jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mona Akbari | Maryam Akbari
Source: SHILAP Revista de lepidopterología (article, 2024-01-01)
Field: Materials Science — Biomaterials

Modeling of Preparation of Chitosan/Tripolyphosphate Nanoparticles Using Machine-Learning Techniques In recent years, there has been extensive research on the synthesis of Chitosan-Tripolyphosphate (CS/TPP) nanoparticles. However, the influence of different parameters and their interactions on nanoparticles is not well known yet. The purpose of the present study is to use machine-learning techniques, Random Forests (RF), and Artificial Neural Networks (ANN), to estimate the size and zeta potential of nanoparticles based on chitosan and tripolyphosphate concentrations, chitosan molecular weight, degree of deacetylation, pH, temperature, stirring rate, and their interactions. Machine-learning algorithms successfully estimated the size and zeta potential of nanoparticles spanning a size range from 50 to 1000 nm. The random forest algorithm had better overall R-squared accuracy of 0.95211 and 0.93978 in comparison to ANN model with R-squared accuracy of 0.94744 and 0.93643 for particle size and zeta potential, respectively. Results depicted that temperature and stirring rate did not have significant effects on the size and zeta potential. On the other hand, CS concentration and pH were the most important parameters on the size and zeta potential, respectively. Furthermore, the results indicated that the primary cause for the unexplained variation in prior studies stemmed from the interactions among parameters. Moreover, a correlation exists between size and zeta potential, attributed to the interaction of attractive and repulsive electrostatic charges, ionic interactions, and the quantity of CS molecules. Zeta potential ; Particle size ; Nanoparticle ; Chitosan ; Ionic bonding ; Ionic strength Nanocomposite Films for Food Packaging ; Ionic liquids properties and applications ; Advanced Battery Materials and Technologies
Authors: Polytechnique Montréal | Seyed Mohsen Hosseini-Ardali | Ahmed Ragab | Mouloud Amazouz | CanmetENERGY | Soumaya Yacout
Source:  (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

AI-Assisted Acceleration of Catalytic Materials Design/Discovery: Towards achieving net-zero emissions The design of innovative materials holds high potential for societal and technological advancement. In this context, the discovery of novel catalysts emerges as a prioritized need within many industrial applications to expedite the adoption of renewable energy sources, thereby contributing to mitigating global warming. However, exploring the whole chemical space for potential catalysts poses significant computational challenges. Furthermore, catalyst design involves a highly interdisciplinary technology that requires simultaneous control and comprehension of numerous interacting and conflicting design variables. The key limitation of traditional trial-and-error methods stems from their inefficiency and time-consuming nature, whereas computational quantum mechanical modeling methods incur substantial computational costs. A domain of growing importance pertains to the adoption of Artificial Intelligence (AI) and its related generative Machine Learning (ML) technologies, which represent a groundbreaking paradigm in materials science research. This paradigm aims to optimize the exploration of material relevance space, enhance knowledge about materials and their properties and accelerate the material discovery process through automated parallel/iterative procedures. This paper discusses the key challenges in AI-based material design/discovery and offers recommendations for future research directions. Acceleration ; Zero emission ; Zero (linguistics) ; Computer science ; Net (polyhedron) ; Catalysis ; Automotive engineering ; Engineering ; Chemistry ; Physics ; Waste management ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Source: Nature Computational Science (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Boosting graph neural networks with virtual nodes to predict phonon properties Boosting (machine learning) ; Computer science ; Artificial neural network ; Phonon ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Emily Lın | Yang Zhong | Gang Chen | Sili Deng
Source: Research Square (preprint, 2024-10-17)
Field: Materials Science — Materials Chemistry

Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks Metal-organic framework ; Adsorption ; Thermodynamics ; Chemistry ; Materials science ; Physical chemistry ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

RandomNets Improve Neural Network Regression Performance via Implicit Ensembling Artificial feed-forward neural networks have long been recognized as powerful machine learning models and are widely used in QSAR and QSPR modeling of molecular properties. Inspired by Random Forest models and the robust techniques of sample and feature bagging, the RandomNets model was developed as an efficient, vectorized solution for ensemble creation, training, and inference. The model adds an extra dimension to the tensors passing through the neural network, combined with input feature masking and optional subsampling of the dataset during training. This vectorized approach improves efficiency and simplifies training and inference of the implicit ensemble. Training a 25-member implicit ensemble requires only twice the time of a comparable baseline network but significantly improves prediction performance, as measured by R² and MSE on test sets from 133 bioactivity datasets, with an average performance increase of around 25%. Compared to the conceptually similar input masking technique using dropout, the implicit ensemble demonstrates reduced sensitivity to hyperparameter choices, similar or improved performance, and a fourfold reduction in training time. Additionally, the implicit ensemble provides the standard deviation of individual predictions, which can help identify uncertain predictions. Hyperparameter ; Computer science ; Artificial neural network ; Machine learning ; Inference ; Artificial intelligence ; Ensemble forecasting ; Random forest ; Feature (linguistics) ; Ensemble learning ; Regression ; Dropout (neural networks) ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2024-10-15)
Field: Materials Science — Materials Chemistry

Powerful and Reliable Prediction using Latent Variables of Experimentally Unobservable Reactions in Organic Synthesis In this study, a novel machine learning algorithm was designed to assist in the development of organic reactions. This algorithm addresses the complexities inherent in batch- type organic reactions, including the necessity for numerous experiments and the effects of intricate characteristics of reaction pathways. By integrating molecular relationships and actual yields from observable reactions, the algorithm is used to estimate untested yields via extrapolation. An approach based on Bayesian optimization and dual annealing optimization is employed to compute expected values and evaluate plausibility. The algorithm’s dual-loop 2 structure, incorporating latent variables and experimental values, maximizes the coefficient of determination. Physicochemical aspects of the algorithm are validated using natural bond orbital charges, and its utility in synthesizing perfluoroiodinated naphthalenes is demonstrated. The algorithm exhibits potential for application in predicting experimentally unobservable reactions, thereby advancing the field of synthetic organic chemistry. Unobservable ; Latent variable ; Econometrics ; Artificial intelligence ; Computer science ; Mathematics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Various Chemistry Research Topics
Authors: Ming Lei | Bo Li | Hongjun Liu | De‐en Jiang
Source: Angewandte Chemie (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Frontispiz: Dynamic Monkey Bar Mechanism of Superionic Li‐ion Transport in LiTaCl<sub>6</sub> Lithium-Ion Batteries. In their Communication (e202315628), De-en Jiang et al. reveal through machine-learning force field molecular dynamics simulations the superionic Li+ transport in glassy LiTaCl6 via a dynamic “monkey bar” mechanism. Bar (unit) ; Lithium (medication) ; Ion ; Chemical physics ; Mechanism (biology) ; Molecular dynamics ; Fast ion conductor ; Materials science ; Ion transporter ; Chemistry ; Nanotechnology ; Physics ; Computational chemistry ; Physical chemistry ; Psychology ; Electrode Machine Learning in Materials Science ; Conducting polymers and applications ; Graphene research and applications
Authors: Apurva Mehta
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-02-19)
Field: Materials Science — General Materials Science

Metallic glass coating material A metallic glass coating material is composed of an alloy of Fe, B, and one of the metals Nb, Mo, Zr, or W. The ratios of Fe, B, and the metal are predetermined using machine learning predictions and high-throughput experiments. In one example, the material is an alloy of Fe, Nb, Mo and B, of the form Fe<sub>x</sub>(Nb, Mo)<sub>y</sub>B<sub>z</sub>, where x is in the range 18-28, y is in the range 35-45, and z is in the range 32-42. In another example, the material may be the alloy Fe<sub>23</sub>(Nb, Mo)<sub>40</sub>B<sub>37</sub>. The alloy may be doped with Zr and/or W, where the Zr and/or W comprises at most 10% of the alloy. Coating ; Materials science ; Composite material ; Metallurgy Material Properties and Applications
Authors: Gagan Kumar Chappa | Milena Petkovic | Natasha Dropka
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Feasibility Study of Vgf Β-Ga2o3 Growth Under Traveling Magnetic Fields Computer science Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Julian Evan Chrisnanto
Source: Jurnal Ilmu dan Inovasi Fisika (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

CBDR-CNN Approach for Rapid Identification of XRD Data: A Preliminary Study In this study, we present a novel approach combining Content-Based Data Retrieval (CBDR) and 1-dimensional Convolutional Neural Networks (1D-CNN) for crystal structure analysis of powder materials by using X-Ray Diffraction (XRD) data. The introduction sets the background by highlighting the importance of X-ray diffraction analysis and the limitations of conventional approaches in dealing with complex crystal structures. To overcome this challenge, researchers have explored artificial intelligence techniques, specifically CNN for crystal structure classification based on XRD image graph represented as 2-theta versus intensity values. The aims of this study are: implementing CBDR method on CNN model for crystal structure classification; simulating CBDR-CNN model for crystal structure classification; verifying CBDR-CNN model in crystal structure classification. Each class for CNN model such as crystal system, class material, sub-class material, and space-group achieved accuracies 99.86%, 99.99%, 99.95%, and 99.82% respectively. The results and discussion section presents the results of the CBDR-CNN model. The CBDR model effectively retrieved the most similar XRD spectrum data from the dataset based on the query properties, including Miller indices and peak position. The model effectively reduced the scope potential candidate materials, sub-materials, and space-groups. The 1D-CNN model showed high accuracy in predicting crystal properties such as material, sub-material, space-group, and crystal system. In conclusion, the CBDR-CNN approach potential revolutionizes XRD data analysis and crystal system prediction, which promotes progress in computer-aided materials study. Artificial intelligence ; Crystal (programming language) ; Crystal structure ; Convolutional neural network ; Computer science ; Machine learning ; Algorithm ; Crystallography ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Cultural Heritage Materials Analysis
Authors: Sherif Abdulkader Tawfik | Sunil Gupta | Svetha Venkatesh
Source: ChemRxiv (preprint, 2024-12-23)
Field: Materials Science — Materials Chemistry

Predicting the electron density of charged systems using machine learning The prediction of the electron density in molecules and crystals is a key pillar in the first principles computation of their properties. Using machine learning to predict the electron density by using the atomic structure alone can save the computational cost of performing first principles computations. While various machine learning approaches have been introduced for predicting the electron density, none of them predicts the electron density for charged systems. This work extends a recent machine learning charge density model, DeepDFT, by including the charge of the structure as an input parameter into the model. We establish an input charge representation approach that successfully predicts the charged electron densities for several test cases, including charged defective perovskites, LiCoO$_2$ supercells with multiple Li vacancies, diamond-based defects, metal organic frameworks and molecular crystals. Electron ; Computer science ; Electron density ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Nuclear physics Machine Learning in Materials Science
Authors: Andrew Mitchell | Grant M. Rotskoff
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Chemistry — Physical and Theoretical Chemistry

Committor guided estimates of molecular transition rates The probability that a configuration of a physical system reacts, or transitions from one metastable state to another, is quantified by the committor function. This function contains richly detailed mechanistic information about transition pathways, but a full parameterization of the committor requires building representing a high-dimensional function, a generically challenging task. Recent efforts to leverage neural networks as a means to solve high-dimensional partial differential equations, often called "physics-informed" machine learning, have brought the committor into computational reach. Here, we build on the semigroup approach to learning the committor and assess its utility for predicting dynamical quantities such as transition rates. We show that a careful reframing of the objective function and improved adaptive sampling strategies provide highly accurate representations of the committor. Furthermore, by directly applying the Hill relation, we show that these committors provide accurate transition rates for molecular system. Transition (genetics) ; Econometrics ; Statistical physics ; Mathematics ; Physics ; Biology ; Genetics Photochemistry and Electron Transfer Studies ; Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Pan-dong Lin | Jun-feng Nie | Shu-gang Cui | Wen-dong Cui | Lei He | Gui‐yong Xiao | Yu‐peng Lu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Atomic-Scale Three-Dimensional Irradiation-Induced Defect Kinetics Models for Bcc Fe-Based Alloys Kinetics ; Irradiation ; Atomic units ; Materials science ; Scale (ratio) ; Crystallography ; Metallurgy ; Chemistry ; Physics ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Jiaying Chen | Linxi Hou | Jing Nan | Bangqing Ni | Wei Dai | Xin Ge
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Prediction of Critical Micelle Concentration (Cmc) of Surfactants Based on Structural Differentiation Using Machine Learning Micelle ; Critical micelle concentration ; Thermodynamics of micellization ; Computer science ; Chemistry ; Materials science ; Biological system ; Chemical engineering ; Engineering ; Organic chemistry ; Biology Surfactants and Colloidal Systems ; Water Quality Monitoring and Analysis ; Spectroscopy and Chemometric Analyses
Authors: Supriyo Ghosh | Sheng Zhang | Cheng Chen | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large scale dynamical simulations of the JT model also shed light on the orbital ordering dynamics in colossal magnetoresistance manganites. The JT effect in these materials describes the distortion of local oxygen octahedra driven by a coupling to the orbital degrees of freedom of $e_g$ electrons. An effective electron-mediated interaction between the local JT modes leads to a structural transition and the emergence of long-range orbital order at low temperatures. Assuming the principle of locality, a deep-learning neural-network model is developed to accurately and efficiently predict the electron-induced forces that drive the dynamical evolution of JT phonons. A group-theoretical method is utilized to develop a descriptor that incorporates the combined orbital and lattice symmetry into the ML model. Large-scale Langevin dynamics simulations, enabled by the ML force-field models, are performed to investigate the coarsening dynamics of the composite JT distortion and orbital order after a thermal quench. The late-stage coarsening of orbital domains exhibits pronounced freezing behaviors which are likely related to the unusual morphology of the domain structures. Our work highlights a promising avenue for multi-scale dynamical modeling of correlated electron systems. Jahn–Teller effect ; Scale (ratio) ; Statistical physics ; Kinetics ; Computer science ; Physics ; Classical mechanics ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Chemical Physics Studies
Authors: Siqi Chen | Zhiqiang Wang | Xiaotie Deng | Yili Shen | Chengwei Ju | Jun Yi | Lin Xiong | Ling Guo | Dieaa Alhmoud | Hui Guan | Lin Zhou
Source: arXiv (Cornell University) (preprint, 2024-11-03)
Field: Materials Science — Materials Chemistry

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based graph neural networks (FBGNN) into the fragment-based many-body expansion (MBE) theory, referred to as FBGNN-MBE, and demonstrated its capacity to reproduce full-dimensional potential energy surfaces (FD-PES) for hierarchic chemical systems with manageable accuracy, complexity, and interpretability. In particular, we divided the entire system into basic building blocks (fragments), evaluated their single-fragment energies using a first-principles QM model and attacked many-fragment interactions using the structure-property relationships trained by FBGNNs. Our development of FBGNN-MBE demonstrated the potential of a new framework integrating deep learning models into fragment-based QM methods, and marked a significant step towards computationally aided design of large functional materials. Graph ; Computer science ; Theoretical computer science Machine Learning in Materials Science
Authors: Jiale Han | Chunhua Ying | Yue Cao | Wen Li | Yuan Feng | Masood Mortazavi | Pingfan Wu | Liang Peng | Jiechen Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Harnessing Machine Learning for High-Throughput Screening of High Thermal Conductivity Polyimides: A Multiscale Feature Engineering Approach Throughput ; Feature (linguistics) ; Thermal conductivity ; High-throughput screening ; Feature engineering ; Artificial intelligence ; Computer science ; Materials science ; Conductivity ; Machine learning ; Nanotechnology ; Process engineering ; Engineering ; Deep learning ; Composite material ; Physics ; Bioinformatics ; Telecommunications ; Biology Synthesis and properties of polymers ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: YU.R. POLYGALOV | A. F. Dresvyânnikov
Source: Herald Of Technological University (article, 2024-01-01)
Field: Chemistry — Electrochemistry

ROLE OF ELECTRODE MATERIALS IN ELECTROCHEMICAL WATER PURIFICATION Electrochemical processes are a versatile way to remove a wide range of water contaminants, especially those that are difficult to achieve by conventional methods. They do not require the use of chemicals and utilise renewable energy more efficiently. This review describes the fundamental principles of some electrochemical water treatment processes, shows the crucial role of electrode materials in the interfacial processes that control the transport and transformation of pollutants, and reviews the state of knowledge on the development and application of new electrode materials. In addition, the advantages and disadvantages of existing and new electrode materials are analysed, and strategies for their development are discussed. Ways to improve approaches to the development of electrodes for electrochemical treatment of natural and waste water are outlined. Advances in electrocatalysis, electrode fabrication techniques and process optimisation have significantly improved the performance of water treatment. In particular, new catalysts (such as bi-, tri-metals and oxides) and electrode structures (porous structure, fluidised bed, nozzles and electrode stacks) designed for specific processes and new modes of operation are the basis for improving contaminant mass transfer and transformation kinetics through preferred reaction pathways. Such studies provide valuable data for future material selection and design, especially for the implementation of artificial intelligence (AI)-based approaches such as BigData, machine learning, which require large amounts of data. Electrode ; Electrochemistry ; Materials science ; Chemistry Electrochemical Analysis and Applications