jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Riaz K. Israni | Jaydeep Singh Parmar | Mehul Chauhan | Hina Maniya | Brij Pal Singh | Harsh Purohit | Jignesh H. Kamdar
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Integrating nanotechnology with deep learning for engineering, healthcare, and environmental solutions: A review Combining nanotechnology and deep learning methods is a leading-edge frontier in engineering solutions, with unparalleled prospects for innovation and growth. The synergistic combination blends the scale and precision of nano-materials with the predictive capability and resilience of artificial intelligence. Deep learning methods can process enormous amounts of data collected by nano-scale sensors, designing and optimizing materials at the atomic level. This integration makes possible the creation of self-healing smart nano-materials, highly efficient energy storage devices, and ultra-sensitive healthcare diagnostic tools. Additionally, deep learning methods can expedite the discovery of new nanostructures and their uses with much less time and expense compared to conventional experimental approaches. As this multidisciplinary area continues to grow, it holds the potential to transform numerous industries, such as electronics, healthcare, environmental science, and energy technology, towards more efficient, sustainable, and smart engineering solutions. Deep learning ; Multidisciplinary approach ; Process (computing) ; Scale (ratio) ; Deep integration ; Big data ; Resilience (materials science) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Nanopore and Nanochannel Transport Studies
Authors: Jonathan Hungerland | Lan Yang | James C. Gumbart | Ilia A. Solov'yov
Source: Biophysical Journal (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

BPS2026 – What can a machine learn from a force-field? Transferability ; Molecular dynamics ; Molecular mechanics ; Transformer Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Crystallography and molecular interactions
Authors: Zhenghan Peng | Adeyemi Uthman | Zhepeng Zhang | Anh Tuan Hoang | Xiang Zhu | Eric Pop | Andrew J. Mannix
Source: 2D Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Wide-field hyperspectral optical microscopy for rapid characterization of two-dimensional semiconductors and heterostructures Abstract Electronic and optoelectronic applications of two-dimensional (2D) semiconductors demand precise control over material quality, including thickness, composition, doping, and defect density. Conventional benchmarking methods (e.g. charge transport, confocal mapping, electron or scanning probe microscopy) are slow, perturb sample quality, or involve trade-offs between speed, resolution, and scan area. To accelerate assessment of 2D semiconductors, we demonstrate a broadband, wide-field hyperspectral optical microscope for 2D materials that rapidly captures a spatial-spectral data cube within seconds. The data cube includes x – y spatial coordinate (a 300 × 300 µ m 2 field, with ∼1 µ m resolution) and a selectable wavelength range between 200 nm to 1100 nm at each pixel. Using synthesized films and heterostructures of transition metal dichalcogenides (MoS 2 , WS 2 , V x W 1− x S 2 , and WSe 2 ), we show that this cost-effective technique detects spectral fingerprints of material identity, doping, grain boundaries, and alloy composition, and enables advanced analysis, including unsupervised machine learning for spatial segmentation. Hyperspectral imaging ; Characterization (materials science) ; Heterojunction ; Semiconductor ; Cube (algebra) ; Microscopy ; 3D optical data storage ; Optical microscope 2D Materials and Applications ; Graphene research and applications ; Organic Electronics and Photovoltaics
Authors: Ying Li | Shuai Xu | Longjian Guo
Source: Scientific Reports (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

A thermal resistance prediction model for heterogeneous integrated chips incorporating an AI-based BP neural network To address the thermal management challenges in heterogeneous integrated chip design, this study proposed an intelligent thermal resistance prediction model that integrated physical mechanisms with a backpropagation (BP) neural network. Unlike traditional black-box data-driven approaches, the proposed method was grounded in fundamental heat transfer principles and systematically constructed a physics-informed feature set encompassing geometric configurations, material properties, power distribution, and boundary conditions. Domain knowledge was explicitly encoded into the neural network architecture by introducing a physics-constrained layer with weight sign constraints and an attention-based feature interaction layer. Within a multi-task learning framework, the model simultaneously optimized the prediction of total thermal resistance, maximum temperature, and temperature non-uniformity. Experimental results demonstrated that the proposed model significantly outperformed baseline methods on the test set. Specifically, the coefficient of determination (R2) for total thermal resistance prediction reached 0.982, with a mean squared error of 0.021 K2/W2 and a mean absolute error of 0.103 K/W. For maximum temperature prediction, the R2 value reached 0.969. Compared with a single-task model that predicted only total thermal resistance, the multi-task architecture improved the primary task performance by 0.004. This study provided a fast, accurate, and physically interpretable intelligent tool for chip thermal design, and offered a valuable practical example of physics-informed machine learning applied to complex engineering problems. Artificial neural network ; Mean squared error ; Backpropagation ; Thermal resistance ; Feature (linguistics) ; Mean absolute percentage error ; Approximation error ; Thermal ; Test set ; Chip Thermal properties of materials ; 3D IC and TSV technologies ; Silicon Carbide Semiconductor Technologies
Authors: Amir Mahdian | Kaveh Farshadfar | Kari Laasonen
Source: The Journal of Organic Chemistry (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

A Machine Learning-Guided Study of Structure–Reactivity Relationships in Diels–Alder Cycloadditions The Diels-Alder cycloaddition is a cornerstone transformation in organic synthesis and has been extensively studied in both experimental and theoretical contexts. In this work, we present a complementary computational approach that combines density functional theory (DFT) and machine learning to further elucidate the role of steric and electronic effects in determining the reactivity and activation barriers. A diverse dataset of 1000 uncatalyzed hydrocarbon Diels-Alder reactions was used to train predictive models that relate activation energies to chemically meaningful molecular descriptors. SHAP analysis of the machine learning models highlights the dominant influence of steric effects, particularly those associated with substituent volume at the internal diene carbons, which can impose conformational strain and lead to significantly elevated barriers. In contrast, substituents at the terminal positions have a more limited impact. We introduced the minimum energy gap between LUMO<sub>diene</sub>-HOMO<sub>dienophile</sub> and LUMO<sub>dienophile</sub>-HOMO<sub>diene</sub> as a key predictive descriptor. This feature shows a strong correlation with the activation energy across the dataset, although steric interactions can lead to notable deviations from the overall trend. The resulting models provide insights for rationalizing selectivity and designing more efficient cycloadditions based on steric and electronic complementarity. Steric effects ; Cycloaddition ; Electronic effect ; Reactivity (psychology) ; Substituent ; Density functional theory ; Selectivity ; Potential energy Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Cyclization and Aryne Chemistry
Authors: Jan Pavšek | Alexander Mitsos | Elvis Jonathan Sim | Jan G. Rittig
Source: Open MIND (preprint, 2026-02-20)
Field: Materials Science — Materials Chemistry

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure, liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model promising for practical application in data scarce scenarios of chemical engineering practice. Artificial neural network ; Enthalpy ; Component (thermodynamics) ; Graph ; Process (computing) ; Experimental data ; Function (biology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Yachan Liu | Elaine Wu | Ping Yang | Aaron Sun | Subhransu Maji | Wei Fan | Peng Bai
Source: arXiv (Cornell University) (preprint, 2026-01-24)
Field: Chemistry — Inorganic Chemistry

Assessment of the synthetic feasibility of hypothetical zeolite-like materials based on ZeoNet A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures--even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis. Representation (politics) ; Classifier (UML) ; Suite ; Artificial neural network ; Pattern recognition (psychology) ; Deep neural networks ; Convolutional neural network ; Synthetic data Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Michael L. Barsoum | Tirzah Abbott | Steven D. Jacobsen | Omar K. Farha | Vinayak P. Dravid
Source: Nano Letters (article, 2026-01-15)
Field: Chemistry — Inorganic Chemistry

Crystal Orientation and Defect Mapping in Electron-Beam-Sensitive Zeolites with Near-Axis Transmission Kikuchi Diffraction Porous materials are vital in catalysis, energy conversion, and environmental remediation. Understanding structural heterogeneity in zeolites is key to linking synthesis, framework intergrowths, and catalytic performance, yet current methods for phase identification and spatial mapping lack sufficient resolution or throughput. We present a high-throughput approach using near-axis transmission Kikuchi diffraction in a scanning electron microscope, achieving high phase and spatial resolution for electron-beam-sensitive zeolites, including ZSM-5 and, for the first time, Zeolite A. This method enables direct visualization of intergrowth features that critically affect catalytic and adsorption behavior, bridging the gap between ensemble-averaged X-ray diffraction and high-resolution but low-throughput transmission electron microscopy. Combining nanoscale mapping with statistical sampling is highly suited for machine-learning pipelines guiding new structure function understanding and could be extended to other beam-sensitive porous materials such as metal-organic or covalent-organic frameworks. Diffraction ; Electron diffraction ; Nanoscopic scale ; Scanning transmission electron microscopy ; Transmission electron microscopy ; Electron backscatter diffraction ; Porosity ; Selected area diffraction ; Phase (matter) Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Weiran Zhang | Hao Pan | Shubo Wang
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning assisted inverse design of nonreciprocal multilayer photonic structures Nonreciprocal structures play an important role in optical physics and applications. Conventional approaches for designing nonreciprocal optical structures rely heavily on extensive numerical simulation and parameter tuning, leading to high computational cost and low efficiency. Here, we apply deep learning to the design of nonreciprocal multilayer photonic structures. Three neural-network models-a forward neural network (FNN), an inverse design network (IDN), and a variational autoencoder (VAE)-are employed to learn the complex mapping between structural/material parameters and nonreciprocal spectral characteristics. We show that the FNN can rapidly and accurately predict the nonreciprocal electromagnetic response of a given structure, while the IDN can directly generate suitable structural parameters for target spectral responses. Both approaches substantially reduce computational cost and design time while improving nonreciprocal performance. Furthermore, the VAE can generate band-limited inverse design under practical performance constraints, facilitating efficient exploration of multiple feasible structures that meet different threshold requirements within specified frequency bands. Our work highlights the potential of deep learning for the advanced design of nonreciprocal optical structures and devices. Autoencoder ; Artificial neural network ; Inverse ; Deep learning ; Photonics ; Inverse problem ; Network planning and design Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Electromagnetic Simulation and Numerical Methods
Authors: YaoKun Lei | Yi Isaac Yang
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Electronic Collective Variables for Chemical Reactions Chemical reaction sampling critically depends on collective variables (CVs) that capture the slow degrees of freedom governing reactive transformations. However, existing reaction CVs are often defined in geometric space or learned in a system-specific manner, which limits their transferability and leaves open the more fundamental question of how reaction progress should be represented. From a physical perspective, chemical reactions are defined by electron redistribution. Here, we introduce a charge-space electronic collective variable that describes the electronic component of reaction progress in a common linear form based on atomic charges. To enable its use in enhanced sampling, atomic charges and the corresponding CV gradients are provided by a neural-network model trained on QM/MM data within an iterative sampling-training workflow. Across multiple reactions in aqueous and enzymatic environments, we show that this electronic CV can be constructed in a common charge-space form, with the corresponding coefficients assigned in a simple manner from charge differences between relevant states. Our simulations further show that reaction progress generally involves coupled electronic and conformational components, and that the same framework can also be extended to restrain side reactions. These findings support charge-based electronic CVs as a physically motivated framework for describing the electronic component of chemical reaction progress with reduced reliance on handcrafted geometric descriptors. Electronic structure ; Chemical reaction ; Degrees of freedom (physics and chemistry) ; Simple (philosophy) ; Component (thermodynamics) ; Transferability ; Chemical space ; Space (punctuation) ; Variable (mathematics) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Zhimeng Shao | Bowen Gao | Zhifang Hu | Honglin Jiang | Qidong Zhang | Zhihe Dou | Yanxi Yin
Source: Materials & Design (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

From atomic mechanisms to multiscale and machine learning assisted modeling: understanding the sintering limit in high purity refractory metals • Systematically reviews multi-scale modeling approaches, from DFT and MD to ReaxFF and machine-learning interatomic potentials. • Highlights the distinct roles of diffusion, grain-boundary migration, and impurity segregation in the high-density regime. • Discusses the limitations of conventional interatomic potentials in describing late-stage sintering behavior. • Identifies structure-aware, unified potential-energy modeling as a promising direction for understanding sintering limits. High-purity refractory metals are widely used in aerospace, nuclear energy, and semiconductor applications. During sintering, however, these materials frequently exhibit kinetic slowdown and densification stagnation at high relative densities. Unlike impurity-containing systems, high-purity refractory metals generally lack solute pinning effects and second-phase-assisted diffusion pathways. As a result, densification is primarily governed by the coupled effects of lattice diffusion, grain-boundary migration, and localized chemical reactions, which become increasingly critical in the high-density regime. Direct experimental observation of sub-nanometer interfacial evolution during sintering remains extremely challenging, limiting the ability of conventional models to accurately describe the attenuation of driving forces at advanced densification stages. This review summarizes recent progress in multi-scale computational modeling of high-purity refractory-metal sintering, with a focus on density functional theory (DFT), molecular dynamics (MD), reactive force fields (ReaxFF), and machine-learning interatomic potentials (MLIPs). The capabilities and limitations of these approaches at elevated temperatures and high densities are discussed, particularly with respect to their complementary roles in describing diffusion processes, grain-boundary behavior, and pore evolution. Overall, recent studies highlight a growing interest in structure-aware, multi-scale potential-energy modeling. Such approaches provide valuable insight into densification limits in high-purity refractory metals-especially under hydrogen atmospheres-and offer guidance for optimizing high-precision sintering processes. Sintering ; ReaxFF ; Refractory metals ; Limiting ; Multiscale modeling ; Impurity ; Density functional theory ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Yachan Liu | Elaine Wu | Ping Yang | Aaron Sun | Subhransu Maji | Wei Fan | Peng Bai
Source: arXiv (Cornell University) (article, 2026-01-24)
Field: Chemistry — Inorganic Chemistry

Assessment of the synthetic feasibility of hypothetical zeolite-like materials based on ZeoNet A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures--even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis. Representation (politics) ; Classifier (UML) ; Suite ; Artificial neural network ; Pattern recognition (psychology) ; Deep neural networks ; Convolutional neural network ; Synthetic data Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Weiwei Li | Han Pan | Dongyu Zhen | Nian Cheng
Source: Physica Scripta (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

A general strategy to study the thermal expansion property of solid materials using deep potential assisted large scale molecular dynamics simulation Abstract Thermal expansion is an important property of solid materials, and molecular dynamics (MD) simulation is a feasible theoretical method for studying the thermal expansion properties of various solid materials. However, previous density functional theory (DFT) or empirical interatomic potential based MD simulations are faced with problems such as high computation cost for DFT calculation or low accuracy for empirical potential. To solve the above problems, machine learning based deep potential assisted large scale MD simulation is proposed in this study, and a general strategy to construct the deep potential is also provided. The proposed method is tested using crystal Si as a benchmark material. Results demonstrate that highly accurate deep potential could be feasibly trained using the general strategy described in our study, and that MD simulation using deep potential could well produce the thermal expansion coefficient of crystal Si from previous studies. Molecular dynamics ; Thermal expansion ; Computation ; Benchmark (surveying) ; Property (philosophy) ; Interatomic potential ; Scale (ratio) Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermal properties of materials