jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaofei Du | Haiyang Du | Yajun Zhao
Source: Polymers for Advanced Technologies (article, 2023-03-17)
Field: Materials Science — Polymers and Plastics

A theoretical model for two‐way bending behavior of bilayer shape memory polymer plate Abstract This study proposes a simple theoretical model based on the principles of phase transition and strain energy balance theories (during the thermo‐mechanical cycle process) to predict the two‐way bending behavior of a bilayer plate composed of two types of shape memory polymers (SMPs) with different glass transition temperatures under axial strains from 5% to 20%. The two‐way bending behavior observed from the theoretical results was verified via comparison with the finite‐element method (FEM) simulation based on the Generalized Maxwell model, wherein good agreement was demonstrated. The maximum errors for the critical bending angle and deflection were 6.82% and 5.36%, respectively. In addition, the two‐way bending behavior of a bilayer plate, including SMP and elastic polymer layers, was also predicted by the theoretical model, where good agreement was observed. The maximum errors for the critical bending angle and deflection were 15.20% and 12.82%, respectively. The theoretical model can predict the thermo‐mechanical behavior of smart actuators and help design the geometry configuration based on bilayer SMP plates in the future, such as smart hands or grippers. Materials science ; Bilayer ; Deflection (physics) ; Finite element method ; Bending ; Shape-memory polymer ; Phase transition ; Actuator ; Bending stiffness ; Composite material ; Polymer ; Optics ; Thermodynamics ; Computer science ; Physics Polymer composites and self-healing ; Silicone and Siloxane Chemistry ; Marine Biology and Environmental Chemistry
Authors: Jianxiang Yang | Yiran Fan | Anle Mu | Jianbin Xiong
Source: Fractal and Fractional (article, 2023-12-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic Analysis and Suppression Strategy Research on a Novel Fractional-Order Ferroresonance System Ferroresonance is characterized by overvoltage and irregular operation in power systems, which can greatly endanger system equipment. Mechanism analysis of the ferroresonance phenomenon depends mainly on model accuracy. Due to the fractional-order characteristics of capacitance and inductance, fractional-order models are more universal and accurate than integer-order models. A typical 110 kV ferroresonance model is first established. The influence of the excitation amplitude on the dynamic behavior is analyzed. The fractional-order ferroresonance model is then introduced, and the effects of the fractional order and flux-chain order on the system’s motion state are studied via bifurcation diagrams and phase portraits. In order to suppress the nonlinear dynamic behavior of fractional-order ferroresonance systems, a novel fractional-order fast terminal sliding mode control method based on finite-time theory and the frequency distributed model is proposed. A new fractional-order sliding mode surface and control law using a saturation function are developed. A robust fractional-order sliding mode controller could achieve finite-time stabilization and tracking despite model uncertainties and external disturbances. Compared with conventional sliding mode methods, the simulation results highlight the effectiveness and superiority. The research provides a theoretical basis for ferroresonant analysis and suppression in large-scale interconnected power grids. Ferroresonance in electricity networks ; Control theory (sociology) ; Phase portrait ; Overvoltage ; Nonlinear system ; Electric power system ; Computer science ; Engineering ; Bifurcation ; Power (physics) ; Physics ; Voltage Magnetic Properties and Applications ; Magnetic Bearings and Levitation Dynamics ; Vibration and Dynamic Analysis
Authors: Lei Zhang | Junyong Zhu | Sheng Wang | Jie Hou | Dong Si | Renzhi Cao
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-07-28)
Field: Materials Science — Materials Chemistry

AnglesRefine: refinement of 3D protein structures using Transformer based on torsion angles ABSTRACT Motivation The goal of protein structure refinement is to enhance the precision of predicted protein models, particularly at the residue level of the local structure. Existing refinement approaches primarily rely on physics, whereas molecular simulation methods are resource-intensive and time-consuming. In this study, we employ deep learning methods to extract structural constraints from protein structure residues to assist in protein structure refinement. We introduce a novel method, AnglesRefine, which focuses on a protein’s secondary structure and employs a transformer model to refine various protein structure angles (psi, phi, omega, CA_C_N_angle, C_N_CA_angle, N_CA_C_angle), ultimately generating a superior protein model based on the refined angles. Results We evaluate our approach against other cutting-edge protein structure refinement methods using the CASP11-14 and CASP15 datasets. Experimental outcomes indicate that our method generally surpasses other techniques on the CASP11-14 test dataset, while performing comparably or marginally better on the CASP15 test dataset. Our method consistently demonstrates the least likelihood of model quality degradation, e.g., the degradation percentage of our method is less than 10%, while other methods are about 50%. Furthermore, as our approach eliminates the need for conformational search and sampling, it significantly reduces computational time compared to existing protein structure refinement methods. Availability https://github.com/Cao-Labs/AnglesRefine.git Contact caora@plu.edu CCS CONCEPTS *Computing methodologies → Machine learning . ACM Reference Format Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, and Renzhi Cao. 2023. AnglesRefine: refinement of 3D protein structures using Transformer based on torsion angles. In Proceedings of 22nd International Workshop on Data Mining in Bioinformatics (BIOKDD 2023) (BIOKDD ‘2023) . ACM, New York, NY, USA, 10 pages. https://doi.org/XXXXXXX.XXXXXXX Computer science ; Protein structure ; Protein structure prediction ; Protein Data Bank ; Transformer ; Software ; Algorithm ; Computational science ; Artificial intelligence ; Chemistry ; Voltage ; Physics ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Li Wang | Junbai Chen | Hongjie Li | Lei Jin | Kun Zhao
Source: IEEJ Transactions on Electrical and Electronic Engineering (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Partial Discharge Characteristics and Mechanism of Medium Voltage Power Cable Defects under Different Voltage Stresses Off‐line partial discharge (PD) diagnosis, which requires an additional power supply has been accepted as an essential step in ensuring the integrity of power cable systems during commissioning testing. Many types of energizing voltage sources (e.g., alternating current resonance, damped alternating current, very low frequency [VLF] sine, and VLF‐cosine rectangular) have been developed, which are used in practice. However, choosing the optimal energizing voltage waveforms for PD testing is still controversial because their capability to motivate PD activities is still unclear. Accordingly, this paper compares the PD ignition capacity of the aforementioned power sources on six well‐designed PD defects in real cable joints. The influence of test voltage frequencies and waveforms are verified. The results show that the PD inception voltage and PD magnitudes in the cable joint defects do not change significantly as a function of the sinusoidal voltage frequency and the VLF‐cosine rectangular voltage exhibits excellent PD ignition capability with a low PD inception voltage and large PD magnitude. Moreover, the dynamic behavior of space charge in a PD defect caused by a gap is analyzed in order to explain the obtained results. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. Partial discharge ; Voltage ; Waveform ; Electrical engineering ; Power (physics) ; Power cable ; Alternating current ; Engineering ; Materials science ; Physics ; Composite material High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Electrostatic Discharge in Electronics
Authors: Yongsen Han | Changyou Suo | Cheng Peng | Zhonghua Li
Source: High Voltage (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

New measurement method of displacement susceptibility based on time‐domain current and potential combined spectra Abstract Displacement susceptibility is a very important parameter to characterise the displacement polarisation of dielectrics. Two methods based on the time‐domain current and potential combined spectra are proposed to measure the displacement susceptibility. One is based on the time‐domain polarisation current and decay potential combined spectra, and the other is based on the time‐domain depolarisation current and recovery potential combined spectra. The principle of the proposed methods are verified by simulation. In addition, a measurement system for the two proposed methods is built and is used to measure the displacement susceptibility of transparent organic glass and opaque nonlinear field grading materials. The experimental results are compared with those of the present optical method and the frequency‐domain method. Simulation and experimental results show that the proposed methods for the measurement of displacement susceptibility are feasible and practical, which lays a foundation for the research on the polarisation characterisation of dielectrics. Displacement current ; Displacement (psychology) ; Time domain ; Spectral line ; Dielectric ; Nonlinear system ; Current (fluid) ; Opacity ; Computational physics ; Measure (data warehouse) ; Optics ; Materials science ; Physics ; Computer science ; Optoelectronics ; Data mining ; Thermodynamics ; Quantum mechanics High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors
Authors: Bowen Deng
Source:  (preprint, 2023-06-27)
Field: Materials Science — Materials Chemistry

Catalysis distillation neural network for the few-shot open catalyst challenge &lt;p&gt;The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a “large-scale language model” in catalysis persist, and the recent success of ChatGPT's(Chat Generative Pre-trained Transformer) few-shot methods surpassing BERT (Bidirectional Encoder Representation from Transformers) underscores the importance of addressing limited data, expensive computations, time constraints and structure-activity relationship in research. Hence, the development of few-shot techniques for catalysis is critical and essential, regardless of present and future requirements. This paper introduces the Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning technology for predicting catalytic reactions on catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN).Our results demonstrate that CDGNN effectively learns embeddings from catalytic structures, enabling the capture of structure-adsorption relationships. This accomplishment has resulted in the utmost advanced and efficient determination of the reaction pathway for hydrogen peroxide, surpassing the current graph neural network approach by 16.1%. Consequently, CDGNN presents a promising approach for few-shot learning in catalysis. &lt;/p&gt; Artificial neural network ; Computer science ; Catalysis ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Alexander S. Novikov
Source: Compounds (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science. Perspective (graphical) ; Computer science ; Relation (database) ; Natural (archaeology) ; Natural science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Materials science ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ankur K. Gupta | Miko M. Stulajter | Yusuf Shaidu | Jeffrey B. Neaton | Wibe A. de Jong
Source: ChemRxiv (preprint, 2023-12-01)
Field: Materials Science — Materials Chemistry

Equivariant Neural Networks Utilizing Molecular Clusters for Accurate Molecular Crystal Binding Energy Predictions Equivariant neural networks have emerged as prominent models in advancing the construction of interatomic potentials due to their remarkable data efficiency and generalization capabilities for out-of-distribution data. Here, we expand the utility of these networks to the prediction of crystal structures consisting of organic molecules. Traditional methods for computing crystal structure properties, such as plane-wave quantum chemical methods based on density functional theory (DFT), are prohibitively resource-intensive, often necessitating compromises in accuracy and the choice of exchange-correlation functional. We present an approach that leverages the efficiency, and transferability of equivariant neural networks, specifically Allegro, to predict molecular crystal structure energies at a reduced computational cost. Our neural network is trained on molecular clusters using a highly accurate Gaussian-type orbital (GTO)-based method as the target level of theory, eliminating the need for costly periodic DFT calculations, while providing access to all families of exchange-corelation functionals and post-Hartree Fock methods. The trained model exhibits remarkable accuracy in predicting binding energies, aligning closely with those computed by plane-wave based DFT methods, thus representing significant cost reductions. Furthermore, the Allegro network was seamlessly integrated with the USPEX framework, accelerating the discovery of low-energy crystal structures during crystal structure prediction. Artificial neural network ; Equivariant map ; Density functional theory ; Computer science ; Gaussian ; Crystal structure prediction ; Hybrid functional ; Crystal (programming language) ; Statistical physics ; Generalization ; Binding energy ; Topology (electrical circuits) ; Algorithm ; Physics ; Artificial intelligence ; Theoretical computer science ; Molecule ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies
Authors: Su‐min Song | Ha Eun Kim | Hyun Woo Kim | Won‐jin Chung
Source: Helvetica Chimica Acta (article, 2023-10-28)
Field: Materials Science — Materials Chemistry

Prediction of Reaction Performance by Machine Learning Using Streamlined Features: NMR Chemical Shifts as Familiar Descriptors Abstract Machine learning (ML) has quickly emerged in synthetic organic chemistry to predict reaction outcomes such as yields and stereoselectivities. Notably, recent applications of the ML approach showed powerful performance in solving various chemical problems. However, the requirement of numerous descriptors and large datasets hampers the general use by non‐specialists. In this study, simple ML models were developed by utilizing easily available 13 C‐NMR chemical shifts of the substrates as familiar descriptors to predict the site‐selectivity of geminal chlorofluorination of unsymmetrical 1,2‐dicarbonyl compounds. We identified that the feed‐forward neural network (FNN) model provides higher accuracy compared to other algorithms. Then, better prediction performance was acquired through streamlined models using minimal, only empirically relevant descriptors. Geminal ; Chemistry ; Chemical shift ; Artificial neural network ; Artificial intelligence ; Machine learning ; Simple (philosophy) ; Selectivity ; Biological system ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Chenghan Sun | Rajat Goel | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2023-11-20)
Field: Materials Science — Materials Chemistry

Developing Cheap but Useful Machine Learning based Models for Investigating High-Entropy Alloy Catalysts This work aims to address the challenge of developing interpretable ML-based models when access to large scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor based approaches, machine learning based force fields and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N and NHx (x = 1, 2 and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including, (1) using a sequential optimization protocol, (2) developing a new-geometry based descriptor, and (3) re-purposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cheap DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community. Workflow ; Density functional theory ; Computer science ; Adsorption ; Entropy (arrow of time) ; Alloy ; Work (physics) ; Catalysis ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Materials science ; Chemistry ; Computational chemistry ; Thermodynamics ; Mathematics ; Physics ; Physical chemistry ; Database Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Vinayak Bhat | Baskar Ganapathysubramanian | Chad Risko
Source: ChemRxiv (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline Organic semiconductors offer tremendous potential across a wide range of (opto)electronic applications. However, the development of these materials is limited by trial-and-error design approaches, as well as computationally heavy modeling approaches to evaluate/screen candidates using a suite of materials descriptors. For the latter, for instance, density functional theory (DFT) methods are widely used to derive descriptors such as the oxidation and reduction potentials, molecular relaxation and reorganization energies, and intermolecular electronic couplings; these calculations are compute-intensive, often requiring hours to days to determine. Such bottlenecks slow the pace and limit the exploration of the vast chemical space that can comprise organic materials. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic, molecule-based crystalline materials that take a few seconds, as compared to hours by DFT. Further, we use the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobilities and associated anisotropies of over 60,000 molecular crystal structures. The ML models and pipeline are made fully available on the open-access OCELOT ML infrastructure. Intermolecular force ; Organic semiconductor ; Pipeline (software) ; Relaxation (psychology) ; Semiconductor ; Chemical physics ; Computer science ; Density functional theory ; Materials science ; Charge (physics) ; Charge carrier ; Chemistry ; Computational chemistry ; Statistical physics ; Molecule ; Physics ; Optoelectronics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Hirofumi Tsuruta | Yukari Katsura | Masaya Kumagai
Source:  (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal structure into the features for input to the ML model. Currently, the most common method is to convert the crystal structure into a graph and predicting its properties using a graph neural network (GNN). Some GNN models, such as crystal graph convolutional neural network (CGCNN) and atomistic line graph neural network (ALIGNN), have achieved highly accurate predictions of material properties. Despite these successes, using a graph to represent a crystal structure has the notable limitation of losing the crystal structure's three-dimensional (3D) information. In this work, we propose DeepCrysTet, a novel deep learning approach for predicting material properties, which uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. DeepCrysTet provides a useful framework that includes a 3D mesh generation method, mesh-based feature design, and neural network design. The experimental results using the Materials Project dataset show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and achieves state-of-the-art performance in predicting elastic properties. Tetrahedron ; Computer science ; Artificial intelligence ; Materials science ; Deep learning ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography