jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olamide Osigbemeh
Source: Journal of International Crisis and Risk Communication Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modeling and Predicting the Thermal Behavior of Si/CNT Nano Composites Using PINNs Silicon/Carbon Nanotube (Si/CNT) composites have shown great performance as Thermal Interface Materials (TIMs) for cooling in advanced electronics. The heat transfer in these materials is highly influenced by the interfacial thermal resistance (ITR) at the interfaces between the CNT filler and the silicon matrix. The ITR poses a critical challenge that hinders the design and performance of the TIM. This research designs a Physics Informed Neural Network (PINN) that predicts the thermal behavior of Si/CNT through a forward and inverse solver since they are known networks in solving complex partial and ordinary differential equations. However, despite the advantages of PINNs, standard PINNs are known to produce models with continuous derivatives. There is an issue seen in standard PINNs when used in predicting heterogenous materials because it struggles with sharp gradients and non-smooth interfaces across the discontinuous materials. This research explores the domain decomposed PINN approach of solving a 2D steady state heat conduction problem of a composite material used in semiconductor packaging. A model of 1µm x 1µm Si/CNT square matrix is sub-divided into four vertical rectangular strips of varying thermal conductivity () to represent the composite material thermal properties. A fine mesh is generated using MATLAB’S PDE Toolbox to solve the Poisson equation under Dirichlet and Neumann boundary conditions. Abaqus FEA simulation results are also used to compare results from PINNs and MATLAB. A good correlation is achieved through fine-tuning model parameters. This research shows that the forward and inverse PINN framework is a powerful data driven tool for material modeling. It successfully implements the flexibility PINNs governed by laws of physics to predict the hidden parameters in next generation TIMs. Thermal conductivity ; Thermal grease ; Thermal conduction ; Boundary value problem ; Interfacial thermal resistance ; Solver ; Thermal ; Thermal resistance ; Heat transfer ; Interface (matter) Thermal properties of materials ; Composite Material Mechanics ; Carbon Nanotubes in Composites
Authors: Yosuke Oyama | Yutaka Majima | Eiji Ohta | Yasufumi Sakai
Source: ArXiv.org (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Neural network potentials (NNPs) are crucial for accelerating computational materials science by surrogating density functional theory (DFT) calculations. Improving their accuracy is possible through pre-training and fine-tuning, where an NNP model is first pre-trained on a large-scale dataset and then fine-tuned on a smaller target dataset. However, this approach is computationally expensive, mainly due to the cost of DFT-based dataset labeling and load imbalances during large-scale pre-training. To address this, we propose LaMM, a semi-supervised pre-training method incorporating improved denoising self-supervised learning and a load-balancing algorithm for efficient multi-node training. We demonstrate that our approach effectively leverages a large-scale dataset of $\sim$300 million semi-labeled samples to train a single NNP model, resulting in improved fine-tuning performance in terms of both speed and accuracy. Artificial neural network ; Noise reduction ; Reduction (mathematics) ; Deep learning ; Noise (video) ; Noisy data ; Data modeling Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Xiaohui Li | Zicong Li | Ruikun Zhang | Yunyu Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advanced Clustering and Deep Learning: A Synergistic Approach to Nanomaterial Characterization Optimization Cluster analysis ; Characterization (materials science) ; Artificial intelligence ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ke Xu (134879) | Yuan Li (67017) | Dongliang Ding (9721691) | Ting Liang (506921) | Jianyang Wu (1910674) | Jianbin Xu (1416805)
Source: Figshare (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Critical SizeTransitions in Silicon Nanowires: Amorphization,Phonon Hydrodynamics, and Thermal Conductivity Understanding the intrinsic thermal transport properties of ultrathin semiconductor nanowires with varying diameters is crucial for the efficient thermal management of next-generation nanoelectronic devices. Here, we developed high-fidelity machine-learning potential (MLP) within the fourth-generation neuroevolution potential framework to elucidate the interplay between structural evolution, amorphous transition behavior, and thermal transport in silicon nanowires (SiNWs), resolving long-standing discrepancies between simulations and experiments. The structure of SiNWs below 1.1 nm in diameter undergoes a complete amorphous transformation, which originates from an amorphous surface structure of 5–6 atomic layers. We identify a nonmonotonic dependence of thermal conductivity on nanowire diameter due to competition between N (Normal) and U (Umklapp) phonon scattering processes. At frequencies <1 THz, N-process scattering rates exceed U-process rates by 3 orders of magnitude in ultrafine SiNWs, enabling fluid-like phonon transport. This study underscores the transformative potential of high-fidelity MLP in unraveling complex nanoscale material behaviors. Thermal conductivity ; Nanowire ; Phonon ; Scattering ; Silicon ; Amorphous solid ; Nanoscopic scale ; Semiconductor ; Phonon scattering Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Nanowire Synthesis and Applications
Authors: Alexandra Boltasseva | Igor V. Bondarev
Source:  (article, 2025-09-16)
Field: Chemistry — Inorganic Chemistry

Crystallization of the transdimensional electron liquid Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, we discuss photonic design approaches and emerging material platforms for showcasing machine-learning-assisted topology optimization for optical metasurface designs with applications in thermophotovoltaics, reflective optics, quantum photonic circuitry and lightsail technology. We demonstrate the effectiveness of autoencoders for compressing the vast design space of metasurfaces into a smaller search space. By employing global optimization via adjoint methods or quantum annealing, one can find the optimal metasurface designs within the smaller space constructed by the autoencoder. The techniques employed in this work extend well beyond the metasurface optimization space and into many inverse design problems for engineering and physics. We also apply machine learning approaches to advance quantum measurements and imaging. Topology optimization ; Quantum ; Photonics ; Space (punctuation) ; Inverse ; Work (physics) ; Inverse problem ; Topology (electrical circuits) Inorganic Fluorides and Related Compounds
Authors: Juyoung Byun | Yurim Kim | Jonghun Lim | Minseong Kim | Junghwan Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-driven optimization of photocatalytic hydrogen production: Integrating techno-economic analysis and regional environmental constraints Hydrogen production ; Software deployment ; Scalability ; Identification (biology) ; Hydrogen ; Sustainability ; Energy carrier ; Sensitivity (control systems) Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Energy, Environment, Economic Growth
Authors: Zihan Yang | Xiangfu Wang
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of fluorescence lifetimes of zero-dimensional antimony halides This paper proposes an innovative machine learning-based approach for predicting fluorescence lifetimes of zero-dimensional antimony halides, which is for the purpose of establishing relationships between material structures and emission properties. Materials science ; Antimony ; Halide ; Fluorescence ; Zero (linguistics) ; Chemical physics ; Atomic physics ; Optics ; Inorganic chemistry ; Metallurgy ; Physics ; Chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Chunlai Yang | Xingwu Zou | Qihang Zhu | Zhuo Lei | Fei Shao | Bo Li | Shuxuan Wang | Xiaoqiang Li
Source: Crystal Growth & Design (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Multiscale Investigation into the Growth Kinetics of Strontium Nitrate Crystals Based on an Ultrasound–Visual Dual-Modal Monitoring System To address limitations in real-time 3D morphology acquisition for crystal growth kinetics, this study pioneers an ultrasound–visual dual-modal monitoring system integrating stereoscopic imaging, ultrasonic attenuation, and molecular simulation. The system achieves 37% lower edge-length error in 3D reconstruction vs 2D projection and enables real-time concentration tracking via a PCA-GA-BP neural network (R2 = 0.9998). Kinetic experiments (n = 50) demonstrate that the (200) plane grows 14.5% faster than (111) (0.544 vs 0.475 μm/min) with higher temperature sensitivity (18% enhancement at 323.15 K). Molecular simulations reveal stronger water adsorption on (111) planes (−41.26 vs −10.84 kcal/mol), while the Modified Adsorption Energy (MAE) model corrects solvent-accessible area effects: the (111) plane proportion decreases from 67.14% to 47.44%, whereas (200) increases to 52.56%, aligning with experimental habits. This integrated methodology provides cross-scale insights for inorganic salt crystallization mechanisms and industrial process optimization. Crystallization ; Adsorption ; Crystal growth ; Sensitivity (control systems) ; Kinetic energy ; Crystal (programming language) ; Kinetics ; Tracking (education) ; Nucleation Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Chemical and Physical Properties in Aqueous Solutions
Authors: Anna Hruzíková | Vahid Barati | Aneta Ešnerová | Ivana Cı́sařová | Aleš Růžička | Ondřej Baszczyňski | Eliška Procházková
Source: Chemistry - A European Journal (article, 2025-03-11)
Field: Chemistry — Organic Chemistry

<sup>31</sup>P NMR Study of P‐Chirogenic Phosphaphenanthrenes with Molecular Flexibility tuned by Amino Acid Substituents Model phosphonamidates derived from 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) with molecular flexibility tuned by amino acid substituents were prepared as pairs of diastereoisomers (DSIs) differing in configuration on phosphorus atom. X-ray diffraction (XRD) determined absolute configuration on phosphorus and revealed conformational flexibility of six-membered oxa-phospha-cycle. Quantum-chemical calculations combined with machine learning provided 2-4 representative conformers from each DSI present in solution. <sup>31</sup>P chemical shift of RS DSIs was higher compared to the SS ones. Calculated <sup>31</sup>P-<sup>1</sup>H as well as <sup>31</sup>P-<sup>13</sup>C J-couplings followed Karplus dependence of J-coupling values on dihedral angles between interacting nuclei, showing reasonable match with the literature data. The effect of molecular flexibility quantified by parameter nConf<sub>20</sub> on NMR parameters was significant, especially for residual dipolar couplings (RDCs), where Pearson correlation factor R decreased with increasing nConf<sub>20</sub> parameter. Dihedral angle ; Diastereomer ; Conformational isomerism ; Flexibility (engineering) ; Chemistry ; Chemical shift ; Absolute configuration ; Crystallography ; Residual dipolar coupling ; Dipole ; Molecule ; Stereochemistry ; Nuclear magnetic resonance spectroscopy ; Mathematics ; Physical chemistry ; Organic chemistry ; Hydrogen bond Organophosphorus compounds synthesis ; Synthetic Organic Chemistry Methods ; Phytochemistry and biological activities of Ficus species
Authors: Y.W. Liu | Tyler C. Sterling | Liu Shi
Source: ArXiv.org (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

Dipolar Nematic State in Relaxor Ferroelectrics Relaxor ferroelectrics exhibit exceptional dielectric and electromechanical properties, yet their microscopic origins remain elusive due to the interplay of hierarchical polar structures and chemical complexity. While models based on polar nanoregions or nanodomains offer valuable phenomenological insights, they often lack the first-principles predictive capability necessary for quantitatively describing functional properties such as piezoelectric coefficients. Here, we use large-scale molecular dynamics simulations, enabled by a universal first-principles-based machine-learning interatomic potential, to investigate atomic-scale polar dynamics in canonical Pb-, Bi-, and Ba-based relaxors. Across all systems, we uncover a universal dipolar nematic state, characterized by long-range orientational order of local polarizations without local alignment, challenging conventional polar cluster-based paradigms. We introduce a universal order parameter, derived from the skewness of the distributions of the local polarization autocorrelation functions, that captures the thermal evolution of both lead-based and lead-free systems within a single master curve. This nematic order, and its robust structural memory under electric field cycling, underpins key relaxor phenomena, including diffuse phase transition, frequency-dependent dielectric dispersion, and reversible giant piezoelectricity. Our findings establish a unified microscopic framework for relaxors and present a broadly applicable statistical approach to understanding complex disordered materials. Polar ; Dipole ; Liquid crystal ; Polarization (electrochemistry) ; Dielectric ; Phase transition ; Piezoelectricity ; Electric field ; Antiferroelectricity Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: M. M. Trestman | Stefan Gugler | Felix A. Faber | O. Anatole von Lilienfeld
Source: ArXiv.org (preprint, 2025-10-10)
Field: Materials Science — Materials Chemistry

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy-system (Styblinski-Tang function) as well as for molecular dynamics trajectories from the MD17 dataset. Compared to FPS and uniform sampling, our numerical results indicate superior data efficiency and robustness when using GGFPS. Distribution analysis of the MD17 data suggests that FPS systematically under-samples equilibrium geometries, resulting in large test errors for relaxed structures. GGFPS cures this artifact and (i) enables up to two fold reductions in training cost without sacrificing predictive accuracy compared to FPS in the 2-dimensional Styblinksi-Tang system, (ii) systematically lowers prediction errors for equilibrium as well as strained structures in MD17, and (iii) systematically decreases prediction error variances across all of the MD17 configuration spaces. These results suggest that gradient-aware sampling methods hold great promise as effective training set selection tools, and that naive use of FPS may result in imbalanced training and inconsistent prediction outcomes. Robustness (evolution) ; Training set ; Sampling (signal processing) ; Set (abstract data type) ; Training (meteorology) ; Point (geometry) ; Reduction (mathematics) ; Data point ; Data set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Eniz Mušeljić | Alice Reinbacher‐Köstinger | Andreas Gschwentner | Manfred Kaltenbacher
Source: IET Science Measurement & Technology (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data driven parameter identification of magnetic properties in steel sheets Abstract As simulations play a crucial role for the development of modern electrical machines, it is very important to have good material models used in these simulations. Material models are dependent on certain material parameters which often cannot be measured directly and usually require a lot of computational resources to be determined. This paper investigates the application of neural networks and Gaussian processes for the identification of the magnetic permeability in electrical steel sheets. Through the manufacturing process of such steel sheets, different cutting techniques produce different material behaviour in the vicinity of the cutting edge. Therefore, the method requires the generation of datasets dependent on the degradation profile of the cut steel sheets. This is achieved through simulation and the constructed models can be reused without further simulation runs. This paper also uses an ensemble method to mitigate the issue of measurement noise. For the whole training and testing only simulation data is used as actual measurement data is not yet available. Identification (biology) ; Materials science Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Jiewen Nie | Tao Chen | Shan Jiang | Peng Dai | Pivnenko Mike | Weijie Wu | Pingjuan Niu | Juncheng Bao | Sen Wang | Daping Chu | Haining Yang
Source: Research Square (preprint, 2025-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Liquid Crystal Metasurface Enabled Hyperspectral Single-pixel Imaging Hyperspectral imaging ; Liquid crystal tunable filter ; Spectral imaging ; Full spectral imaging ; Narrowband ; Spectral resolution ; Detector ; Pixel ; Noise (video) Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Optical Polarization and Ellipsometry
Authors: Wei Nong | Ruiming Zhu | Zekun Ren | Mads Dines Petersen | Shuya Yamazaki | N. Kazeev | A. Ustyuzhanin | Gang Wu | Shuo‐Wang Yang | Kedar Hippalgaonkar
Source: ArXiv.org (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However, despite significant advances, we identify a critical yet overlooked issue undermining their reliability: a systematic energy underprediction. This problem becomes starkly evident in large-scale thermodynamic stability assessments. By performing over 12 million calculations using nine MLIAPs for over 150,000 inorganic crystals in the Materials Project, we demonstrate that most frontier models consistently underpredict energy above hull (Ehull), a key metric for thermodynamic stability, total energy, and formation energy, despite the fact that over 90\% of test structures (DFT-relaxed) are in the training data. The mean absolute errors (MAE) for Ehull exceed ~30 meV/atom even by the best model, directly challenging claims of achieving ``DFT accuracy'' for property predictions central to materials discovery, especially related to (meta-)stability. Crucially, we trace this underprediction to insufficient handling of symmetry degrees of freedom (DOF), constituting both lattice symmetry and Wyckoff site symmetries for the space group. MLIAPs exhibit pronounced errors (MAE for Ehull $&gt;$ ~40 meV/atom) in structures with high symmetry DOF, where subtle atomic displacements significantly impact energy landscapes. Further analysis also indicates that the MLIAPs show severe energy underprediction for a large proportion of near-hull materials. We argue for improvements on symmetry-aware models such as explicit DOF encoding or symmetry-regularized loss functions, and more robust MLIAPs for predicting crystal properties where the preservation and breaking of symmetry are pivotal. Symmetry (geometry) ; Lattice (music) ; Homogeneous space ; Property (philosophy) ; Metric (unit) ; Symmetry breaking ; Energy (signal processing) ; Degrees of freedom (physics and chemistry) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhifeng Chen | Si Sun | Huanhuan Qiao
Source: Journal of intelligent medicine. (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence‐assisted atomic precision clusters for central nerve diseases Abstract Owing to their nanoscale dimensions, well‐defined atomic structure, and elevated specific surface area, clusters have emerged as a novel therapeutic platform for neurological disorders. However, efficiently and rationally designing functionalized clusters capable of specifically recognizing and modulating key disease targets remains a major difficulty. The rapid advancement of artificial intelligence (AI) technology offers a revolutionary solution to this bottleneck. By integrating deep learning, generative models, and multi‐omics big data, AI can mine vast amounts of biomedical and chemical information with unprecedented speed and precision. It will drive transformative innovations in rational design of clusters, precise modulation of enzymatic activity, and high‐throughput screening of therapeutic targets for neurological disorders. Key (lock) ; Transformative learning ; Scale (ratio) ; Nanoscopic scale ; Atomic force microscopy ; Nerve cells ; Big data Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Alzheimer's disease research and treatments
Authors: Guillermo Marcos-Ayuso | David Quesada | María Y. Cobos-Abad | Carlos Lendínez | Sara Fernández-Moyano | Pablo Mauleón | Ramón Goméz Arrayás
Source: ACS Catalysis (article, 2025-11-24)
Field: Chemistry — Organic Chemistry

Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence CuI-catalyzed hydroboration of alkynes is a cost-effective route to trans-alkenyl boronates (valuable intermediates for C–C cross-coupling) but controlling regioselectivity in these reactions is challenging. Here, we address this challenge by developing a machine learning model, using high-throughput computational-derived descriptors and a combined experimental/literature data set to predict regioselectivity (expressed as ΔΔG‡). The resulting support vector regression (SVR) model achieved high accuracy (cross-validated R2 > 0.8, RMSE ∼0.4–0.6 kcal/mol) and revealed mechanistically relevant trends through feature importance analysis. For example, the model rationalizes why certain N-donor ligands that fail to promote Cu-catalyzed hydroboration can effectively catalyze alkyne hydrosilylation, linking this divergence to differences in geometric ligand descriptors. Moreover, ML-guided screening identified promising ligands that improved hydroboration outcomes (increased yield and regioselectivity), as confirmed in experiments (e.g., enabling reduced catalyst loading without sacrificing selectivity). Overall, this integrated ML strategy offers a powerful tool for understanding and predicting regioselectivity in CuI-mediated reactions and, with appropriate calibration, could be extended to other organocopper systems. Regioselectivity ; Hydroboration ; Borylation ; Support vector machine ; Feature (linguistics) ; Yield (engineering) ; Alkyne Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions