further screening identifies ten critical alloy factors, and a support vector machine (SVM) model built on these factors achieves 89.09% accuracy on the test set, significantly outperforming existing conventional experience-driven or empirical-rule-based approaches. Based on this model, two bulk metallic glasses (Zr 38 Cu 28 Ag 7 Al 7 Be 20 and Zr 46 . 75 Cu 25 . 5 Al 12 . 75 Ni 11 Sc 4 ) are designed and successfully synthesized; their experimental characterization agrees well with the predictions. This study offers a new approach for addressing imbalanced small-sample problems in materials science and improving the reliability of predictions across broad composition spaces. Generalization ; Feature (linguistics) ; Sample (material) ; Pattern recognition (psychology) ; Amorphous metal Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; SAS software applications and methods
Authors: Giovanni Nicodemo | Christine Guéneau | Paul C.M. Fossati | G. Zullo | D. Pizzocri | Lelio Luzzi
Source: Computational Materials Science (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Accelerating thermochemistry in MOX fuel modelling via neural-network surrogates of CALPHAD calculations: Integration into SCIANTIX Thermochemistry ; MOX fuel ; CALPHAD ; Fuel cells ; Phase diagram Nuclear Materials and Properties ; Phase Equilibria and Thermodynamics ; Nuclear reactor physics and engineering
Authors: Yun-Wen Mao | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

Bayesian Optimization in Chemical Compound Sub-Spaces using Low-Dimensional Molecular Descriptors Efficient optimization of molecules with targeted properties remains a significant challenge due to the vast size and discrete nature of chemical compound space. Conventional machine-learning-based optimization approaches typically require large datasets to construct accurate surrogate models, limiting their applicability in data-scarce settings. In this study, we present a Bayesian optimization (BO) framework that identifies optimal molecular structures with high precision using fewer than 2,000 training data points within a chemical subspace containing more than 133,000 molecules. The framework employs a low-dimensional and physics-informed molecular descriptor vector that facilitates data-efficient surrogate modelling and optimization. A key innovation of the proposed framework is a reliable inverse mapping scheme that translates optimized points in the descriptor space back into chemically valid molecular structures, thereby bridging continuous optimization and discrete molecular design. We demonstrate the effectiveness of our approach on the QM9 benchmark dataset, where the framework successfully identifies organic molecules with the target entropy and zero-point vibrational energy (ZPVE) values.For entropy optimization, our approach achieves a 100% success rate while requiring fewer than 1,000 molecular evaluations in more than 80% of test cases. For ZPVE, the success rate exceeds 80% for molecules containing more than two heavy atoms. These results highlight the critical role of low-dimensional, interpretable descriptors in enabling data-efficient optimization and robust inverse molecular design, and establish Bayesian optimization as a practical tool for molecular discovery in small-data regimes. Bayesian optimization ; Molecular descriptor ; Bayesian probability ; Chemical space ; Entropy (arrow of time) ; Subspace topology ; Benchmark (surveying) ; Inverse ; Optimization problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference