jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yaoguang Zhai | Richa Rashmi | Etienne Palos | Francesco Paesani
Source: The Journal of Chemical Physics (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Many-body interactions and deep neural network potentials for water We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field. Computer science ; Artificial intelligence ; Artificial neural network ; Representation (politics) ; Machine learning ; Transferability ; Field (mathematics) ; Deep learning ; Range (aeronautics) ; Water model ; Dilemma ; Data science ; Molecular dynamics ; Physics ; Aerospace engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Samaneh Mahmoudi Qashqay | Mohammad-Reza Zamani-Meymian | Ali Maleki | Zabiholah Zabihi Lahrami
Source: Journal of Power Sources (article, 2024-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation Supercapacitor ; Fabrication ; Electrode ; Computer science ; Capacitance ; Chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Fuel Cells and Related Materials
Authors: Wise Herowati | Wahyu Aji Eko Prabowo | Muhamad Akrom | T. Sutojo | Noor Ageng Setiyanto | Achmad Wahid Kurniawan | Novianto Nur Hidayat | Supriadi Rustad
Source: KnE Engineering (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time. Keywords: machine learning, corrosion inhibition, pyrimidine, QSPR model, predictive analysis Pyrimidine ; Algorithm ; Corrosion ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Materials science ; Metallurgy ; Stereochemistry Material Properties and Failure Mechanisms
Authors: Masoud Negahdary | Samuel Mabbott
Source: Coordination Chemistry Reviews (article, 2024-10-11)
Field: Materials Science — Biomaterials

Automated synthesis and processing of functional nanomaterials: Advances and perspectives Chemistry ; Nanotechnology ; Nanomaterials ; Biochemical engineering Supramolecular Self-Assembly in Materials ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Tao Du | Shanwu Li | Sudheer Ganisetti | Mathieu Bauchy | Yuanzheng Yue | Morten M. Smedskjær
Source: National Science Review (article, 2024-01-13)
Field: Chemistry — Inorganic Chemistry

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of <i>ab initio</i> molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)<sub>2</sub>, Im = C<sub>3</sub>H<sub>3</sub>N<sub>2</sub><sup>-</sup>) glasses, as well as for the formation of ZIF-zni (Zn(Im)<sub>2</sub>) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses. Zeolitic imidazolate framework ; Phase (matter) ; Materials science ; Chemical engineering ; Chemistry ; Physical chemistry ; Metal-organic framework ; Engineering ; Adsorption ; Organic chemistry Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Xiaotong Wang | Xiaomeng Sun | Zhehan Liu | Yue Zhao | Guangrun Wu | Yunpeng Wang | Qian Li | Chunjuan Yang | Tao Ban | Yu Liu | Jian‐An Huang | Yang Li
Source: Advanced Science (article, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface‐Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues Surface-enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate preparation that enhances Raman signal strength on a global scale. To systematically solve these problems, artificial intelligence techniques are applied to analyze the signals of pesticides based on 3D and dynamic SERS imaging. Utilizing perovskite/silver nanoparticles composites (CaTiO<sub>3</sub>/Ag@BONPs) as enhanced substrates, enabling it not only to cleanse pesticide residues from the surface to pulp of fruits and vegetables, but also to investigate the penetration dynamics of an array of pesticides (chlorpyrifos, thiabendazole, thiram, and acetamiprid). The findings challenge existing paradigms, unveiling a previously unnoticed weakening process during pesticide invasion and revealing the surprising permeability of non-systemic pesticides. Of particular note is easy to overlook that the combined application of pesticides can inadvertently intensify their invasive capacity due to pesticide interactions. The innovative study delves into the realm of pesticide penetration, propelling a paradigm shift in the understanding of food safety. Meanwhile, this strategy provides strong support for the cutting-edge application of SERS imaging technology and also brings valuable reference and enlightenment for researchers in related fields. Pesticide ; Raman scattering ; Nanotechnology ; Materials science ; Pesticide residue ; Nanoparticle ; Computer science ; Raman spectroscopy ; Chemistry ; Environmental science ; Optics ; Physics ; Agronomy Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: А. А. Коптелов | Adam J. Thompson | Stephen R. Hallett | Bassam El Said
Source: Materials & Design (article, 2024-02-27)
Field: Materials Science — Polymers and Plastics

A deep learning approach for predicting the architecture of 3D textile fabrics In this paper, a deep learning approach to 3D textile geometry simulations is presented. Two different network architectures with convolutional and recurrent properties are explored. The deep neural networks were trained to generate a fully compacted 3D textile unit cell based on the weave initial architecture. The AI training was conducted on a set of precomputed weaving case studies generated by digital element based weaving simulation software. The proposed strategy demonstrated effectiveness in estimation of 3D textile architectures. The designed system was able to operate within 10% error for stiffness properties prediction. The main benefit of the proposed approach over conventional modelling is its computational efficiency. Rapid weaving simulations provide an opportunity to explore the effects of different yarn architectures, matrix materials, and manufacturing techniques on the mechanical properties of woven composites, leading to a better understanding of their behaviour and their potential for use in new applications. Weaving ; Textile ; Convolutional neural network ; Yarn ; Deep learning ; Computer science ; Architecture ; Set (abstract data type) ; Artificial intelligence ; Software ; Materials science ; Mechanical engineering ; Engineering ; Composite material Textile materials and evaluations ; Mechanical Behavior of Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Khalid Zobaid Adnan | Mahesh R. Neupane | Tianli Feng
Source: International Journal of Heat and Mass Transfer (article, 2024-09-21)
Field: Materials Science — Materials Chemistry

Thermal boundary conductance of metal–diamond interfaces predicted by machine learning interatomic potentials Materials science ; Diamond ; Conductance ; Thermal conductivity ; Interatomic potential ; Thermal ; Metal ; Boundary (topology) ; Condensed matter physics ; Thermodynamics ; Composite material ; Molecular dynamics ; Metallurgy ; Computational chemistry ; Physics ; Chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Saad Alatefi | Okorie Ekwe Agwu | Ahmad Alkouh
Source: Results in Engineering (article, 2024-11-05)
Field: Chemistry — Analytical Chemistry

Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen • A Bayesian regularized neural network model for predicting CO 2 diffusivity in heavy crude oil and bitumen systems. • Developed model is explicitly expressed unlike most existing Machine Learning models. • The model has been physically validated using trend analysis. • The developed model outperformed existing AI models based on various statistical metrics. • Inference latency of the developed model has been established. Optimizing CO 2 injection for enhanced oil recovery (EOR) requires a precise estimation of the CO 2 -diffusivity coefficient in porous media. This study developed a predictive model for the molecular diffusivity coefficient of CO 2 in bitumen and heavy crude oils using a Bayesian regularized artificial neural network. The unique contributions of the developed model compared to existing models include: First, a simple and accurate mathematical correlation between inputs and CO 2 -diffusivity has been presented, making it easy to use particularly for individuals with limited understanding of machine learning. Secondly, the time it takes the model to make a prediction (its inference latency) has been established and the model has been physically validated using trend analysis. Fourthly, independent datasets were used to test for the model generalizability. The model was evaluated using 260 data points from the literature, with 70% for training and 30% for testing. Key performance metrics were calculated, including root mean square error (RMSE = 0.03), and coefficient of determination (R 2 = 0.996). Furthermore, an outlier detection analysis using the statistical leverage approach demonstrated that the data used was of high quality. A relevancy factor analysis revealed that pressure had the greatest impact on CO 2 diffusivity, followed by temperature, while CO 2 mass fraction had the least impact. The developed model operates within a temperature range of 295K – 363K and a pressure range of 1MPa – 8MPa. Overall, the outcomes of this study contribute to the efficient prediction of CO 2 diffusivity in heavy crude oil and bitumen. Asphalt ; Diffusion ; Crude oil ; Environmental science ; Petroleum engineering ; Computer science ; Biological system ; Artificial intelligence ; Materials science ; Thermodynamics ; Engineering ; Biology ; Physics ; Composite material Petroleum Processing and Analysis ; Computational Drug Discovery Methods ; Hydrocarbon exploration and reservoir analysis
Authors: Chenxi Zhao | Xueying Lu | Huanyu Tu | Yulong Yang | Siyu Wang | Aihui Chen | Haibin Zhang
Source: Journal of Energy Storage (article, 2024-07-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on specific capacitance prediction of biomass carbon-based supercapacitors based on machine learning Supercapacitor ; Capacitance ; Biomass (ecology) ; Carbon fibers ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Composite material ; Biology ; Electrode ; Composite number ; Ecology Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: Jeong‐In Hwang | Kyeong‐Ok Choi | Sungmin Jeong | Suyong Lee
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R<sup>2</sup> > 0.992 and RMSE < 2.75). Hyperspectral imaging ; Linear discriminant analysis ; Vegetable oil ; Edible oil ; Artificial intelligence ; Random forest ; Mathematics ; Sunflower oil ; Pattern recognition (psychology) ; Fatty acid ; Food science ; Chemistry ; Computer science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Hongrui Chen | Xin Liu | Xingchen Liu | Paul Witherell
Source: Additive manufacturing (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

MeltpoolGAN: Melt pool prediction from path-level thermal history Materials science ; Path (computing) ; Thermal ; Thermodynamics ; Computer science Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Radiative Heat Transfer Studies
Authors: Xiaowen Li | Jian Qiu | Heping Cui | Xianping Chen | Jiabing Yu | Kai Zheng
Source: ACS Applied Materials & Interfaces (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients Efficient and rapid screening of target materials in a vast material space remains a significant challenge in the field of materials science. In this study, first-principles calculations and machine learning algorithms are performed to search for high out-of-plane piezoelectric stress coefficient materials in the MXene functional database among the 1757 groups of noncentrosymmetric MXenes with nonzero band gaps, which meet the criteria for piezoelectric properties. For the monatomic MXene testing set, the random forest regression (RFR), gradient boosting regression (GBR), support vector regression (SVR), and multilayer perceptron regression (MLPR) exhibit <i>R</i><sup>2</sup> values of 0.80, 0.80, 0.89, and 0.87, respectively. Expanding our analysis to the entire MXene data set, the best active learning cycle finds more than 140 and 22 MXenes with out-of-plane piezoelectric stress coefficients (<i>e</i><sub>31</sub>) exceeding 3 × 10<sup>-10</sup> and 5 × 10<sup>-10</sup> C/m, respectively. Moreover, thermodynamic stabilities were confirmed in 22 MXenes with giant piezoelectric stress coefficients and 9 MXenes with both large in-plane (<i>d</i><sub>11</sub> > 15 pm/V) and out-of-plane (<i>d</i><sub>31</sub> > 2 pm/V) piezoelectric strain coefficients. These findings highlight the remarkable capabilities of machine learning and its optimization algorithms in accelerating the discovery of novel piezoelectric materials, and MXene materials emerge as highly promising candidates for piezoelectric materials. MXenes ; Materials science ; Piezoelectricity ; Nanotechnology ; Machine learning ; Composite material ; Computer science MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Sakib Matin | Alice E. A. Allen | Justin S. Smith | Nicholas Lubbers | Ryan B. Jadrich | Richard A. Messerly | Benjamin Nebgen | Ying Wai Li | Sergei Tretiak | Kipton Barros
Source: Journal of Chemical Theory and Computation (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations. Computer science ; Inversion (geology) ; Solver ; Artificial intelligence ; Experimental data ; Machine learning ; Training set ; Restricted Boltzmann machine ; Molecular dynamics ; Algorithm ; Artificial neural network ; Physics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Huibin Qiu | Yongpeng Xia | Cuili Xiang | Fen Xu | Lixian Sun | Yongjin Zou
Source: International Journal of Hydrogen Energy (article, 2024-07-11)
Field: Chemistry — Inorganic Chemistry

Prediction of hydrogen storage in metal-organic frameworks using CatBoost-based approach Hydrogen storage ; Metal-organic framework ; Computer science ; Hydrogen ; Process engineering ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography