jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vipin K E (21737628) | Prahallad Padhan (2360749)
Source: Figshare (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Thermoelectric Propertiesin Skutterudite Materials:Integrating Experimental Data, Density Functional Theory, and MachineLearning An XGBoost regression model has been utilized to accurately predict the temperature-dependent thermal conductivity (κ), electrical conductivity (σ), Seebeck coefficient (S), and figure of merit (ZT) of skutterudite materials from 50 to 1000 K. Shapley values, derived from cooperative game theory, were employed to quantify the contributions of various material descriptors, providing valuable insights into the underlying correlations and trade-offs between different features and thermoelectric properties. The analysis revealed crucial factors influencing the thermoelectric performance of skutterudites, including temperature, compositional attributes, electronic configurations, and structural properties. The model predicts a room-temperature ZT of approximately 0.45 for Nd(CoP<sub>3</sub>)<sub>4</sub>, a promising thermoelectric material that has not yet been experimentally investigated. In addition, the prediction of half-metallic ferromagnetic behavior in Nd(CoP<sub>3</sub>)<sub>4</sub> by first-principles density functional theory (DFT) with Hubbard corrections has led to the investigation of its thermoelectric properties. At elevated temperatures, a strong correlation was observed between XGBoost predictions and DFT calculations for the σ and S of Nd(CoP<sub>3</sub>)<sub>4</sub>. The agreement between predicted and calculated values was less pronounced, though still reasonable, for κ and ZT. By integrating machine learning with fundamental materials science principles, this study paves the way for accelerated discovery and optimization of high-performance thermoelectric materials, contributing to the advancement of sustainable energy technologies and the pursuit of a more energy-efficient future. Skutterudite ; Thermoelectric effect ; Thermal conductivity ; Thermoelectric materials ; Seebeck coefficient ; Density functional theory Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Lai Nguyen Huy | Minh Tien Tran | Hoheok Kim | Ho Won Lee
Source: ArXiv.org (preprint, 2025-07-28)
Field: Materials Science — Materials Chemistry

Phase-fraction guided denoising diffusion model for augmenting multiphase steel microstructure segmentation via micrograph image-mask pair synthesis The effectiveness of machine learning in metallographic microstructure segmentation is often constrained by the lack of human-annotated phase masks, particularly for rare or compositionally complex morphologies within the metal alloy. We introduce PF-DiffSeg, a phase-fraction controlled, one-stage denoising diffusion framework that jointly synthesizes microstructure images and their corresponding segmentation masks in a single generative trajectory to further improve segmentation accuracy. By conditioning on global phase-fraction vectors, augmented to represent real data distribution and emphasize minority classes, our model generates compositionally valid and structurally coherent microstructure image and mask samples that improve both data diversity and training efficiency. Evaluated on the MetalDAM benchmark for additively manufactured multiphase steel, our synthetic augmentation method yields notable improvements in segmentation accuracy compared to standard augmentation strategies especially in minority classes and further outperforms a two-stage mask-guided diffusion and generative adversarial network (GAN) baselines, while also reducing inference time compared to conventional approach. The method integrates generation and conditioning into a unified framework, offering a scalable solution for data augmentation in metallographic applications. Segmentation ; Benchmark (surveying) ; Image segmentation ; Noise reduction ; Microstructure ; Pattern recognition (psychology) ; Diffusion ; Noise (video) ; Phase (matter) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Shivam Singh | Paras Kumar Yadav | Pramod Kumar | Sharik Ahmad
Source:  (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

FastTrack ART: Accelerating HIV Treatment via Quantum-AI-Enhanced Molecular Simulations The rapid evolution of drug-resistant HIV strains poses significant challenges for antiretroviral therapy (ART), necessitating advanced computational techniques for drug discovery. Traditional AI-driven molecular docking methods suffer from high computational costs and limited adaptability to mutating HIV variants. To address these limitations, this paper proposes a Hybrid Quantum-AI framework that integrates Variational Quantum Eigensolvers (VQE) with Graph Neural Networks (GNNs) to enhance molecular binding affinity predictions. Experimental validation on real-world HIV mutation datasets demonstrates that the proposed model improves binding accuracy from 75% to 85%, significantly reducing false positives in drug-target interactions. Additionally, the Quantum-AI framework accelerates molecular simulations by 55%, reducing computational time from weeks to hours. The model is implemented using IBM Qiskit and TensorFlow, leveraging quantum variational algorithms for efficient drug screening. Benchmark comparisons indicate an 83% improvement in computational efficiency over traditional AI-based methods, highlighting the feasibility of quantum computing in biomedical applications. These findings suggest that hybrid Quantum-AI models can revolutionize HIV drug discovery, optimize ART regimen personalization, and bridge the gap between computational drug research and clinical applications. Computational model ; Benchmark (surveying) ; Human immunodeficiency virus (HIV) ; False positive paradox ; Adaptability ; Graph ; Computational complexity theory ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; HIV/AIDS drug development and treatment
Authors: Yao Wen | Wanli Jia | Ruofan Shen | Jiayao Wang | Lin Zhang | Xinmei Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Bandgap and Formation Energy in Two-Dimensional Metal Oxides Band gap ; Materials science ; Energy (signal processing) ; Metal ; Artificial intelligence ; Nanotechnology ; Computer science ; Machine learning ; Optoelectronics ; Physics ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Shyue Ping Ong | Aaron D. Kaplan | Runze Liu | Ji Qi | Tsz Wai Ko | Bowen Deng | Gerbrand Ceder | Kristin A. Persson
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Rightsizing AI Models and Datasets for Materials Design In silico materials design has long faced a fundamental tradeoff between accuracy, universality, and efficiency. In 2022, we pioneered the concept of a universal machine learning interatomic potential (UMLIP) [Chen &amp; Ong, Nat. Comput. Sci., 2022, 2, 718–728] – a foundational materials model (FMM) with comprehensive coverage of the periodic table. FMMs enable accurate, large-scale simulations across a broad spectrum of materials, offering transformative potential for materials discovery and design. More recently, the field has seen a trend toward increasingly complex FMM architectures—often with over 10 million parameters—trained on datasets exceeding 100 million structures, driven largely by major tech companies like Google DeepMind, Microsoft, and various startups. In this talk, I challenge the prevailing “bigger is better” paradigm in FMM development. I will present MatPES, a foundational, community-curated potential energy surface (PES) dataset of ~400,000 structures. Leveraging MatPES, we demonstrate that gains in FMM performance are primarily driven by data quality, and there are no “accuracy moat” in FMM architectures. Models trained on MatPES match or exceed the accuracy of previous FMMs across a diverse set of equilibrium, near-equilibrium, and dynamic benchmarks. Finally, I will argue that the key priorities in architectural and algorithmic development should be in the parallelization and scaling of such FMMs in high performance computing and their integration in high-throughput materials workflows. Field (mathematics) ; Set (abstract data type) ; Key (lock) ; Scaling ; Transformative learning ; Data-driven ; Work (physics) ; Benchmark (surveying) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Scientific Computing and Data Management
Authors: Mark K. Transtrum | Gus L. W. Hart | Tyler J. Jarvis | Jared P. Whitehead
Source: Physical Review Research (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Generalized aliasing explains double descent and informs model design A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, overparametrized models exhibit counterintuitive behaviors, such as “double descent” in which models of increasing complexity exhibit generalization error. Other models may exhibit more complicated patterns of predictive error with multiple peaks and valleys. Neither double descent nor multiple descent phenomena are well explained by the bias-variance decomposition. We introduce a decomposition that we call the generalized aliasing decomposition (GAD) to explain the relationship between predictive performance and model complexity. The GAD decomposes the predictive error into three parts: (1) , which dominates when the number of parameters is much smaller than the number of data points, (2) , which dominates when the number of parameters is much greater than the number of data points, and (3) , which dominates between these two extremes. We demonstrate the applicability of the GAD to diverse applications, including random feature models from machine learning, Fourier transforms from signal processing, solution methods for differential equations, and predictive formation enthalpy in materials discovery. Because key components of the generalized aliasing decomposition can be explicitly calculated from the relationship between model class and samples without seeing any data labels, it can answer questions related to experimental design and model selection collecting data or performing experiments. We further demonstrate this approach on several examples and discuss implications for predictive modeling and data science. Aliasing ; Generalization ; Function (biology) ; Model selection ; Gradient descent ; Counterintuitive ; Extrapolation ; Error function ; Overfitting Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Stochastic Gradient Optimization Techniques
Authors: Liping Zhou (814435) | Jin Sun (117866) | Rongheng Ma (20666846) | Xiaoming Lyu (417710) | Shimei Wang (2371510) | Jing Wu (54032) | Xuanyang Ding (21735050) | Zibo Gao (8315094) | Siqi Gong (436793) | Yingji Wang (16624716) | Yang Li (7082)
Source: Figshare (article, 2025-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overcoming Fluorescence Interference: “PlanarHotspot” Substrates for Vitamin B Detection with SERS Surface-enhanced Raman scattering (SERS) has demonstrated remarkable potential for molecular detection due to its specificity. However, detecting fluorescent vitamin B molecules remains challenging due to fluorescence interference and weak Raman cross-sections. To address this, we developed an innovative large-area silver nanodisk substrate with “planar hotspots” featuring an extensive effective hotspot surface area. This design enables the adsorption of target molecules into the planar hotspot region, effectively quenching fluorescence through direct interaction with the silver substrate while amplifying Raman signals. For the first time, we detected high-fluorescence vitamin B molecules using SERS. This method successfully detected vitamin B molecules in simulated biofluid environments by integrating advanced machine-learning algorithms. As a precise, rapid, reproducible, noninvasive, and multifunctional detection platform, it holds significant promise for intelligent trace-level analysis of vitamin B molecules in food, pharmaceuticals, and human samples. Raman scattering ; Fluorescence ; Molecule ; Raman spectroscopy ; Vitamin b ; Substrate (aquarium) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chennasamudram Harsha | Sanath | K D Preethesh | Deeksha Deeksha | Sharatkumar | Suhandas
Source:  (article, 2025-04-28)
Field: Chemistry — Inorganic Chemistry

Implementation Of Automated Purity Grading System for Dry Coconuts Using Machine Learning Dry coconut, also known as copra, is a raw material used in the coconut oil industry for food, cosmetics, and pharmaceuticals. The purity of copra determines the quality of coconut oil, so grading has to be accurate. Traditional manual sorting methods are labor- intensive, inconsistent, and prone to errors. To overcome these challenges, this study proposes an Automated Purity Grading System using Machine Learning. The system uses a CNN to classify images of dry coconuts and determine if they are pure or impure. It has a Raspberry Pi-based setup with an integrated camera and conveyor belt that does sorting in real time, reducing errors in classification. The CNN model used is trained and optimized with TensorFlow Lite for effective grading that is both consistent and costeffective. This helps decrease the reliance on humans while making coconut processing efficient. Larger datasets and sensor integration for improved classification are the future improvements. Computer science ; Grading (engineering) ; Artificial intelligence ; Machine learning ; Engineering Coconut Research and Applications
Authors: Bichuan Cao (21307422) | Jiawei Dong (1456573) | Zedong Wang (2617439) | Linjun Wang (1296336)
Source: Figshare (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Large-ScaleNon-Adiabatic Dynamics Simulation Basedon Machine Learning Hamiltonian and Force Field: The Case of ChargeTransport in Monolayer MoS<sub>2</sub> We present an efficient and reliable large-scale non-adiabatic dynamics simulation method based on machine learning Hamiltonian and force field. The quasi-diabatic Hamiltonian network (DHNet) is trained in the Wannier basis based on well-designed translation and rotation invariant structural descriptors, which can effectively capture both local and nonlocal environmental information. Using the representative two-dimensional transition metal dichalcogenide MoS<sub>2</sub> as an illustration, we show that density functional theory (DFT) calculations of only ten structures are sufficient to generate the training set for DHNet due to the high efficiency of Wannier analysis and orbital classification in sampling the interorbital couplings. DHNet demonstrates good transferability, thus enabling direct construction of the electronic Hamiltonian matrices for large systems. Compared with direct DFT calculations, DHNet significantly reduces the computational cost by about 5 orders of magnitude. By combining DHNet with the DeePMD machine learning force field, we successfully simulate electron transport in monolayer MoS<sub>2</sub> with up to 3675 atoms and 13475 electronic levels by using a state-of-the-art surface hopping method. The electron mobility is calculated to be 110 cm<sup>2</sup>/(V s), which is in good agreement with the extensive experimental results in the range of 3–200 cm<sup>2</sup>/(V s) during 2013–2023. Due to the high performance, the proposed DHNet and large-scale non-adiabatic dynamics methods have great potential to be applied to study charge carrier dynamics in a wide range of material systems. Wannier function ; Hamiltonian (control theory) ; Molecular dynamics ; Density functional theory ; Invariant (physics) ; Electron ; Monolayer Machine Learning in Materials Science ; 2D Materials and Applications ; Electrocatalysts for Energy Conversion
Authors: T. Meyer | Gesa-R. Siemann | Paulina Majchrzak | Thomas Seyller | J. S. Rigden | Yù Zhang | Emma Springate | Charlotte E. Sanders | Philip Hofmann
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Surfaces, Coatings and Films

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in TDC line shape for quasi-free-standing monolayer graphene and for a simple analytical model. Our analysis reveals how finite energy and time resolution can affect the TDC line shape. We discuss how this can be taken into account in a quantitative analysis, and under what conditions the time-dependent photoemission intensity after laser excitation can be approximated by a simple exponential decay. Angle-resolved photoemission spectroscopy ; Photoemission spectroscopy ; Line (geometry) ; Intensity (physics) ; Excitation ; Excited state ; Inverse photoemission spectroscopy ; Spectroscopy ; Energy (signal processing) Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Younghun Kim (201840) | Wonseok Lee (3705475) | Jihan Kim (1527391)
Source: Figshare (article, 2025-08-22)
Field: Chemistry — Inorganic Chemistry

AmorphousMetal–OrganicFramework Databasefor Amorphization Prediction and CO<sub>2</sub> Direct Air CaptureScreening The amorphous phases of crystalline metal–organic frameworks (MOFs), known as amorphous metal–organic frameworks (AMOFs), offer a vast yet underexplored search space for numerous applications. To efficiently navigate this chemical space, data-driven methodologies are essential for elucidating structure–property relationships. In this study, a novel approach to explore this space is introduced by utilizing chemically accurate reactive force field (ReaxFF) to first create the AMOF database. Through melt-quenching simulations performed using ReaxFF, a database consisting of 487 AMOF structures was constructed. From this database, machine learning models trained on the generated AMOF database accurately predicted which MOFs are more likely to go through thermal amorphization based on key structural features. Furthermore, promising candidates for CO<sub>2</sub> direct air capture (DAC) applications were identified by running Widom insertion calculation, discovering three AMOF structures exhibiting DAC-favorable features after thermal amorphization. These findings highlight the effectiveness of this new data-driven approach for exploring the amorphous phases of MOFs beyond existing databases. Amorphous solid ; Field (mathematics) ; Thermal ; Space (punctuation) ; Key (lock) ; Force field (fiction) Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Erica Curran | Sina Sadeghi | Stephen McBride | Karin Mattsson | Megan Pike | R. Harrison | Mustapha Abdulmojeed | Franziska Theiß | Patrick TomHon | Eduard Y. Chekmenev | Milad Abolhasani | Thomas Theis
Source: Analytical Chemistry (article, 2025-12-09)
Field: Chemistry — Spectroscopy

Machine Learning-Guided Optimization of SABRE Hyperpolarization for α-Ketoglutarate in Acetone–Water Signal amplification by reversible exchange (SABRE) is a hyperpolarization method that polarizes target nuclei of metabolites quickly and efficiently. Recent SABRE advances, including Ace-SABRE, yield biocompatible, aqueous solutions of hyperpolarized markers for metabolic monitoring. Building on recent advancements, expanding the substrate scope of Ace-SABRE is desirable. However, SABRE polarization is sensitive to many different parameters; therefore, traditional optimization approaches are experimentally time-consuming. In this proof-of-concept application of machine learning (ML), Bayesian optimization (BO) is used for four important input parameters to model the complex SABRE dynamics while saving experimental time. The presented ML model also provides chemical insights that enable predictions of sample compositions for increased polarization levels. In this work, we transition from an original average free polarization of <i>p</i> = ∼0.90% to a maximum observed free polarization of <i>p</i> = ∼6.6% for 1-<sup>13</sup>C alpha-ketoglutarate (AKG) with <sup>13</sup>C at natural abundance, utilizing both direct outputs as well as chemical insights revealed by the ML model. Hyperpolarization (physics) ; Polarization (electrochemistry) ; Bayesian optimization ; Induced polarization ; Spin isomers of hydrogen ; Aqueous solution ; Analytical Chemistry (journal) Advanced NMR Techniques and Applications ; Nonlinear Optical Materials Research ; Molecular spectroscopy and chirality
Authors: Mengdie Fan | Chenhui Sang | Hua Li | Yue Wei | Bing Zhang | Yang Xing | Jing Zhang | Jie Yin | Wei An | Bing Shao
Source: Research (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. However, existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets, sparsity of valid data, and lack of classification in datasets, resulting in poor generalization capability and accuracy. In this study, a dataset for 10,905 compounds was constructed including their retention times. Next, an innovative classification system was implemented, classifying 10,905 compounds into a 3-tier hierarchy across 141 classes, based on functional group weighting. Then, data augmentation was performed within each category using simplified molecular input line entry system (SMILES) enumeration combined with structural similarity expansion. Finally, by training the optimal quantitative structure-retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. The results demonstrate that this model achieves an <i>R</i> <sup>2</sup> of 0.98 and an average prediction error of 23 s, outperforming currently published models. This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc. Chromatography ; Chemistry Analytical Chemistry and Chromatography