jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sruthi Krishna | Vutukuri Roop Ravi Teja | V. Kushvanth
Source: IET conference proceedings. (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Utilizing chemical and color sensors for machine learning-based fruit spoilage prediction Predicting fruit quality is one of the most important things for agriculture since it helps time harvests and lessen food waste. Traditional methods of ripeness determination tend to be slow and quite subjective, mainly relying on visual assessment. The goal of the project would be to use some chemical and color sensors to develop a more accurate and automated approach to fruit ripeness detection. By coupling machine learning algorithms and sensor data, the project would classify fruits into different ripeness categories. The process intends to deliver greater efficiency, scalability, and precision, allowing farmers to make data-oriented decisions and enhance overall fruit-quality management. The fruit quality prediction system is based on combining chemical and color sensors for ripeness assessment of different fruits. The system uses chemical sensors to determine the chemical composition of the fruit and color sensors to determine the color of the fruit, which are two major attributes to be considered in judging the ripeness of a fruit. The data from the sensors are sent to a Flask web application, the machine learning algorithms predict the ripeness of the fruit as "ripe", "not ripe", "ripe with chemicals" or "ripe without chemicals". This approach is fully automated and increases the accuracy of the assessment of fruit ripeness compared to conventional methods, ensuring better harvest management and preventing food wastage. Food spoilage ; Artificial intelligence ; Food science ; Computer science ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Konstantin Karandashev
Source: ArXiv.org (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Kernel Ridge Regression for conformer ensembles made easy with Structured Orthogonal Random Features A computationally efficient protocol for machine learning in chemical space using Boltzmann ensembles of conformers as input is proposed; the method is based on rewriting Kernel Ridge Regression expressions in terms of Structured Orthogonal Random Features, yielding physics-motivated trigonometric neural networks. To evaluate the method's utility for materials discovery, we test it on experimental datasets of two quantities related to battery electrolyte design, namely oxidation potentials in acetonitrile and hydration energies, using several popular molecular representations to demonstrate the method's flexibility. Despite only using computationally cheap forcefield calculations for conformer generation, we observe systematic decrease of machine learning error with increased training set size in all cases, with experimental accuracy reached after training on hundreds of molecules and prediction errors being comparable to state-of-the-art machine learning approaches. We also present novel versions of Huber and LogCosh loss functions that made hyperparameter optimization of the new approach more convenient. Kernel (algebra) ; Hyperparameter ; Chemical space ; Set (abstract data type) ; Conformational isomerism ; Regression ; Kriging ; Test set ; Ridge ; Hyperparameter optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Wenjia Huo | Boyang Liang | Xiang Wu | Z. Zhang | Weichao Zhou | Haihong Wang | Xinping Ran | Yaoyao Bai | Rongrong Zheng
Source: Polymers (article, 2025-07-30)
Field: Materials Science — Polymers and Plastics

Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning and Molecular Dynamics Simulations The utilization of machine learning (ML) has brought more opportunities for the discovery of high-performance materials with specific properties to replace traditional engineering materials. The glass transition temperature (T<sub>g</sub>) is a crucial characteristic of polyimide (PI). But small datasets can only partially reveal structural information and decrease the ability of the models to learn from the observed data. In this investigation, a dataset comprising 1261 PIs was assembled. A quantitative structure-property relationship targeting T<sub>g</sub> was constructed using nine regression algorithms, with the Categorical Boosting demonstrating the highest accuracy, achieving a coefficient of determination of 0.895 for the test set. SHapley Additive exPlanations analysis identified the NumRotatableBonds descriptor had a significantly negative impact on T<sub>g</sub>. Finally, all-atom molecular dynamics (MD) simulations calculated eight PI structures to verify the accuracy of the prediction model. The ML prediction was consistent with the MD simulation, with the lowest prediction deviation of approximately 6.75%, but the time and resource consumption were tremendously reduced. These findings emphasize the significance of utilizing extensive datasets for model training. This available and interpretable ML framework provides impressive acceleration over the MD simulation and serves as a reference for the structural design of PI with the desired T<sub>g</sub> in the future. Interpretability ; Glass transition ; Molecular dynamics ; Polyimide ; Computer science ; Machine learning ; Regression ; Quantitative structure–activity relationship ; Artificial intelligence ; Boosting (machine learning) ; Spec# ; Test set ; Materials science ; Biological system ; Mathematics ; Chemistry ; Nanotechnology ; Computational chemistry ; Polymer ; Statistics Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Thermal properties of materials
Authors: Yin Shi | Zujian Dai | Xinyang Pan | Lixin He
Source: ArXiv.org (preprint, 2025-09-24)
Field: Materials Science — Materials Chemistry

Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials Deep learning methods for electronic-structure Hamiltonian prediction has offered significant computational efficiency advantages over traditional DFT methods, yet the diversity of atomic types, structural patterns, and the high-dimensional complexity of Hamiltonians pose substantial challenges to the generalization performance. In this work, we contribute on both the methodology and dataset sides to advance universal deep learning paradigm for Hamiltonian prediction. On the method side, we propose NextHAM, a neural E(3)-symmetry and expressive correction method for efficient and generalizable materials electronic-structure Hamiltonian prediction. First, we introduce the zeroth-step Hamiltonians, which can be efficiently constructed by the initial charge density of DFT, as informative descriptors of neural regression model in the input level and initial estimates of the target Hamiltonian in the output level, so that the regression model directly predicts the correction terms to the target ground truths, thereby significantly simplifying the input-output mapping for learning. Second, we present a neural Transformer architecture with strict E(3)-Symmetry and high non-linear expressiveness for Hamiltonian prediction. Third, we propose a novel training objective to ensure the accuracy performance of Hamiltonians in both real space and reciprocal space, preventing error amplification and the occurrence of "ghost states" caused by the large condition number of the overlap matrix. On the dataset side, we curate a high-quality broad-coverage large benchmark, namely Materials-HAM-SOC, comprising 17,000 material structures spanning 68 elements from six rows of the periodic table and explicitly incorporating SOC effects. Experimental results on Materials-HAM-SOC demonstrate that NextHAM achieves excellent accuracy and efficiency in predicting Hamiltonians and band structures. Hamiltonian (control theory) ; Deep learning ; Classifier (UML) ; Artificial neural network ; Regression ; Deep neural networks ; Parameter space Machine Learning in Materials Science
Authors: Anna Wójcicka | Mateusz Wojtulewicz | Andrzej Brodzicki | Magdalena Leonkiewicz | Patryk Maciejewski | Joanna Jaworek-Korjakowska | Krzysztof Mroczka
Source:  (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

AI-Driven Analysis of Fracture Types in Metallic Materials: Classification, Segmentation, and Quantification with Deep Neural Networks Identifying and classifying fracture types in metallic materials is crucial for understanding failure mechanisms, predicting structural integrity, and improving material design. Accurate fracture analysis supports quality control, lifetime assessment, and the development of more resilient metal components in critical applications such as aerospace, automotive, and infrastructure. We propose a deep learning-based framework for automatic classification, segmentation, and quantitative assessment of fracture types in metallic materials. The method distinguishes brittle, ductile, and mixed fractures using microscopic SEM images and employs state-of-the-art convolutional neural networks, including EfficientNet and ConvNeXt, within a transfer learning pipeline. To address the class imbalance and the limited size of the dataset, we implement extensive data augmentation and regularization techniques. The best performing model (EfficientNet-B5) achieves an accuracy of 90.7% and an F1-score of 0.858. In addition to classification, we introduce a novel segmentation-based approach to estimate the level of mixedness on fracture surfaces, utilizing U-Net-like architectures and supported by optimized thresholding and test-time enhancement. This quantitative mixedness assessment enables deeper insight into fracture mechanics and energy absorption processes. The proposed solution demonstrates state-of-the-art performance in both classification and fine-grained fracture composition analysis, offering significant potential for applications in material diagnostics and structural failure analysis. Fracture (geology) ; Convolutional neural network ; Thresholding ; Artificial neural network ; Pattern recognition (psychology) ; Complex fracture Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fatigue and fracture mechanics
Authors: Anubhav Paul | Kumar Rishav | Guus Klootwijk | Mitradeep Sarkar | Onima Bisht | Zizheng Li | Sonia Conesa‐Boj | Georgia T. Papadakis | Silvania F. Pereira
Source: ACS Photonics (article, 2025-12-17)
Field: Materials Science — Polymers and Plastics

Characterization of the 3D-Optical Properties of van der Waals Materials with Deep Learning-Based Coherent Fourier Scatterometry The characterization of optical anisotropy in thin van der Waals (vdW) materials is crucial for both fundamental studies and nanophotonic applications. However, conventional techniques such as spectroscopic ellipsometry face significant limitations in measuring out-of-plane anisotropy and require large-area, uniform films. In this work, we present a novel framework based on coherent Fourier scatterometry (CFS) combined with deep learning for the rapid, label-free characterization of in-plane and out-of-plane refractive indices of anisotropic thin films. We designed a specialized deep neural network, AnisoVision, and trained it on simulated far-field angular spectra from multilayer stacks using the 4 × 4 Berreman matrix formalism. To efficiently capture the directional dependence of anisotropy, we utilize radially polarized light and extract only three far-field azimuthal cross sections (0, 45, 90°), enabling robust retrieval while minimizing data requirements. Our method demonstrates accurate index retrieval for both isotropic and anisotropic materials, including uniaxial h-BN and biaxial α-MoO<sub>3</sub> flakes of varying thickness. We further validate the model's stability by testing multiple flakes of the same material across a range of thicknesses, yielding consistent optical constants. Our approach is single-shot, nondestructive, and applicable to localized sample regions, making it suitable for heterogeneous or exfoliated samples. Additionally, the technique can be readily extended to broadband operation for spectroscopic analysis. Our work establishes CFS coupled with deep learning as a powerful platform for high-throughput optical metrology of low-dimensional materials. Characterization (materials science) ; Anisotropy ; Isotropy ; van der Waals force ; Refractive index ; Metrology ; Ellipsometry ; Coherence (philosophical gambling strategy) ; Fourier transform Transition Metal Oxide Nanomaterials ; 2D Materials and Applications ; Optical Coatings and Gratings
Authors: Qi Du | Feng Wang | Chengkun Wu
Source: IEEE Transactions on Computers (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Parallelization Strategies for DeepMD-Kit Using OpenMP: Enhancing Efficiency in Machine Learning-Based Molecular Simulations DeepMD-kit enables deep learning-based molecular dynamics (MD) simulations that require efficient parallelization to leverage modern HPC architectures. In this work, we optimize DeepMD-kit using advanced OpenMP strategies to improve scalability and computational efficiency on an ARMv8 processor-based server. Our optimizations include data parallelism for neural network inference, force calculation acceleration, NUMAaware memory management, and synchronization reductions, leading to up to 4.1× speedup and 82% higher memory band-width efficiency compared to the baseline implementation. Strong scaling analysis demonstrates superlinear speedup at mid-range core counts, with improved workload balancing and vectorized computations. However, challenges remain at ultra-large scales due to increasing synchronization overhead. Computer science ; Parallel computing ; Computer architecture ; Artificial intelligence ; Computational science Machine Learning in Materials Science ; Advanced Biosensing Techniques and Applications ; Scientific Computing and Data Management
Authors: Sitanan Sartyoungkul | S. Balasubramaniyan | Pavel Sidorov | Yuuya Nagata
Source: Digital Discovery (article, 2025-11-26)
Field: Chemistry — Spectroscopy

Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography Integration of automated synthesis and fragment descriptor-based machine learning enables accurate prediction of SFC retention times and accelerates column characterization. Fragment (logic) ; Retention time ; Column (typography) ; Pattern recognition (psychology) ; Supercritical fluid chromatography ; Automated method ; Automation Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Phase Equilibria and Thermodynamics
Authors: Tien Quang Nguyen | Susan Meñez Aspera | Yingjie Chen | Kaoru Hisama | Michihisa Koyama
Source: Journal of Computer Chemistry Japan (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Anion Intercalation and Selectivity in NiFe LDH: Insights from Neural Network-Enhanced Molecular Dynamics Molecular dynamics (MD) simulations, accelerated by a universal neural network potential, were employed to investigate the dynamic behaviors of ten inorganic anions (Br-, Cl-, F-, OH-, NO3-, H2PO2-, H2PO3-, HPO42-, CO32- and SO42-) intercalated into NiFe layered double hydroxide at varying hydration levels. Our results show that the lattice parameters along the layered double hydroxide (LDH) layers are minimally affected by the intercalated anions and water content, while the lattice parameter perpendicular to the layers, i.e., the basal spacing, is strongly influenced by the type of anion and hydration level. The basal spacing is closely correlated with the ionic radius and charge of the anions, as well as the amount of water uptake. Mean squared displacement (MSD) analysis reveals distinct behaviors of the anions under different hydration conditions. While some anions, such as NO3- and H2PO2-, exhibit noticeable mobility, others remain largely immobilized, primarily due to strong electrostatic interactions with the LDH layers and water molecules. Hydration weakens the interaction between anions and the LDH but also restricts the mobility of anions due to the formation of hydration shells. These findings provide insights into anion dynamics and selectivity in NiFe LDH, which are critical for designing materials for anion removal applications. Intercalation (chemistry) ; Selectivity ; Ion ; Molecular dynamics ; Chemistry ; Artificial neural network ; Chemical physics ; Materials science ; Inorganic chemistry ; Computational chemistry ; Computer science ; Catalysis ; Organic chemistry ; Machine learning Layered Double Hydroxides Synthesis and Applications ; Ionic liquids properties and applications ; Catalytic Processes in Materials Science
Authors: Blaise Awola Ayirizia | Anderson Rocha | Valeria I. Arteaga-Muñiz | Yuhang Tang | Wibe A. de Jong | J. A. Muñoz
Source: Solids (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression for Machine Learning on Effective Crystal Graphs of Body-Centered Cubic Iron Most machine learning algorithms operate on vectorized data with Euclidean structures because of the significant mathematical advantages offered by Hilbert space, but improved representational efficiency may offset more involved learning on non-Euclidean structures. Recently, a method that integrates the marginalized graph kernel into the Gaussian process regression framework was used to learn directly on molecular graphs. Here, we describe an implementation of this method for crystalline materials based on effective crystal graph representations: the molecular graphs of 128-atom supercells of body-centered cubic (BCC) iron with periodic boundary conditions. Regressors trained on hundreds of time steps of a density functional theory molecular dynamics (DFT-MD) simulation achieved root mean square errors of less than 5 meV/atom. The mechanical stability of BCC iron was investigated at high pressure and elevated temperature using regressors trained on short DFT-MD runs, including at conditions found in the inner core of the earth. Phonon dispersions obtained from the short runs show that BCC iron is mechanically stable at 360 GPa when the temperature is above 2500 K. Atoms in the super cell were displaced in the direction of the first, second, and third nearest-neighbors from selected configurations that included thermal atomic displacements, and forces exerted on the displaced atoms were computed by numerical differentiation of the regressors. Gaussian process ; Graph ; Crystal (programming language) ; Gaussian ; Kriging ; Crystal structure ; Kernel (algebra) ; Molecular dynamics ; Phonon Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Hasan Imani Parashkooh | Cuiying Jian
Source: ChemEngineering (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Sustainable Energy: Predicting Adsorption in Aromatic Molecules The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Computer science ; Machine learning ; Artificial intelligence ; Domain (mathematical analysis) ; Retraining ; Process (computing) ; Transfer of learning ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Eric Seng | Hugh O’Connor | Adam M. Boyce | Josh J. Bailey | Anton van Beek
Source: ArXiv.org (preprint, 2025-08-12)
Field: Materials Science — Materials Chemistry

Flow Battery Manifold Design with Heterogeneous Inputs Through Generative Adversarial Neural Networks Generative machine learning has emerged as a powerful tool for design representation and exploration. However, its application is often constrained by the need for large datasets of existing designs and the lack of interpretability about what features drive optimality. To address these challenges, we introduce a systematic framework for constructing training datasets tailored to generative models and demonstrate how these models can be leveraged for interpretable design. The novelty of this work is twofold: (i) we present a systematic framework for generating archetypes with internally homogeneous but mutually heterogeneous inputs that can be used to generate a training dataset, and (ii) we show how integrating generative models with Bayesian optimization can enhance the interpretability of the latent space of admissible designs. These findings are validated by using the framework to design a flow battery manifold, demonstrating that it effectively captures the space of feasible designs, including novel configurations while enabling efficient exploration. This work broadens the applicability of generative machine-learning models in system designs by enhancing quality and reliability. Interpretability ; Generative grammar ; Representation (politics) ; Generator (circuit theory) ; Generative Design ; Generative model ; Flexibility (engineering) ; Quality (philosophy) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Neural Network Applications