jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ramanadhan Lakshmi Keerthana | Hawren Fang | Prakash Deep | Satya Sriram DMV
Source:  (article, 2025-12-13)
Field: Materials Science — Surfaces, Coatings and Films

Metrology-Weighted Deep Learning for SEM Image Quality Enhancement without Ground Truth Data Noisy and low-quality images are a common challenge in scanning electron microscope (SEM) imaging, especially when fast acquisition is prioritized to maintain high manufacturing throughput. In semiconductor fabrication, this tradeoff between image quality and scanning speed can compromise the precision of critical metrology tasks essential for quality control and process monitoring. This paper presents a novel deep learning approach for SEM image quality enhancement that uniquely addresses this challenge without requiring paired ground-truth data. A metrology-weighted deep learning framework is introduced, and it fundamentally differs from conventional image denoising approaches by prioritizing measurement accuracy over perceived image quality. The proposed solution incorporates domain-specific knowledge of semiconductor manufacturing processes and metrology requirements, enabling robust performance across diverse structural patterns. The generic nature of our deep image denoiser makes it widely applicable in diverse semiconductor manufacturing processes, particularly in the era of extreme ultraviolet (EUV) lithography. It can be applied to improving SEM to layout alignment, SEM contour extraction for optimal proximity correction (OPC) modeling, and SEM image-based defect detection. This work represents a significant advancement in semiconductor metrology, offering a solution that balances the competing demands of throughput and precision in modern semiconductor manufacturing. Metrology ; Semiconductor device fabrication ; Deep learning ; Image quality ; Image (mathematics) ; Process (computing) ; Throughput ; Ground truth Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Anthony Lo | Connor W. Coley | Wojciech Matusik
Source: arXiv (Cornell University) (preprint, 2025-09-25)
Field: Chemistry — Physical and Theoretical Chemistry

A Genetic Algorithm for Navigating Synthesizable Molecular Spaces Inspired by the effectiveness of genetic algorithms and the importance of synthesizability in molecular design, we present SynGA, a simple genetic algorithm that operates directly over synthesis routes. Our method features custom crossover and mutation operators that explicitly constrain it to synthesizable molecular space. By modifying the fitness function, we demonstrate the effectiveness of SynGA on a variety of design tasks, including synthesizable analog search and sample-efficient property optimization, for both 2D and 3D objectives. Furthermore, by coupling SynGA with a machine learning-based filter that focuses the building block set, we boost SynGA to state-of-the-art performance. For property optimization, this manifests as a model-based variant SynGBO, which employs SynGA and block filtering in the inner loop of Bayesian optimization. Since SynGA is lightweight and enforces synthesizability by construction, our hope is that SynGA can not only serve as a strong standalone baseline but also as a versatile module that can be incorporated into larger synthesis-aware workflows in the future. Genetic algorithm ; Crossover ; Block (permutation group theory) ; Property (philosophy) ; Simple (philosophy) ; Filter (signal processing) ; Variety (cybernetics) ; Workflow Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Panpan Zhou | Haoran Shen | Ning Xu | Qianwen Zhou | Chengguo Yan | Xinying Li | Nuo Lei | Jinghua Jiang | Dan Song | Jiaguang Zheng | Yongjin Zou | Lixian Sun | Zhongkang Han | Xiulin Fan | Xuezhang Xiao | Lixin Chen
Source: Energy storage materials (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Ambient-condition hydrogen storage: Accelerating the development of high-capacity hydrides via a quantitative interpretable machine learning framework Electronegativity ; Hydride ; Hydrogen ; Hydrogen storage ; Alloy ; Metal ; Saturation (graph theory) Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Xiaotong Sun (6535064) | Ruili Li (470065) | Bowen Zhang (595078) | Hui Wang (30400) | Yujun Cheng (10946416) | Jianping Guan (18989752) | Qi Liu (33068) | Xiaoqing Chen (89898)
Source: Figshare (article, 2025-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Function–Structure–Synthesis:MachineLearning Enabled Closed-Loop Design of Biomass-Derived Porous CarbonMaterials Rational development of biomass-derived porous carbons (BDPCs) is vital for advancing fundamental research and addressing clean energy challenges such as hydrogen purification and supercapacitor energy storage. Current experimental designs are limited by empirical methods, and computational models struggle with the complex function–structure–synthesis relationships of biomass feedstocks. In this study, we introduce a data-driven closed-loop machine learning (ML) workflow integrated with literature-derived BDPC synthesis data sets, enabling inverse design from desired functionalities to optimal structures and synthesis conditions. Our ML framework predicts seven critical synthesis parametersincluding precarbonation, physical state, type and amount of activator, activation temperature, time, and heating ratebased on targeted microstructural features. Experimental validations with various biofeedstocks confirm the efficacy of our approach. Notably, AI-S, a BDPC synthesized from sugarcane bagasse under ML guidance, achieves a CO<sub>2</sub> working capacity of 12.3 mmol/g at 273 K and 10 bar and a CO<sub>2</sub>/H<sub>2</sub> selectivity of 47.8the highest reported for BDPCs to date. This ML-driven workflow bridges BDPC properties, microstructure, and synthesis, demonstrating a reliable and predictive method for functionally oriented porous material design and synthesis, thereby accelerating material discovery in energy and environmental applications. Workflow ; Inverse ; Artificial neural network ; Porous medium ; Bagasse ; Biomass (ecology) ; Energy (signal processing) ; Porosity Supercapacitor Materials and Fabrication ; CO2 Reduction Techniques and Catalysts ; Catalysis for Biomass Conversion
Authors: Cheng Fan (17164) | Maodong Li (4734390) | Sihao Yuan (18576853) | Zhaoxin Xie (17396134) | Dechin Chen (17372267) | Yi Isaac Yang (1609150) | Yi Qin Gao (132523)
Source: Figshare (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Performing PathIntegral Molecular Dynamics Usingan Artificial Intelligence-Enhanced Molecular Simulation Framework This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient path integral molecular dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double-proton transfer in the formic acid dimer) and a bulk-phase transition system (water–ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving the quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems using relatively low computational cost. Modular design ; Molecular dynamics ; Path integral formulation ; Path (computing) ; Computational complexity theory ; Complex system ; Computational model ; Quantum Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum, superfluid, helium dynamics
Authors: Cipriano Rángel | J. Espinosa-Garcı́a
Source: ChemRxiv (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Potential energy surfaces: Δ-machine learning from analytical functional forms Delta-machine learning (Δ-ML) is a highly cost-effective approach to developing high-level potential energy surfaces (PES) from a large number of low-level configurations. In particular, the high flexibility of the analytical PES-2008 is exploited to efficiently sample points from the low-level data set and, using information from the highly accurate PIP-NN surface, construct the Δ-ML PES. This approach is applied to the well-known H + CH4 hydrogen abstraction reaction. In order to test the validity and accuracy of the Δ-ML approach to describe this polyatomic system, kinetic studies using the variational transition state with multidimensional tunneling corrections and dynamic studies on the deuterated reaction, H + CD4, reaction using quasiclassical trajectory calculations were performed on the three surfaces, PES-2008 (low level), PIP-NN (high-level) and Δ-ML. The delta-machine learning approach reproduces the kinetics and dynamics information of the high-level surface, showing its efficiency in describing multidimensional polyatomic systems. Energy (signal processing) ; Computer science ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Li Lingjie | Pan Yichi | Zhang Haokang | Liang Wei | Shen V. Chong | Yang Dekun
Source:  (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-assisted angle-resolved scatterometry of 3D NAND channel hole structure Nanoscale critical dimension profiling of high-aspect-ratio channel holes in 3D NAND flash memory is achieved via optical metrology and neural networks, enabling accurate, high-throughput, and non-destructive measurement for advanced semiconductor memory manufacturing. Metrology ; Channel (broadcasting) ; Critical dimension ; NAND gate ; Flash (photography) ; Dimension (graph theory) ; Overlay Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advanced Surface Polishing Techniques
Authors: Yufeng Wang | Peiyao Wang | Wei Lü | Emérita Mendoza Rengifo | Dali Yang | Lu Ma | Yuewei Lin | Liu, Qun | Haibin Ling
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Materials Chemistry

XDIP: A Curated X-ray Absorption Spectrum Dataset for Iron-Containing Proteins Earth-abundant iron is an essential metal in regulating the structure and function of proteins. This study presents the development of a comprehensive X-ray Absorption Spectroscopy (XAS) database focused on iron-containing proteins, addressing a critical gap in available high-quality annotated spectral data for iron-containing proteins. The database integrates detailed XAS spectra with their corresponding local structural data of proteins and enables direct comparison between spectral features and structural motifs. Utilizing a combination of manual curation and semi-automated data extraction techniques, we developed a comprehensive dataset via extensive literature review, ensuring the quality and accuracy of data, which contains 437 protein structures and 1954 XAS spectrums. Our methods included careful documentation and validation processes to ensure accuracy and reproducibility. This dataset not only centralizes information on iron-containing proteins but also supports advanced data-driven discoveries, such as machine learning, to predict and analyze protein structure and functions. This work underscores the potential of integrating detailed spectroscopic data with structural biology to advance the field of biological chemistry and catalysis. Field (mathematics) ; Documentation ; X-ray absorption spectroscopy ; Function (biology) ; Data curation ; Absorption spectroscopy ; Protein structure Enzyme Structure and Function ; X-ray Spectroscopy and Fluorescence Analysis ; Protein Structure and Dynamics
Authors: Arsalan Hashemi | Pekka Peljo | Tapio Ala-Nissilä | Kari Laasonen
Source: ChemRxiv (preprint, 2025-01-15)
Field: Chemistry — Spectroscopy

Boosting Predictability: Towards Rapid Estimation of Organic Molecule Solubility The water solubility of organic molecules is critical for optimizing the performance and stability of aqueous flow batteries, as well as for various other applications. Although relatively straightforward to measure in some cases, the theoretical prediction of the solubility remains a considerable challenge. To this end, machine learning algorithms have become increasingly important tools in the past decade. High-quality data and effective descriptors are essential for constructing reliable data-driven estimation models. We systematically investigate the effectiveness of enhanced structure-based descriptors and an outlier detection procedure for improving aqueous solubility predictability. We train and evaluate random forest regression models using various descriptors to predict experimental solubility. Outliers are identified through an iterative maximum-error deletion procedure. We discover that descriptors derived from hydration free energy and weighted fingerprints, along with other established features, are effective. Notably, solvation energy, octanol-water partition coefficient, atomic charge polarizability interactions, and the presence of a full-carbon aromatic ring are critical for solubility prediction. Furthermore, the effectiveness of the outlier detection protocol is validated by improving the performance of the model and detailed analysis of the dataset. This study significantly improves the predictive capacity of supervised machine learning for molecular properties, enabling advancements in various technological applications. Predictability ; Boosting (machine learning) ; Solubility ; Organic molecules ; Estimation ; Molecule ; Environmental science ; Computer science ; Econometrics ; Chemistry ; Artificial intelligence ; Organic chemistry ; Mathematics ; Statistics ; Engineering ; Systems engineering Analytical Chemistry and Chromatography
Authors: Shingo Sotoma | Kota Shiraya
Source: Carbon Reports (article, 2025-08-31)
Field: Chemistry — Physical and Theoretical Chemistry

Carbon quantum dots for biological thermometry: Applications and advances Carbon quantum dots (CQDs) have recently emerged as promising fluorescent nanothermometers for biological applications, due to their tunable photoluminescence, low cytotoxicity, and chemical versatility. The changes in their fluorescence properties with temperature have opened new ways for precise thermal mapping at the cellular and tissue levels. This review presents a comprehensive overview of the fundamental mechanisms underlying CQD-based thermometry, emphasizing advances in material design, surface passivation, and environmental resilience. Particular attention is given to strategies that improve their stability in complex biological environments, a critical requirement for reliable intracellular sensing. Recent applications, including live-cell and in vivo thermometry, are highlighted to illustrate the expanding scope of CQD-based thermal probes. Future developments, such as near-infrared emissive CQDs, long-term biostability, and integration with machine learning and hybrid nanomaterials to achieve dynamic, high-precision thermal sensing, are outlined. By summarizing both foundational principles and emerging innovations, this review aims to provide a way to advance CQDs as indispensable tools in biomedical diagnostics, therapeutic monitoring, and thermal biology research. Quantum dot ; Nanotechnology ; Carbon quantum dots ; Materials science thermodynamics and calorimetric analyses ; Advanced Chemical Sensor Technologies ; Pharmacological Effects and Assays
Authors: Jamal Al-Nabulsi | Zaid Ajzan Alsalami | J. Deepak | Johar MGM | Anupama Routray | A. Karthikeyan | Harjot Singh Gill | Yashwant Singh Bisht | Ahmad Abumalek
Source: Scientific Reports (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Intelligent prediction of heat capacity of polyethylene glycol polymer Polyethylene glycol (PEG) exhibits tunable molar heat capacity, making it essential for thermal management and materials processing. Using an experimental databank of 528 observations, this study applies machine learning to develop predictive models based on temperature and molar mass. Data reliability was ensured through Monte Carlo-based outlier detection and correlation analysis, which confirmed temperature as the dominant predictor. Among the algorithms tested, Random Forest delivered the highest performance (R² = 0.9969, AARE = 8.29%), outperforming other methods that showed signs of overfitting. The results demonstrate that Random Forest provides a cost-effective, accurate, and scalable alternative to calorimetric experiments, enabling precise estimation of PEG heat capacity for applications in materials design, energy storage, and biomedical engineering. Polyethylene glycol ; Heat capacity ; Reliability (semiconductor) ; Random forest ; Polymer ; Outlier ; Monte Carlo method ; Thermal ; Scalability Thermal properties of materials ; Machine Learning in Materials Science ; Membrane Separation and Gas Transport
Authors: Jingyang Zhang | Zhixue Guo | Bing Wang | Liying Liu | Ru‐Zhi Wang
Source: Small (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Tailoring Superlattice Dimensions: A Pathway to Emergent Quantum Functional Devices (Small 46/2025) Superlattices Engineered 0D–3D superlattices (SLs) transcend conventional materials by precise atomic periodicity control and interfacial synergies. In article number 2505805, Ru-Zhi Wang and co-workers synthesize dimensional fabrication advances, comparing physical/chemical methods for tailored quantum devices. Dimensional SLs enable breakthroughs in optics, bandgap engineering, photoelectronics, and magnetism transition. Critical analysis of cross-dimensional advantages/limitations guides machine-learning-aided design and in situ characterization strategies for next-generation quantum functional devices. Superlattice ; Magnetism ; Fabrication ; Quantum ; Characterization (materials science) ; Quantum dot ; Band gap Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface Chemistry and Catalysis
Authors: Kamaladdin Abedi | Shayan Hoseini | Kian Dana
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Innovations and Future Directions in Hybrid Non-Thermal Plasma-Catalyst Systems for Voc Decomposition Decomposition ; Catalysis ; Thermal decomposition ; Plasma ; Nonthermal plasma ; Thermal ; Process engineering ; Environmental science ; Chemistry ; Biochemical engineering ; Chemical engineering ; Materials science ; Physics ; Engineering ; Thermodynamics ; Organic chemistry ; Nuclear physics Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Catalysts for Methane Reforming
Authors: Jan Pavšek | Alexander Mitsos | Manuel Dahmen | Thiam‐Soon Tan | Jan G. Rittig
Source: JuSER (Forschungszentrum Jülich) (preprint, 2025-09-21)
Field: Materials Science — Materials Chemistry

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks We propose a machine learning (ML) architecture to better capture the dependency of thermodynamic properties on the independent states. When predicting state-dependent thermodynamic properties, ML models need to account for both molecular structure and the thermodynamic state, described by independent variables, typically temperature, pressure, and composition. Modern molecular ML models typically include state information by adding it to molecular fingerprint vectors or by embedding explicit (semi-empirical) thermodynamic relations. Here, we propose to rather split the information processing on the molecular structure and the dependency on states into two separate network channels: a graph neural network and a multilayer perceptron, whose output is combined by a dot product. We refer to our approach as DeepEOSNet, as this idea is based on the DeepONet architecture [Lu et al. (2021), Nat. Mach. Intell.]: instead of operators, we learn state dependencies, with the possibility to predict equation of states (EOS). We investigate the predictive performance of DeepEOSNet by means of three case studies, which include the prediction of vapor pressure as a function of temperature, and mixture molar volume as a function of composition, temperature, and pressure. Our results show superior performance of DeepEOSNet for predicting vapor pressure and comparable performance for predicting mixture molar volume compared to state-of-research graph-based thermodynamic prediction models from our earlier works. In fact, we see large potential of DeepEOSNet in cases where data is sparse in the state domain and the output function is structurally similar across different molecules. The concept of DeepEOSNet can easily be transferred to other ML architectures in molecular context, and thus provides a viable option for property prediction. Dependency (UML) ; Embedding ; Artificial neural network ; Function (biology) ; Dependency graph ; Thermodynamic state ; Molecular graph ; Property (philosophy) ; Applicability domain Machine Learning in Materials Science
Authors: Amir Hossein Abdollahi Nohoji | Parviz Keshavarzi | Mohammad Danaie
Source: Results in Optics (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Low-power all-optical photonic crystal synapse using Ge2Sb2Te5 phase-change material • All-optical photonic crystal synapse with Ge 2 Sb 2 Te 5 phase-change material proposed. • Synaptic weight controlled via focused laser-induced GST phase change. • 3D FDTD and FEM simulations show >99 % transmission, <−20 dB reflection at 1504 nm. • Compact, low-power design ideal for optical neural networks and processing systems. • Physical and thermal models accurately predict GST crystallization during weighting. In this study, we propose an all-optical neuromorphic photonic crystal synapse structure equipped with a Ge 2 Sb 2 Te 5 phase-change material (GST-PCM). By leveraging the unique properties of the GST-PCM material, this structure enables the control of the synaptic weight through targeted and focused laser irradiation. 3D simulations employing the finite-difference time-domain (FDTD) and finite element method (FEM) demonstrated optical transmission exceeding 99 % and reflection below −20 dB at a wavelength of 1504 nm within the proposed structure. These features, together with their compact dimensions and low power consumption, make our proposed structure an ideal candidate for optical processing applications and neuromorphic neural networks. Furthermore, we investigated the physical and thermal equations to determine the crystallization fraction of GST-PCM during the synapse weighting process. These equations showed excellent agreement with the simulation results and could accurately calculate the GST-PCM crystallization fraction as a function of time and laser power. Our proposed structure not only has the potential to be extended to neuromorphic systems and optical neural networks but also serves as an innovative platform for all-optical synapses because of its precise control of optical properties, high adaptability, and low power consumption. Neuromorphic engineering ; Weighting ; Artificial neural network ; Laser ; Photonics ; Transmission (telecommunications) ; Photonic crystal ; Synapse Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices