jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fangning Ren | Xu Chen | Fang Liu
Source: ChemRxiv (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Size-transferable prediction of excited state properties for molecular assemblies with machine-learned exciton model Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We trained an ML model on perylene dimer exciton Hamiltonians using quantum chemistry descriptors to resolve phase correction issues. Our model accurately predicted excitation energies and oscillator strengths in perylene trimers and tetramers, and analyzed the size-dependent optical bandgap in aggregates with up to 50 monomers, revealing quantum dot-like confinement effects. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies. Excited state ; Exciton ; State (computer science) ; Computer science ; Physics ; Materials science ; Atomic physics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing
Authors: Shraddha Shukla | Manikandan
Source: Research Square (preprint, 2024-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Supercapacitor Performance for Cobalt Oxide and Its Derivatives using Machine Learning Techniques <title>Abstract</title> Supercapacitors' high-power density and quick charge-discharge times have made them stand out as potential energy storage technologies. The electrochemical properties of cobalt oxide and its derivatives have sparked substantial interest in their application as electrode materials for supercapacitors. This study aims to predict the performance of supercapacitors utilizing cobalt oxide and its derivatives through different machine learning models. The prediction model integrates material properties, electrode morphology, and electrolyte characteristics to forecast key performance metrics such as specific capacitance, energy density, power density and cyclic stability. By employing advanced computational tools, this research contributes to the understanding and optimization of cobalt oxide-based supercapacitors, facilitating their use in many different systems that stores energy. The experimental results such as high TPR values of all the grades (0.87 for grade A, 0.91 for grade B, 0.62 for grade C and 0.79 for grade D) on applying RF model confirm the predictive methodology outlined in this study. Furthermore, according to this study, MLP model combined with bagging (Bagging + MLP) is a better model as it gives least MAE and RMSE of 0.15 and 0.30 respectively with a precision of 0.642, recall of 0.562, accuracy of 0.836 and F1 score of 0.579. Supercapacitor ; Cobalt oxide ; Cobalt ; Oxide ; Materials science ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Capacitance ; Metallurgy Supercapacitor Materials and Fabrication
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Supplementary Document S2 from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;p&gt;Next-generation sequencing&lt;/p&gt; Artificial intelligence ; Posterior fossa ; Computer science ; Pattern recognition (psychology) ; Natural language processing ; Biology ; Anatomy Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Kentaro Fuku | Takefumi Yoshida
Source: ChemRxiv (preprint, 2024-06-17)
Field: Chemistry — Analytical Chemistry

Unsupervised Raw IR Spectra Images Recognition: Toward Chemist-Like Chemical Structure Classification Recent advances in artificial intelligence have significantly improved the spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. This study demonstrated the potential of machine learning for chemical classification by extracting IR spectral images from the Spectral Database for Organic Compounds (SDBS) database and converting them into 218196-dimensional vector data. Hierarchical clustering of 227 compounds revealed distinct main clusters (A-G), each with specific sub-clusters exhibiting higher intra-cluster similarity. Despite challenges, including sensitivity to spectral deviations and difficulty in distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, this image recognition approach exhibits potential. The Tanimoto coefficient has been used as a metric for molecular similarity, providing valuable insights, although sometimes diverging from chemists’ intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. Overall, this research demonstrates the feasibility of using IR spectral image data in machine learning for chemical classification, offering a novel perspective that complements traditional methods, although some classifications may not always align with chemists’ intuition. Chemist ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Emanuele Costa | Giuseppe Scriva | Sebastiano Pilati
Source: arXiv (Cornell University) (preprint, 2024-03-14)
Field: Materials Science — Materials Chemistry

Solving deep-learning density functional theory via variational autoencoders In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization. Indeed, any small noise can lead astray from realistic profiles, causing the failure of the learned functional and, hence, strong violations of the variational property. In this article, we employ variational autoencoders to build a compressed, flexible, and regular representation of the ground-state density profiles of various quantum models. Performing energy minimization in this compressed space allows us to avoid both numerical instabilities and variational biases due to excessive constraints. Our tests are performed on one-dimensional single-particle models from the literature in the field and, notably, on a three-dimensional disordered potential. In all cases, the ground-state energies are estimated with errors below the chemical accuracy and the density profiles are accurately reproduced without numerical artifacts. Artificial intelligence ; Computer science ; Deep learning ; Density functional theory ; Cognitive science ; Machine learning ; Psychology ; Chemistry ; Computational chemistry Machine Learning in Materials Science
Authors: Rhowell N. Tiozon | Reuben James Q. Buenafe | A. T. Herath | Nese Sreenivasulu | Vito M. Butardo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Non-Destructive Prediction of Polyphenolic Content in Wholegrain Pigmented Rice Using Surface Infrared Spectroscopy and Multivariate Machine Learning Models Multivariate statistics ; Polyphenol ; Multivariate analysis ; Infrared spectroscopy ; Spectroscopy ; Content (measure theory) ; Near-infrared spectroscopy ; Chemistry ; Biological system ; Food science ; Computer science ; Mathematics ; Machine learning ; Physics ; Optics ; Biochemistry ; Biology ; Organic chemistry Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Metabolomics and Mass Spectrometry Studies
Authors: Pol Timmer | Koen Minartz | Vlado Menkovski
Source: arXiv (Cornell University) (preprint, 2024-05-26)
Field: Materials Science — Materials Chemistry

Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science and metallurgy. These processes are highly nonlinear, stochastic, and sensitive to small perturbations of system parameters and initial conditions. Methods for the simulation of these processes have been developed using discrete numerical models, but these are computationally expensive. This work aims to scale crystal growth simulation with a machine learning emulator. Specifically, autoregressive latent variable models are well suited for modeling the joint distribution over system parameters and the crystallization trajectories. However, successfully training such models is challenging due to the stochasticity and sensitivity of the system. Existing approaches consequently fail to produce diverse and faithful crystallization trajectories. In this paper, we introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth emulation at the mesoscopic scale that overcomes these challenges. We validate CGNE results using the morphological properties of the crystals produced by numerical simulation. CGNE delivers a factor of 11 improvement in inference time and performance gains compared with recent state-of-the-art probabilistic models for dynamical systems. Mesoscopic physics ; Probabilistic logic ; Crystallization ; Scale (ratio) ; Statistical physics ; Computer science ; Physics ; Artificial intelligence ; Thermodynamics ; Condensed matter physics ; Quantum mechanics Crystallization and Solubility Studies
Authors: Hyun Kil Shin | Youngho Sihn
Source: Research Square (preprint, 2024-09-27)
Field: Chemistry — Inorganic Chemistry

Quantitative structure-activity relationship model to predict the stability constant of Uranium coordination complexes for novel uranium adsorbent design Uranium ; Adsorption ; Constant (computer programming) ; Stability (learning theory) ; Chemistry ; Materials science ; Physical chemistry ; Computer science ; Metallurgy Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes
Authors: Chaoqiang Feng | Yaolong Zhang | Bin Jiang
Source: arXiv (Cornell University) (preprint, 2024-10-07)
Field: Materials Science — Surfaces, Coatings and Films

Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space Electron density is a fundamental quantity, which can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for the number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines a targeted sampling favoring large density and a screening of grid points associated with linearly independent atomic features. This new sampling strategy is integrated with a field-induced recursively embedded atom neural network model to develop a real-space grid-based ML model for electron density and its response to an electric field. This approach is applied to a QM9 molecular dataset, a H2O/Pt(111) interfacial system, and an Au(100) electrode under an electric field. The number of training points is found much smaller than previous models, when yielding comparably accurate predictions for the electron density of the entire grid. The resultant machine learned electron density model enables us to properly partition partial charge onto each atom and analyze the charge variation upon proton transfer in the H2O/Pt(111) system. The machined learned electronic response model allows us to predict charge transfer and the electrostatic potential change induced by an electric field in an Au(100) electrode. Space (punctuation) ; Computer science ; Sampling (signal processing) ; Machine learning ; Artificial intelligence ; Computer vision Electron and X-Ray Spectroscopy Techniques
Authors: Norbertus Tri Suswanto Saptadi | Sean Coonery Sumarta | Enrico Juang
Source:  (article, 2024-08-29)
Field: Chemistry — Inorganic Chemistry

Coconut Shell Charcoal Classification Model for Organic Raw Materials Using CNN Coconut shell charcoal is the basic raw material for biomass briquette products obtained in Sulawesi, Indonesia and has high quality value. Quality briquette products require organic raw materials from Indonesian National Standard coconut shell charcoal. The research aims to design a classification model by sorting and selecting bad, poor, and good types of charcoal according to the fulfillment needs to produce briquette products. An approach based on artificial intelligence technology with deep learning methods through Convolution Neural Network (CNN) architecture. The classification model determines the qualities needed for the sustainability of companies, the industrial world and households. The research produced a classification model to obtain quality organic raw materials with a training accuracy value of 0.92 and validation accuracy of 0.88 so that the model can be used to detect coconut shell charcoal better and more accurately. Charcoal ; Raw material ; Shell (structure) ; Computer science ; Artificial intelligence ; Pulp and paper industry ; Pattern recognition (psychology) ; Materials science ; Composite material ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry Coconut Research and Applications ; Material Selection and Properties ; Materials Engineering and Processing
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2024-05-15)
Field: Materials Science — Materials Chemistry

Generative Foundation Model for On-demand Reverse Polymer Design Forward screening and reverse design of drug molecules, inorganic molecules, and polymers with better properties are crucial engines for shortening the laboratory-to-market cycle. Particularly, due to the lack of large-scale datasets, polymer discovery based on materials informatics is more formidable. Despite this, polymer scientists have developed a series of machine learning models on polymer structure-property relationships using only small polymer datasets, thereby driving the forward screening process of polymers. However, the success of this paradigm ultimately hinges on the capacity of the candidate pool, while exhaustively enumerating all polymer structures through human imagination is challenging. Therefore, achieving on-demand reverse design of polymers is crucial. In this work, we curate a polymer dataset containing nearly one million polymeric structure-property pairs based on expert intuition. Using this dataset, we propose a generative pre-trained model for polymer on-demand generation using a large language model. The model produce polymers with 99.27\% chemical validity in top-1 generation mode (approximately 200k generated polymers), marking the highest reported success rate among polymer generative models. In addition, the average $R^2$ between the properties of the generated molecules and their expected values across 15 predefined properties is 0.96. To further assess the pre-trained model's performance in generating additional user-defined polymer properties for downstream tasks, we conduct fine-tuning experiments on three publicly available small polymer datasets using semi-template and template-free generation paradigm. Through these extensive experiments, we demonstrate that our pre-trained model and fine-tuned models are capable of achieving on-demand reverse design of polymers with specified properties, whether in (semi-)template generation or the more challenging template-free generation scenarios. Polymer ; Generative grammar ; Computer science ; Generative model ; Artificial intelligence ; Intuition ; Materials science ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Manxiang Huang | Q. Lin | Honghao Cai | Hui Ni
Source: Brazilian Journal of Poultry Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fast Recognition of Table Eggs from Different Farming Systems Using Physical Traits and Multi-layer Perceptron ABSTRACT Eggs are a widely consumed source of protein, with consumers often preferring free-range eggs due to their higher nutritive value and prices. However, dishonest traders sometimes mislabel cage eggs as free-range eggs for unjustified profits. Biochemical methods are currently used to differentiate between caged and free-range eggs, which could involve chemical reagents, sample preparation, and costly instruments. In this study, physical traits measurements were combined with machine learning to identify eggs according to their farming system. Measurements of 27 physical traits for 480 eggs were conducted using simple tools, and the multicollinearity was reduced by comparing correlation coefficients, resulting in 16 physical traits. Multi-layer Perceptron Neural Network, Naive Bayes, Linear Support Vector Classifier, Radial Basis Functions Support Vector Classifier and Random Forest were used to create recognition models, and the leave-one-out cross-validation method was used for training and evaluation. The Multi-layer Perceptron Neural Network achieved the best classification performance with an accuracy of 0.94167, a F1 score of 0.94118. The result demonstrates that the physical traits of eggs provide sufficient features for the Multi-layer Perceptron Neural Network classifier. Compared to mainstream biochemical methods, we proposed a novel approach to differentiate between caged and free-range eggs using only physical trait measurements, thereby avoiding the need for chemical reagents, sample preparation, and expensive instruments. Table (database) ; Layer (electronics) ; Perceptron ; Computer science ; Artificial intelligence ; Data mining ; Artificial neural network ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Ying Fang | Hezhu Shao
Source: arXiv (Cornell University) (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Wenzhou TE: a first-principles calculated thermoelectric materials database Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics. Thermoelectric effect ; Database ; Materials science ; Physics ; Computer science ; Thermodynamics X-ray Diffraction in Crystallography
Authors: Yu-Fan Lin | Ching-Heng Cheng | Bo-Cheng Qiu | Cheng-Jun Kang | Chia-Ming Lee | Chih–Chung Hsu
Source: arXiv (Cornell University) (preprint, 2024-08-31)
Field: Chemistry — Analytical Chemistry

Self-supervised Fusarium Head Blight Detection with Hyperspectral Image and Feature Mining Fusarium Head Blight (FHB) is a serious fungal disease affecting wheat (including durum), barley, oats, other small cereal grains, and corn. Effective monitoring and accurate detection of FHB are crucial to ensuring stable and reliable food security. Traditionally, trained agronomists and surveyors perform manual identification, a method that is labor-intensive, impractical, and challenging to scale. With the advancement of deep learning and Hyper-spectral Imaging (HSI) and Remote Sensing (RS) technologies, employing deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a promising solution. Notably, wheat infected with serious FHB may exhibit significant differences on the spectral compared to mild FHB one, which is particularly advantageous for hyperspectral image-based methods. In this study, we propose a self-unsupervised classification method based on HSI endmember extraction strategy and top-K bands selection, designed to analyze material signatures in HSIs to derive discriminative feature representations. This approach does not require expensive device or complicate algorithm design, making it more suitable for practical uses. Our method has been effectively validated in the Beyond Visible Spectrum: AI for Agriculture Challenge 2024. The source code is easy to reproduce and available at {https://github.com/VanLinLin/Automated-Crop-Disease-Diagnosis-from-Hyperspectral-Imagery-3rd}. Hyperspectral imaging ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Fusarium ; Computer science ; Head (geology) ; Image (mathematics) ; Biology ; Horticulture Spectroscopy and Chemometric Analyses
Authors: Matthias Rupp | Emine Küçükbenli | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

Guest Editorial: Special Topic on Software for Atomistic Machine Learning A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning. Computer science ; Software ; Software engineering ; Artificial intelligence ; Data science ; Programming language Machine Learning in Materials Science