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arXiv 2024年

作者： Guo, Gabe Saidi, Tristan Luca Terban, Maxwell W. Valsecchi, Michele Billinge, Simon J.L. Lipson, Hod Columbia University Department of Computer Science New YorkNY10027 United States Momentum Transfer Mannheim68169 Germany Columbia University Department of Chemical Engineering New YorkNY10027 United States Columbia University Department of Applied Physics and Applied Mathematics New YorkNY10027 United States Columbia University Department of Mechanical Engineering New YorkNY10027 United States Stanford University Department of Computer Science StanfordCA94305 United States

A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known structures. The model factors both the measured diffraction pattern as well as relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-size broadened powder diffraction pattern, we find that our model, PXRDNET, can successfully solve simulated nanocrystals as small as 10 Å across 200 materials of varying symmetry and complexity, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with average error among these candidates being only 7% (as measured by post-Rietveld refinement R-factor). Furthermore, PXRDNET is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nano-materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Rietveld refinement

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Application of Fuzzy Support Vector Regression to Predict the Kovats Retention Indices of Flavors and Fragrances

Application of Fuzzy Support Vector Regression to Predict th...

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2022 International Conference on Electrical engineering and Informatics, ICELTICs 2022

作者： Agustia, Mariza Noviandy, Teuku Rizky Maulana, Aga Suhendra, Rivansyah Muslem, Muslem Sasmita, Novi Reandy Idroes, Ghazi Mauer Rahimah, Souvia Afidh, Razief Perucha Fauzie Subianto, Muhammad Irvanizam, Irvanizam Idroes, Rinaldi Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh23111 Indonesia Universitas Teuku Umar Faculty of Engineering Department of Information Technology Aceh Barat 23681 Indonesia Universitas Islam Negeri Ar-Raniry Faculty of Science and Technology Department of Chemistry Banda Aceh23111 Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Computational and Applied Statistics Research Group Department of Statistics Banda Aceh23111 Indonesia Universitas Syiah Kuala Faculty of Engineering Department of Chemical Engineering Banda Aceh23111 Indonesia Universitas Padjadjaran Faculty of Agroindustrial Technology Department of Food Industrial Technology Bandung Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

The Kovats retention index is the most robust retention parameter used to identify analytes in gas chromatography. The Kovats retention index's value in similar compounds tends to have a similar pattern. This pattern allows a close relationship between chemical structure and the retention index value. The Fuzzy Support Vector Regression (FSVR) method is a robust algorithm that has been used to solve various regression problems. This study aimed to evaluate the FSVR algorithm performance to predict the Kovats retention index of flavor and fragrance compounds. The model was built using the Python programming language. The retention index value calculation begins with inputting the molecular descriptor data into the sigmoid fuzzy membership function. The dataset consists of 51 compounds then partitioned into two sets, namely 80% in the training set and 20% in the testing set. The best parameters to build the model were obtained using hyperparameter tuning. The kernels used are linear kernel and Radial Basis Function (RBF) kernel. The model with a linear kernel with parameter values of C = 1000 and epsilon = 10 managed to get the best results compared to the model with an RBF kernel, with R2 = 0.977 and Root Mean Square Error (RMSE) = 37.21 on the testing set. This model also can predict the Kovats retention index on the testing set with an average difference of 2.8%. These results showed that the linear kernel is better than the RBF kernel in predicting the value of the Kovats retention index. It can be concluded that the FSVR method can be used to predict the retention index of flavor and fragrance compounds. © 2022 IEEE.

关键词： Forecasting

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Analyzing a Higher Order $Q(T)$ Model and its Implications in the Late Evolution of the Universe Using Recent Observational Datasets

SSRN

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SSRN 2023年

作者： Khurana, Madhur Chaudhary, Himanshu Mumtaz, Saadia Pacif, Shibesh Kumar Jas Mustafa, G. Department of Applied Physics Delhi Technological University Delhi110042 India Department of Applied Mathematics Delhi Technological University Delhi110042 India Sagara Odisha Sambalpur768224 India Department of Mathematics Shyamlal College University of Delhi Delhi110032 India Institute of Chemical Engineering and Technology University of the Punjab Quaid-e-Azam Campus Lahore54590 Pakistan Department of Physics Zhejiang Normal University Jinhua321004 China

In this research paper, we explore a well-motivated parametrization of the time-dependent deceleration parameter, characterized by a cubic form, within the context of late-time cosmic acceleration. The current analysis is based on the $f(Q,T)$ gravity theory, by considering the background metric as the homogeneous and isotropic Friedmann–Lemaître–Robertson–Walker (FLRW) metric. Investigating the model reveals intriguing features of the late universe. To constrain the model, we use the recent observational datasets, including cosmic chronometer (CC), Supernovae (SNIa), Baryon Acoustic Oscillation (BAO), Cosmic Microwave Background Radiation (CMB), Gamma-ray Burst (GRB), and Quasar (Q) datasets. The joint analysis of these datasets results in tighter constraints for the model parameters, enabling us to discuss both the physical and geometrical aspects of the model. Moreover, we determine the present values of the deceleration parameter ($q_0$), the Hubble parameter ($H_0$), and the transition redshift ($z_t$) from deceleration to acceleration ensuring consistency with some recent results of Planck 2018. Our statistical analysis yields highly improved results, surpassing those obtained in previous investigations. Overall, this study presents valuable insights into the higher order $q(t)$ model and its implications for late-time cosmic acceleration, shedding light on the nature of the late universe. © 2023, The Authors. All rights reserved.

关键词： Cosmology

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Emulating complex synapses using interlinked proton conductors

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Physical Review applied 2025年第1期23卷 014027-014027页

作者： Lifu Zhang Ji-An Li Yang Hu Jie Jiang Rongjie Lai Marcus K. Benna Jian Shi Department of Materials Science and Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA Neurosciences Graduate Program University of California San Diego La Jolla California 92093 USA Department of Mathematics Purdue University West Lafayette Indiana 47907 USA Department of Neurobiology School of Biological Sciences University of California at San Diego La Jolla California 92093 USA Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA

In terms of energy efficiency and computational speed, neuromorphic electronics based on nonvolatile memory devices are expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains a pivotal obstacle in either digital or analog AI chips for unleashing the true power of brainlike computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) was proposed recently [M. K. Benna and S. Fusi, Nat. Neurosci. 19, 1697 (2016)], the synaptic weight and internal variables of which evolve following diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. Memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers and the capacity and number of coupled storage components. The advantages of the demonstrated complex synapse for both memory capacity and memory consolidation are revealed by neural network simulations of face-familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.

关键词： Nonvolatile storage

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Fabrication and bioactivity studies of wollastonite/polycaprolactone composites

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International Journal of Nano and Biomaterials 2023年第2期10卷 86-99页

作者： Lakshmi, R. Choudhary, Rajan Senatov, Fedor Kaloshkin, Sergey Kothandam, Shobana Ponnamma, Deepalekshmi Sadasivuni, Kishor Kumar Swamiappan, Sasikumar Department of Chemistry Auxilium College Tamil Nadu Vellore632006 India Faculty of Materials Science and Applied Chemistry Rudolfs Cimdins Riga Biomaterials Innovations and Development Centre of RTU Institute of General Chemical Engineering Riga Technical University Pulka St 3 RigaLV-1007 Latvia Baltic Biomaterials Centre of Excellence Headquarters at Riga Technical University Kipsala Street 6A RigaLV-1048 Latvia National University of Science and Technology "MISiS" Moscow119049 Russia Department of Chemistry School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore632014 India Materials Science and Technology Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University P.O. Box 2713 Doha Qatar Center for Advanced Materials Qatar University P.O. Box 2713 Doha Qatar Department of Chemistry School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore632014 India

Current work investigates the influence of wollastonite on mechanical stability and biomineralisation ability of wollastonite/ polycaprolactone (PCL) composites (WP composites). The solvent casting particulate leaching method was used to prepare these composites. FT-IR spectra indicated the presence of functional groups associated with PCL and wollastonite. Scanning electron microscopy showed aggregated morphology and wollastonite particles were embedded in the PCL matrix. The incorporation of wollastonite leads to a more than three-fold increase in the compressive strength of the composites. Moreover, the composites containing 50% wollastonite exhibited better compressive strength than 80%. This observation suggested that if the concentration of a filler is exceeded beyond its critical concentration then the mechanical stability of the composite will be compromised. The composites revealed good apatite deposition ability. Thus, the biomineralisation ability and compressive strength of PCL can be improved by preparing its composites with bioceramics. © 2023 Inderscience Enterprises Ltd.

关键词： Bioceramics

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Mass transfer study of phenolic compound liquid-liquid extraction process from bio-oil of coffee shell pyrolysis product

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Materials Today: Proceedings 2023年

作者： Fardhyanti, Dewi Selvia Kadarwati, Sri Hadiyanto Fatriasari, Widya Prasetiawan, Haniif Chemical Engineering Department Faculty of Engineering Universitas Negeri Semarang Kampus Sekaran Gunungpati 50229 Indonesia Department of Chemistry Faculty of Mathematics and Natural Science Universitas Negeri Semarang 50229 Indonesia Center of Biomass and Renewable Energy Department of Chemical Engineering Diponegoro University Jl. Prof. Soedarto SH Tembalang Semarang 50275 Indonesia Graduate Program of Environmental Science School of Postgraduate Studies Diponegoro University Jl. Imam Bardjo SH Semarang Indonesia Chemical Engineering Department Faculty of Engineering Diponegoro University Semarang Indonesia Jl Raya Bogor KM 46 Cibinong 16911 Indonesia

Coffee shells are one of the potential renewable materials used in the pyrolysis process to produce bio-oil. However, the amount of phenolic compounds, which is roughly 36.74%, is one of the primary drawbacks of using coffee shell bio-oil as a fuel source. Only a little amount of research has been done on the mass transfer modeling and phenomena of phenol extraction from coffee shell bio-oil. The objectives of this study are to identify the best extraction circumstances, ascertain the empirical correlation of total mass transfer, and produce the empirical correlation of Sherwood number, which can be used to characterize the role of variables in this mass transfer process. The ratio of impeller diameter to tank diameter and the Sherwood number, as well as Reynolds, Schmidt, Weber, dispersed phase holdup, were studied as dimensionless numbers. This study used liquid-liquid extraction process at the temperature of 50 °C, with chloroform-methanol 80% as a solvent with ratio of 2:1 v/v and use bio-oil to solvent ratio of 1:1 v/v. The extraction time has an interval of 30 min (20, 50, 80, 110 min), with varied impeller diameter (3.5 cm and 4 cm), vessels diameter (9 cm and 10 cm) and mixing speed (250 rpm, 300 rpm, 350 rpm). According to the findings, the greatest phenol yield was obtained at impeller diameter 3.5 cm, tank diameter 9 cm, and mixing speed 300 rpm, with a distribution coefficient of 0.848. The results demonstrate that the maximum overall mass transfer coefficient is 4.096 × 10-4 m/s and that the liquid diffusivity is 1.7965 × 10-9 m2/s. The equation model from this study could be applied to a liquid-liquid extraction process that is scaled up to extract more phenolic compounds from the the produced bio-oil. © 2023 Elsevier Ltd. All rights reserved.

关键词： Mass transfer

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Comparative performance of machine learning models for temperature prediction in vanadium redox flow batteries

Comparative performance of machine learning models for tempe...

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International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)

作者： Anxiu Zhang Filbert H. Juwono Hongfei Li Erick Purwanto W. K. Wong Linfeng Liu Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China

ISBN: (数字)9781837242863

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction.

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Discovering Melting Temperature Prediction Models of Inorganic Solids by Combining Supervised and Unsupervised Learning

arXiv

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arXiv 2024年

作者： Gharakhanyan, Vahe Wirth, Luke J. Torres, Jose A. Garrido Eisenberg, Ethan Wang, Ting Trinkle, Dallas R. Chatterjee, Snigdhansu Urban, Alexander Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Columbia Electrochemical Energy Center Columbia University New YorkNY10027 United States Department of Materials Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Chemical Engineering Columbia University New YorkNY10027 United States School of Statistics University of Minnesota MinneapolisMN55455 United States

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a data set of the melting points of 476 non-metallic crystalline binary compounds, using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： Melting point

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Impregnation process and kinetics studies of MgO nanocatalyst/montmorilonite-K10 for biodiesel production from Cerbera odollam oil in modified-microwave reactor

Kuwait Journal of Science

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Kuwait Journal of Science 2024年第2期51卷

作者： Sutapa, I Wayan Taba, Paulina Shiomori, Koichiro Taipabu, Muhammad Ikshan Kamari, Azlan Department of Chemistry Faculty Mathematics and Natural Science University of Pattimura Jalan Ir. Putuhena Poka Ambon 97233 Indonesia Department of Chemistry Faculty Mathematics and Natural Science University of Hasanuddin Jalan Perintis Kemerdekaan Makassar 90245 Indonesia Department of Applied Chemistry Faculty of Engineering University of Miyazaki 1-1 Gakuen Kibanadai Nishi Miyazaki 889-2192 Japan Department of Chemical Engineering National Cheng Kung University Tainan 70101 Taiwan Department of Chemical Engineering Pattimura University Ambon 97134 Indonesia Department of Chemistry Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Perak Tanjung Malim 35900 Malaysia

Impregnation of MgO nanocatalysts on montmorillonite-K10 (MTe) and kinetics studies on transesterification reactions for biodiesel production using Cerbera odollam oil have been conducted. The impregnation process of nanocatalyst into MTe was carried out by mixing the MgO nanocatalyst with MTe dispersed in an ethanol solution. The MgO-MTe product was characterized using X-ray diffraction (XRD), Fourier transform infrared (FT-IR), and scanning electron microscopy (SEM). Transesterification reaction process of Cerbera odollam oil using MgO-MTe catalyst carried out in batch modified-microwave reactor with various oil/methanol ratios, reaction temperatures, reaction time, and the percentage weight of catalyst. Furthermore, the reaction kinetics analysis is done using the pseudo-first-order reaction. The quality of biodiesel was determined using the American society for testing and materials (ASTM) method for biodiesel test. The results show that with the increasing percentage weight of catalysts, product increases with optimum conditions obtained on the weight of catalysts 7% (w/w). Meanwhile, the ratio of oil-to-methanol ration with the optimum yield was obtained at 1:12 at 78 °C with reaction time of 3 h. The reaction kinetics analysis was obtained including k = 2.50 x 10⁻⁴ min⁻¹, E_a = 73.43 kJ mol⁻¹ and the pre-exponential factor A = 19.22 min.⁻¹ © 2024 The Authors

关键词： Cerbera odollam Impregnation Nanocatalyst Reaction kinetics Transesterification

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Transductive and inductive outlier detection with robust autoencoders 24

Transductive and inductive outlier detection with robust aut...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Ofir Lindenbaum Yariv Aizenbud Yuval Kluger Faculty of Engineering Bar-Ilan University Ramat Gan Israel School of Mathematical Sciences Tel Aviv University Tel Aviv Israel Program in Applied Mathematics Yale University New Haven CT and Department of Pathology Yale University School of Medicine New Haven CT and Computational Biology & Bioinformatics Program Yale University New Haven CT

Accurate detection of outliers is crucial for the success of numerous data analysis tasks. In this context, we propose the Probabilistic Robust AutoEncoder (PRAE) that can simultaneously remove outliers during training (transductive) and learn a mapping that can be used to detect outliers in new data (inductive). We first present the Robust AutoEncoder (RAE) objective that excludes outliers while including a subset of samples (inliers) that can be effectively reconstructed using an AutoEncoder (AE). RAE minimizes the autoencoder's reconstruction error while incorporating as many samples as possible. This could be formulated via regularization by subtracting an ℓ0 norm, counting the number of selected samples from the reconstruction term. As this leads to an intractable combinatorial problem, we propose two probabilistic relaxations of RAE, which are differentiable and alleviate the need for a combinatorial search. We prove that the solution to the PRAE problem is equivalent to the solution of RAE. We then use synthetic data to demonstrate that PRAE can accurately remove outliers in various contamination levels. Finally, we show that using PRAE for outlier detection leads to state-of-the-art results for inductive and transductive outlier detection.

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