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Application of Integrated computational materials Engineering (ICME) in Aluminum Casting Development

INTERNATIONAL JOURNAL OF METALCASTING 2025年 1-16页

作者： Wang, Qigui Wang, Andy Coryell, Jason Gerard, Dale Jones, Peggy Sachdev, Anil Adv Mat Technol & Virtualizat Gen Motors Global Prod Dev Warren MI 48093 USA Saginaw Valley State Univ Coll Sci Engn & Technol Saginaw MI 48710 USA GM Global Res & Dev Warren MI 48090 USA

Automotive structure lightweighting is becoming increasingly important especially for electric vehicles to improve driving range. Aluminum shape castings offer effective lightweighting solutions because of their low density and high flexibility in product design and structural integration in comparison with steel parts. The aluminum casting processes are very complex and challenging, particularly for large integrated multi-functional castings. This requires not only good alloy selection and casting design but also optimal casting and heat treatment process control to achieve the desired material properties and product performance. One of the most effective ways to develop high integrity aluminum castings is through exploitation of virtual casting computational tools. This paper reports the advancement and implementation of multi-scale computational tools for virtual cast component development (VCCD) at GM using an integrated computational materials engineering (ICME) approach from an atom to auto. The VCCD tools have been extensively used for robust design of aluminum shape castings.

关键词： ICME computational materials from atom to auto virtual cast component development aluminum shape casting

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Star-gen: an HPC-AI framework for constructing large-scale computational materials database

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2025年第2期7卷 85-99页

作者： Chen, Pin Mo, Qing Xu, Zexin Zhang, Xianwei Lu, Yutong Sun Yat Sen Univ Sch Data & Comp Sci Natl Supercomp Ctr Guangzhou 132 East Circle & Univ City Guangzhou 510006 Peoples R China

Constructing large-scale, high-quality computational materials databases is pivotal for advancing material simulation and design. However, two essential challenges are yet to be fully resolved in this field: acquiring comprehensive atomic-level structural information and effectively executing large-scale computational tasks for these material structures on supercomputers. In this study, we present a methodology that adeptly couples Artificial Intelligence (AI) and High-Performance Computing (HPC) to establish a comprehensive computational database with diverse materials data. We propose an AI-driven pipeline with a periodic-E(3)-equivariant diffusion model for structure generation and a transformer-based property prediction model incorporating 3D geometric analysis for material structure evaluation, followed by calculations on selected structures using Density Functional Theory (DFT). Specifically, a high-throughput computing framework was developed for efficient execution of various CPU/GPU/IO-intensive tasks, capitalizing on the heterogeneous computing nodes and shared storage architecture of supercomputers. Based on our HPC-AI strategy, we generated approximately 10 million hypothetical crystal structures, constituting the most extensive crystal material database currently available. By leveraging 2,000 nodes of the Tianhe-2 supercomputer for high-throughput computations, we accomplished about 80,000 DFT calculation datasets within a span of three months. Our approach represents a data-driven paradigm for boosting the materials design practice.

关键词： HPC-AI framework computational materials High-throughput computing Large-scale materials generation

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computational Techniques for Accelerated materials Discovery

Computational Techniques for Accelerated Materials Discovery

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作者： Cerasoli, Franklin T. University of North Texas

学位级别：Ph.D., Doctor of Philosophy

Increasing ubiquity of computational resources has enabled simulation of complex electronic systems and modern materials. The PAOFLOW software package is a tool designed to construct and analyze tight binding Hamiltonians from the solutions of DFT calculations. PAOFLOW leverages localized basis sets to greatly reduce computational costs of post-processing QE simulation results, enabling efficient determination of properties such as electronic density, band structures in the presence of electric or magnetic fields, magnetic or spin circular dichroism, spin-texture, Fermi surfaces, spin or anomalous Hall conductivity (SHC or AHC), electronic transport, and more. PAOFLOW’s broad functionality is detailed in this work, and several independent studies where PAOFLOW’s capabilities directly enabled research on promising candidates for ferroelectric and spintronic based technologies are described. Today, Quantum computers are at the forefront of computational information science. materials scientists and quantum chemists can use quantum computers to simulate interacting systems of fermions, without having to perform the iterative methods of classical computing. This dissertation also describes a study where the band structure for silicon is simulated for the first time on quantum hardware and broadens this concept for simulating band structures of generic crystalline structures on quantum machines.

关键词： computational materials Tight binding Band structure Spintronics Quantum computing Variational quantum eigensolver

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Generalized representative structures for atomistic systems

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JOURNAL OF PHYSICS-CONDENSED MATTER 2025年第7期37卷 075901-075901页

作者： Goff, James M. Mullen, Coreen Yang, Shizhong Starovoytov, Oleg N. Wood, Mitchell A. Sandia Natl Labs Ctr Comp Res Albuquerque NM 87185 USA Elizabeth City State Univ Dept Math Comp Sci & Engn Technol Elizabeth City NC 27909 USA Southern Univ Comp Sci Dept Baton Rouge LA 70807 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

关键词： atomic structure generation atomic cluster expansion molecular dynamics computational materials machine-learning descriptors

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Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

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ACS APPLIED materials & INTERFACES 2025年第3期17卷 5496-5505页

作者： Robinson Brown, Dennis Hurlock, Matthew J. Nenoff, Tina M. Rimsza, Jessica M. Sandia Natl Labs Geochem Dept Albuquerque NM 87123 USA Sandia Natl Labs Nanoscale Sci Dept Albuquerque NM 87123 USA Sandia Natl Labs Adv Sci & Technol Albuquerque NM 87123 USA

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. Through this computational material design study, a previously unexplored mechanism in PL formation, solvent-solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

关键词： Carbon capture computational materials porousliquids metal organic frameworks materials design molecular dynamics

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Designing transparent conductors using forbidden optical transitions

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MATTER 2023年第9期6卷 3021-3039页

作者： Woods-Robinson, Rachel Xiong, Yihuang Shen, Jimmy-Xuan Winner, Nicholas Horton, Matthew K. Asta, Mark Ganose, Alex M. Hautier, Geoffroy Persson, Kristin A. Lawrence Berkeley Natl Lab Mat Sci Div Berkeley CA 94720 USA Univ Calif Berkeley Appl Sci & Technol Grad Grp Berkeley CA 94720 USA Dartmouth Coll Thayer Sch Engn 14 Engn Dr Hanover NH USA Lawrence Livermore Natl Lab Livermore CA USA Imperial Coll London Dept Chem Mol Sci Res Hub White City CampusWood Lane London England Univ Calif Berkeley Dept Mat Sci & Engn Berkeley CA USA Lawrence Berkeley Natl Lab Mol Foundry Div Berkeley CA USA

Many semiconductors present weak or forbidden transitions at their fundamental band gaps, inducing a widened region of transparency. This occurs in high-performing n-type transparent conductors (TCs) such as Sn-doped In2O3 (ITO);however, thus far, the presence of forbidden transitions has been neglected in the search for new p-type TCs. To address this, we first compute high-throughput absorption spectra across & DBLBOND;18,000 semiconductors, showing that over half exhibit forbidden or weak optical transitions at their band edges. Next, we demonstrate that compounds with highly localized band-edge states are more likely to present forbidden transitions. Lastly, we search this set for p-type and n-type TCs with forbidden transitions and, by performing defect calculations, propose unexplored TC candidates such as ambipolar BeSiP2, p-type wurtzite BAs, and n-type Ba2InGaO5, among others. We share our dataset and recommend that future screenings for optical properties consider the impact of forbidden transitions.

关键词： forbidden optical transitions transparent conductors transparent conducting materials optoelectronic properties optical absorption high-throughput screening density functional theory computational materials

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A Numeric Approach for Investigating Electron Dynamics in Zinc-Blende Semiconductor Heterostructures

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ADVANCED THEORY AND SIMULATIONS 2023年第10期6卷

作者： Reichman, Yair Toroker, Maytal Caspary Technion Israel Inst Technol Dept Mat Sci & Engn IL-3200003 Haifa Israel Nancy & Stephen Grand Technion Energy Program IL-320003 Haifa Israel

Charge transport via interfaces in materials is crucial in comprehending the constraints of many electronic devices. Gaining the knowledge and understanding of the processes entail when two materials form contact between them may allow designing better performance devices. Nonetheless, simulating experimental length scale structures puts a computational challenge. This work investigates several heterostructure systems of typical semiconductors with zinc-blende unit cells forming an interface in the contact plane. The purpose is to understand the trends in the electronic properties of such heterostructures regarding the strain profile, as it occurs near the interface and far away in deeper atomic planes. The local band-gap values of each layer of atoms are calculated to demonstrate how the gaps have changed with respect to the distance from the interface. Finally, the potential energy at the interface region is parameterized. The correlation between the transmission coefficient of the one-electron wave function is analyzed to pass through the interface and interface characteristic parameters. The primary conclusion is that the system's electrostatic potential embeds the heterostructure's unique properties. It is demonstrated how one can predict the transmission coefficient of the electronic wave function at the interface by examining the material's potential parameters at the interface exclusively.

关键词： charge transport computational materials DFT calculations semiconductor interfaces strain

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Substructure interaction graph network with node augmentation for hybrid chemical systems of heterogeneous substructures

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computational materials SCIENCE 2023年 216卷

作者： Na, Gyoung S. Korea Res Inst Chem Technol Daejeon South Korea

Complex chemical systems containing multiple heterogeneous substructures are common in real-world chemical applications, such as hybrid perovskites and inorganic catalysts. Although graph neural networks (GNNs) have achieved numerous successes in predicting the physical and chemical properties of a single molecule or crystal structure, GNNs for multiple heterogeneous substructures have not yet been studied in chemical science. In this paper, we propose substructure interaction graph network with node augmentation (SIGNNA) that is an integrated architecture of heterogeneous GNNs to predict the physical and chemical properties from the interactions between the heterogeneous substructures of the chemical systems. In addition to the network architecture, we devise a node augmentation method to generate valid subgraphs from given chemical systems for graph-based machine learning, even though the decomposed substructures are physically invalid. SIGNNA outperformed state-of-the-art GNNs in the experimental evaluations and the high-throughput screening on benchmark materials datasets of hybrid organic-inorganic perovskites and inorganic catalysts. The source code of SIGNNA is publicly available at https://***/ngs00/signna.

关键词： computational materials Machine learning Energy materials Catalysis

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computational investigations on the performance of transition metal carbide (MXene) for methylene blue dye adsorption and remediation from wastewater

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materials TODAY COMMUNICATIONS 2024年 39卷

作者： Liu, Huazhong Wang, Xiaolan Wang, Ruixue Xu, Han Yan, Weimin An, Qing Wuchang Univ Technol Artificial Intelligence Sch Wuhan Peoples R China Huanggang Normal Univ Sch Phys & Telecommun Huanggang Peoples R China Wuhan Donghu Univ Dept Basic Courses Wuhan Peoples R China

MXenes are receiving a lot of attention as adsorbents for environmental contaminants, due to their distinct characteristics, and the adsorption abilities were toughly influenced by the surface terminal groups. In this research the adsorption ability of free-standing Ti 3 C 2 for methylene blue (MB) dye as a major hazardous contaminant in the watery environment is investigated and compared with the O and OH terminated MXene counterparts using computational material tools. DFT simulations are carried out to explore the structural properties and electronic structures, thermodynamic stability, and adsorption behavior of MB on the considered MXenes surface. The Ti 3 C 2 monolayer is observed to behave as superior adsorbent for MB with strong interaction characteristic which is typical for the chemisorption followed by the Ti 3 C 2 (OH) 2 and Ti 3 C 2 O 2 MXenes with hydrogen bonding (strong physisorption) and electrostatics attractions, respectively. Meanwhile the DFTB-MD simulation of MB/MXene in ambient conditions reveals a strong ability of Ti 3 C 2 monolayer in MB adsorption by chemical bond formation between N/C atom of MB and surface Ti atom followed by hydrogen bond formation in MB/Ti 3 C 2 (OH) 2 system. The superior adsorption behavior of free-standing Ti 3 C 2 monolayer in aqueous solution, make this novel monolayer a promising adsorbent for MB adsorption and removal from watery contaminants. The present findings will be beneficial for the design of suitable nanoadsorbents for remediation of hazardous organic contaminants from wastewater.

关键词： DFT-MD simulation computational materials MXene Methylene blue Water treatment

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Artificial intelligence: machine learning for chemical sciences

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JOURNAL OF CHEMICAL SCIENCES 2022年第1期134卷 2-2页

作者： Karthikeyan, Akshaya Priyakumar, U. Deva Int Inst Informat Technol Ctr Computat Nat Sci & Bioinformat Hyderabad 500032 India

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences.

关键词： Deep learning machine learning computational chemistry drug design molecular design computational materials neural networks

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