jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulong Guo | Zhengwei Huang | Yang Sheng | Yan Teng | Chunyang Li | Chun Li | Ling Jiang
Source: Research Square (preprint, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Discrimination of small sample tea varieties based on convolutional neural network and deep convolutional generative adversarial network enhanced near-infrared diffuse reflectance spectral dataset <title>Abstract</title> Near-infrared diffuse reflectance spectroscopy is widely recognized as a rapid, non-destructive, and environmentally friendly detection technology. However, in order to ensure the accuracy and stability of the detection model, a large number of sample data is necessary. This paper proposed the rapid and non-destructive detection of small sample tea variety recognition based on the near-infrared diffuse reflectance spectrum data extended by convolutional neural network (CNN) and deep convolutional generative adversarial network (DCGAN). The near-infrared diffuse reflectance spectra of 240 tea samples were obtained by Lambda 950 spectrometer using eight of the most popular tea varieties on the Chinese market. Firstly, the spectral data was enhanced using translation, linear superposition, noise addition, and DCGAN methods, and the quality of the generated spectra was evaluated using the support vector machine (SVM) and gradient boosting decision tree (GBDT) methods. Compared with other methods, the DCGAN has the highest accuracy of 91.75%. Secondly, the optimal number of iterations of DCGAN was confirmed to be 6000 by SVM and GBDT methods. To further augment the precision of identifying small samples of tea, two additional classification models of the Extreme Gradient Boosting (Xgboost) and CNN were applied to the DCGAN. Finally, the results demonstrated that the CNN achieved the highest identification accuracy of 98.68% compared with SVM (90.46%), GBDT (90.42%), and Xgboost (88.83%) with an additional 100 samples and 6000 iterations. Therefore, the combination of deep convolutional generative adversarial network enhanced near-infrared diffuse reflectance spectral dataset and the CNN successfully realizes the identification of small sample tea varieties. The experimental results strongly indicate that this method holds significant potential for practical implementation in the field of small sample tea varieties identification. Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; Generative adversarial network ; Sample (material) ; Mathematics ; Deep learning ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: June Anne Caladcad | Eduardo Piedad
Source: arXiv (Cornell University) (preprint, 2024-08-27)
Field: Chemistry — Inorganic Chemistry

Deep learning classification system for coconut maturity levels based on acoustic signals The advancement of computer image processing, pattern recognition, signal processing, and other technologies has gradually replaced the manual methods of classifying fruit with computer and mechanical methods. In the field of agriculture, the intelligent classification of post-harvested fruit has enabled the use of smart devices that creates a direct impact on farmers, especially on export products. For coconut classification, it remains to be traditional in process. This study presents a classification of the coconut dataset based on acoustic signals. To address the imbalanced dataset, a data augmentation technique was conducted through audiomentation and procedural audio generation methods. Audio signals under premature, mature, and overmature now have 4,050, 4,050, and 5,850 audio signals, respectively. To address the updation of the classification system and the classification accuracy performance, deep learning models were utilized for classifying the generated audio signals from data generation. Specifically, RNN and LSTM models were trained and tested, and their performances were compared with each other and the machine learning methods used by Caladcad et al. (2020). The two DL models showed impressive performance with both having an accuracy of 97.42% and neither of them outperformed the other since there are no significant differences in their classification performance. Maturity (psychological) ; Computer science ; Artificial intelligence ; Business ; Psychology ; Developmental psychology Coconut Research and Applications ; Advanced Chemical Sensor Technologies
Authors: Hojung Jung | Yeong-Joon Park | Laurence Schmid | Jaehyeong Jo | Dongkyu Lee | Bongsang Kim | Se-Young Yun | Jinwoo Shin
Source: arXiv (Cornell University) (preprint, 2024-11-01)
Field: Chemistry — Physical and Theoretical Chemistry

Conditional Synthesis of 3D Molecules with Time Correction Sampler Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields. Computer science ; Algorithm Various Chemistry Research Topics ; Chemical Synthesis and Analysis ; Inorganic and Organometallic Chemistry
Authors: Jixin Hou | Xianyan Chen | Taotao Wu | Ellen Kuhl | Xianqiao Wang
Source: arXiv (Cornell University) (preprint, 2024-02-07)
Field: Materials Science — Materials Chemistry

Automated Data-Driven Discovery of Material Models Based on Symbolic Regression: A Case Study on Human Brain Cortex We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, an algorithm based on genetic programming, our approach generates elegant hyperelastic models that achieve accurate data fitting through parsimonious mathematic formulae, while strictly adhering to hyperelasticity constraints such as polyconvexity. Our investigation spans three distinct hyperelastic models -- invariant-based, principal stretch-based, and normal strain-based -- and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression robustly discovers accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch- and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to human brain tissue. Polyconvexity examinations affirm the rigor of convexity within the training regime and demonstrate excellent extrapolation capabilities beyond this regime for all three models. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems. Regression analysis ; Regression ; Computer science ; Brain cortex ; Human brain ; Artificial intelligence ; Neuroscience ; Psychology ; Machine learning ; Statistics ; Mathematics ; Medicine ; Internal medicine Machine Learning in Materials Science ; Image Processing and 3D Reconstruction
Authors: 付丽丽 Fu Lili | 张志强 Zhang Zhiqiang | 徐慧敏 Xu Huimin | 任青颖 Ren Qingying | 郑锐林 Zheng Ruilin | 韦玮 Wei Wei
Source: Laser & Optoelectronics Progress (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

基于机器学习的光学功能玻璃研究进展 光功能玻璃材料在研究过程中所涉及到的研发周期长和效率低的问题，极大阻碍了光学玻璃材料的发展。机器学习技术的出现对玻璃材料学发展起到了极大的推动作用，通过学习数据蕴含的规律，在庞大而复杂的玻璃数据中学习并预测新数据，加快了光学功能玻璃的研发进程。本文总结并展示了在光学玻璃预测中涉及到的几类机器学习算法并对其做了简要介绍，在此基础上重点介绍这些理论算法在玻璃研究工作中的重要应用，包括加速和改进传统玻璃研究方法、助力玻璃的成分-性质关联预测以及对光学玻璃的配方设计的建议等方面的内容，最后对机器学习在光功能玻璃研究中的应用前景以及未来发展趋势做了分析与展望。 Computer science Machine Learning in Materials Science
Authors: Mona S. Minkara | Tyler Locke
Source: Biophysical Journal (article, 2024-02-01)
Field: Chemistry — Organic Chemistry

Dynamics of surfactant protein B at the alveolar air-liquid interface: Insights from molecular modeling and simulations Pulmonary surfactant ; Molecular dynamics ; Dynamics (music) ; Interface (matter) ; Chemistry ; Chemical physics ; Materials science ; Biophysics ; Physics ; Computational chemistry ; Biochemistry ; Biology Surfactants and Colloidal Systems ; Inhalation and Respiratory Drug Delivery ; Lipid Membrane Structure and Behavior
Authors: Mohammadreza Lotfalian | Mitra Athari Allaf | Masoud Mansouri
Source: arXiv (Cornell University) (preprint, 2024-06-05)
Field: Materials Science — Materials Chemistry

Lithium Borohydride (LiBH4): An Innovative Material for Neutron Radiation Shielding Radiation shielding plays a crucial role in various industries, including nuclear and space exploration. Among the most abundant elements and isotopes found in nature, 10B has one of the highest neutron absorption cross-sections, closely followed by 6Li. It is worth noting that hydrogen, with its light nucleus, serves as an excellent neutron reflector. Surprisingly, the potential of the lithium borohydride molecule (LiBH4), which consists exclusively of these elements, as a shield against neutron radiation has not yet been explored. This study investigates various materials that can potentially be used as shields. First, we assessed traditional shields and previous optimizations for shielding. The findings showed that concrete containing 10% B4C yielded the best results. High-performance concrete (HPC) replaced regular concrete. By gradually incorporating lithium borohydride into the shield, along with the appropriate level of boron carbide, further optimization was achieved. Calculations were performed using the MCNPX 2.7E code. The introduction of the new shield resulted in a significant 40% reduction in volume compared with the previous sample. The study findings showed that a 30 cm thick shield effectively blocked 95% of the total neutrons and 92% of the total gamma radiation. Additionally, it was noted that the shielding effects of lithium borohydride against fast neutrons are greater than those of boron carbide. Various parameters and data of the designed shield were calculated and compared with those of the previous sample. Electromagnetic shielding ; Radiation shielding ; Neutron ; Radiochemistry ; Borohydride ; Nuclear engineering ; Materials science ; Lithium (medication) ; Radiation ; Nuclear chemistry ; Nuclear physics ; Chemistry ; Physics ; Composite material ; Engineering ; Medicine ; Organic chemistry ; Catalysis Hydrogen Storage and Materials ; Nuclear Physics and Applications ; Nuclear physics research studies
Authors: T. M. Kamsma | M. S. Klop | W. Q. Boon | Cristian Spitoni | Bodo Rueckauer | R. van Roij
Source: arXiv (Cornell University) (preprint, 2024-06-05)
Field: Chemistry — Organic Chemistry

Chemically Regulated Conical Channel Synapse for Neuromorphic and Sensing Applications Fluidic iontronics offer a unique capability for emulating the chemical processes found in neurons. We extract multiple distinct chemically regulated synaptic features from an experimentally accessible conical microfluidic channel carrying functionalized surface groups, using finite-element calculations of continuum transport equations. By modeling a Langmuir-type surface reaction on the channel wall we couple fast voltage-induced volumetric salt accumulation with a long-term channel surface charge modulation by means of fast charging and slow discharging. These nonlinear charging dynamics emerge across several orders of magnitude of reaction rates and equilibria, and are understood through an analytic approximation rooted in first-principles. We show how short-and long-term potentiation and depression, frequency-dependent plasticity, and chemical-electrical signal spike-timing dependence and coincidence detection (acting like a chemical-electrical AND logic gate), akin to the NMDA mechanism for Hebbian learning in biological synapses, can all be emulated. Neuromorphic engineering ; Synapse ; Conical surface ; Channel (broadcasting) ; Neuroscience ; Computer science ; Computer architecture ; Nanotechnology ; Materials science ; Engineering ; Telecommunications ; Artificial intelligence ; Psychology ; Artificial neural network ; Mechanical engineering Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Molecular Junctions and Nanostructures
Authors: Philipp Rieder | Orkun Furat | Francois L. E. Usseglio‐Viretta | Jeffery M. Allen | Peter J. Weddle | Donal P. Finegan | Kandler Smith | Volker Schmidt
Source: arXiv (Cornell University) (preprint, 2024-10-15)
Field: Materials Science — Surfaces, Coatings and Films

Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data Li-ion battery performance is strongly influenced by their cathodes' properties and consequently by the 3D microstructure of the particles the cathodes are comprised of. During calendaring and cycling, cracks develop within cathode particles, which may affect performance in multiple ways. On the one hand, cracks reduce internal connectivity such that electron transport within cathode particles is hindered. On the other hand, intra-particle cracks can increase the cathode reactive surface. Due to these contradictory effects, it is necessary to quantitatively investigate how battery cycling effects cracking and how cracking in-turn influences battery performance. Thus, it is necessary to characterize the 3D particle morphology with structural descriptors and quantitatively correlate them with effective battery properties. Typically, 3D structural characterization is performed using image data. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles that exhibit crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, more easily available 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. In future research, the virtually generated 3D particles will be used as geometry input for spatially resolved electro-chemo-mechanical simulations, to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries. Crystallite ; Materials science ; Computer science ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Cultural Heritage Materials Analysis
Authors: Edwin Smith Rivera Fernandez | Ronal Andres Ceballo | Oscar Leon Neira Bueno | Oriana Ines Avila Salas | Ruben Dario Fonseca Rodriguez
Source: Research Square (preprint, 2024-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DFT Study of the Influence of Cationic and Anionic Fragments on the Linear Optical Properties, First Hyperpolarizability, and Two-Photon Absorption of Benzoxazole Derivatives: A Perspective for OLEDs and Solar Cells Applications Hyperpolarizability ; Benzoxazole ; Cationic polymerization ; Absorption (acoustics) ; Two-photon absorption ; Photochemistry ; Perspective (graphical) ; OLED ; Materials science ; Chemistry ; Polymer chemistry ; Optics ; Nanotechnology ; Organic chemistry ; Layer (electronics) ; Physics ; Molecule ; Computer science Nonlinear Optical Materials Research ; Photochemistry and Electron Transfer Studies ; Nonlinear Optical Materials Studies
Authors: Thomas Galvani | Ali Hamze | Laura Caputo | Onurcan Kaya | Simon M.‐M. Dubois | Luigi Colombo | Việt Hùng Nguyễn | Yongwoo Shin | Hyeon‐Jin Shin | Jean‐Christophe Charlier | Stephan Roche
Source: arXiv (Cornell University) (preprint, 2024-03-18)
Field: Materials Science — Materials Chemistry

Exploring Dielectric Properties in Models of Amorphous Boron Nitride We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about $100$ atoms in the periodic cell) sample sizes to explore the emergence of mid-gap states and its correlation with structural features. Next, by using a simplified tight-binding electronic model, we analyse the dielectric functions for complex three dimensional models (containing about $10.000$ atoms) embedding varying concentrations of ${\rm sp^{1}, sp^{2}}$ and ${\rm sp^3}$ bonds between B and N atoms. Within the limits of these methodologies, the resulting value of the zero-frequency dielectric constant is shown to be influenced by the population density of such mid-gap states and their localization characteristics. We observe nontrivial correlations between the structure-induced electronic fluctuations and the resulting dielectric constant values. Our findings are however just a first step in the quest of accessing fully accurate dielectric properties of as-grown amorphous BN of relevance for interconnect technologies and beyond. Boron nitride ; Dielectric ; Amorphous solid ; Materials science ; Boron ; Nitride ; Composite material ; Optoelectronics ; Crystallography ; Chemistry Boron and Carbon Nanomaterials Research ; Thermal properties of materials ; Semiconductor materials and devices
Authors: Julián G. Fernández | Guéric Etesse | Natalia Seoane | Enrique Comesaña | Kazuhiko Hirakawa | Antonio J. García‐Loureiro | Marc Bescond
Source: Scientific Reports (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibirum Green's function for electrons with the heat equation (NEGF+H), which allows to accurately describe the energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This methodology, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature ([Formula: see text]). Using a vast search space of [Formula: see text] different device configurations, we obtained a set of optimum devices with prediction relative errors lower than [Formula: see text] for CP and [Formula: see text] for T<sub>e</sub>. The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10 s to find the optimum designs. Workflow ; Computer science ; Lattice (music) ; Coupling (piping) ; Heterojunction ; Computational science ; Physics ; Optoelectronics ; Mechanical engineering Machine Learning in Materials Science ; Thermal properties of materials ; Semiconductor materials and devices
Authors: Shiv Prakash
Source: arXiv (Cornell University) (preprint, 2024-09-07)
Field: Materials Science — Materials Chemistry

Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets The blood-brain barrier (BBB) serves as a protective barrier that separates the brain from the circulatory system, regulating the passage of substances into the central nervous system. Assessing the BBB permeability of potential drugs is crucial for effective drug targeting. However, traditional experimental methods for measuring BBB permeability are challenging and impractical for large-scale screening. Consequently, there is a need to develop computational approaches to predict BBB permeability. This paper proposes a GPS Transformer architecture augmented with Self Attention, designed to perform well in the low-data regime. The proposed approach achieved a state-of-the-art performance on the BBB permeability prediction task using the BBBP dataset, surpassing existing models. With a ROC-AUC of 78.8%, the approach sets a state-of-the-art by 5.5%. We demonstrate that standard Self Attention coupled with GPS transformer performs better than other variants of attention coupled with GPS Transformer. Training (meteorology) ; Property (philosophy) ; Transformer ; Training set ; Computer science ; Scale (ratio) ; Artificial intelligence ; Data mining ; Machine learning ; Engineering ; Cartography ; Geography ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andres Correa Hernandez | Claire Gmachl
Source: arXiv (Cornell University) (preprint, 2024-06-11)
Field: Chemistry — Spectroscopy

A Machine Learning Framework for Quantum Cascade Laser Design A multi-layer perceptron neural network was used to predict the laser transition figure of merit, a measure of the laser threshold gain, of over 900 million Quantum Cascade Laser designs using only layer thicknesses and the applied electric field as inputs. Designs were generated by randomly altering the layer thicknesses of an initial 10-layer design. Validating the predictions with our 1D Schrödinger solver, the predicted values show 5% to 15% error for structures where a laser transition could occur, and 35% to 70% error for structures where there was no laser transition. The algorithm allowed (i) for the identification of high figure of merit structures, (ii) recognition of which layers should be altered to maximize the figure of merit at a given electric field, and (iii) increased the original design figure of merit of 94.7 to 141.2 eV ps Å^2, a 1.5-fold improvement and significant for QC lasers. The computational time for laser design data collection is greatly reduced from 32 hours for 27000 designs using our 1D Schrödinger solver on a virtual machine, to 8 hours for 907 million designs using the machine learning algorithm on a laptop computer. Cascade ; Quantum cascade laser ; Quantum ; Laser ; Computer science ; Physics ; Engineering ; Optics ; Quantum mechanics Spectroscopy and Laser Applications ; Laser Design and Applications