检索结果-内蒙古大学图书馆

Accelerated multi-Objective Alloy Discovery through Efficient Bayesian methods: Application to the FCC Alloy Space

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hastings, Trevor mulukutla, mrinalini Khatamsaz, Danial Salas, Daniel Xu, Wenle Lewis, Daniel Person, Nicole Skokan, matthew miller, Braden Paramore, James Butler, Brady Allaire, Douglas Attari, Vahid Karaman, Ibrahim Pharr, George Srivastava, Ankit Arróyave, Raymundo Texas A&M University Department of Materials Science and Engineering College StationTX77843-3003 United States DEVCOM Army Research Laboratory South Texas A&M University College StationTX77843-3003 United States Texas A&M University Department of Mechanical Engineering College StationTX77843 United States

This study introduces BIRDSHOT, an integrated Bayesian materials discovery framework designed to efficiently explore complex compositional spaces while optimizing multiple material properties. We applied this framework to the CoCrFeNiVAl FCC high entropy alloy (HEA) system, targeting three key performance objectives: ultimate tensile strength/yield strength ratio, hardness, and strain rate sensitivity. The experimental campaign employed an integrated cyber-physical approach that combined vacuum arc melting (VAm) for alloy synthesis with advanced mechanical testing, including tensile and high-strain-rate nanoindentation testing. By incorporating batch Bayesian optimization schemes that allowed the parallel exploration of the alloy space, we completed five iterative design-make-test-learn loops, identifying a non-trivial three-objective Pareto set in a high-dimensional alloy space. Notably, this was achieved by exploring only 0.15% of the feasible design space, representing a significant acceleration in discovery rate relative to traditional methods. This work demonstrates the capability of BIRDSHOT to navigate complex, multi-objective optimization challenges and highlights its potential for broader application in accelerating materials discovery. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensile testing

Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Khatamsaz, Danial Wagner, Joseph Vela, Brent Arróyave, Raymundo Allaire, Douglas L. Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University College StationTX77843 United States Wm Michael Barnes’64 Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States

Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over a problem formulation space to identify optimal design formulations in line with decision-maker preferences. By mapping various design scenarios to a multi-attribute utility function, our approach enables the system to balance conflicting objectives—such as ductility, yield strength, density, and solidification range—without requiring an exact problem definition at the outset. We demonstrate the efficacy of our method through an in silico case study on a mo-Nb-Ti-V-W alloy system targeted for gas turbine engine blade applications. The framework converges on a"sweet spot" that satisfies critical performance thresholds, illustrating that integrating problem formulation discovery into the autonomous design loop can significantly streamline the experimental process. Future work will incorporate human feedback to further enhance the adaptability of the system in real-world experimental settings. © 2025, CC BY.

关键词： Titanium alloys

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Hardcastle, Christofer O’mullan, Ryan Arróyave, Raymundo Vela, Brent Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University College StationTX77843 United States Wm Michael Barnes’64 Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States

Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies, we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the boundaries of feasible design spaces. Through three case studies, we highlight the utility of informative priors for handling constraints on continuous and categorical properties. (1) Phase Stability: By incorporating CALPHAD predictions as priors for solid-solution phase stability, we enhance model validation using a publicly available XRD dataset. (2) Phase Stability Prediction Refinement: We demonstrate an in silico active learning approach to efficiently correct phase diagrams. (3) Continuous Property Thresholds: By embedding priors into continuous property models, we accelerate the discovery of alloys meeting specific property thresholds via active learning. In each case, integrating physics-based insights into the classification framework substantially improved model performance, demonstrating an efficient strategy for constraint-aware alloy design. Copyright © 2025, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

Statistical Analysis of Car Data Using Analysis of Covariance (ANCOVA) 1

学校读者我要写书评

暂无评论

3rd International Conference on mathematics and its Applications in science and engineering, ICmASE 2022

作者： Syam, Thaer Syam, mahmoud m. Khan, Adnan Syam, mahmoud I. Syam, muhammad I. Department of Mechanical Engineering Texas A&M University College StationTX3123 TAMU United States School for Engineering of Matter Arizona State University Transport & Energy TempeAZ85287-6106 United States Department of Material Science and Engineering Texas A&M University College StationTX3003 TAMU United States Department of Mathematics Foundation Program Qatar University P.O. 2713 Doha Qatar Department of Mathematical Sciences College of Science UAE University Al-Ain Al Ain United Arab Emirates

ISBN: (数字)9783031217005

ISBN: (纸本)9783031216992

engineering statistics involves data concerning manufacturing processes such as: component dimensions, tolerances, type of material, and fabrication process control. In this work, some statistical analysis was done on a car data which was collected in 1983. This data set contains a sample of 66 cars from three different countries producers (USA, Japan, and Europe) in terms of their price and 13 different features. The objective was to determine how these quantitative variables affect the car price using the Analysis of Covariance (ANCOVA). Some analysis was done to find the best multiple Linear Regression (mLR) model that identifies the most significant factors that influence the car price. The addition of removal of new factors was based on trial and error and based on the value of the linear correlation with the car price (Pearson coefficient of correlation r). 9 out of 13 factors were chosen based on mLR and the model selection/reduction analysis. The conclusion from ANCOVA analysis found that the European cars are the most expensive cars when compared to USA and Japan. Japan cars are the second expensive and USA cars are the cheapest. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linear regression

Robust Quantized Consensus of multi-agent Systems Under Disturbance and DoS Attacks

学校读者我要写书评

暂无评论

IEEE Transactions on Control of Network Systems 2025年

作者： Wang, Xinhe Wen, Guanghui Zhao, Dan Huang, Tingwen Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University Department of Systems Science School of Mathematics Nanjing211189 China Texas A&M University Science Program College StationTX77840 United States

External disturbance and denial-of-service (DoS) attacks pose significant challenges to the quantized control of multi-agent systems (mAS). most of the existing quantized control strategies primarily focus on the scaling factors design and auxiliary system construction without external disturbance. Note that, these strategies require highly accurate system model and will lead to the saturation of the quantizer if there exists external disturbance. To overcome the above shortcomings, a new scaling function is developed in this paper by incorporating robustness factors into the scaling function design, significantly enhancing the robustness of the quantization mechanism. Based on this, a robust quantized control strategy is designed to achieve the bounded consensus of linear mAS in the presence of disturbance and DoS attacks, where the trade-off among quantization level, consensus performance, and resilience to DoS attacks is explored. Besides, the robust design framework shows significant flexibility and efficiency in addressing the resilient control of nonlinear mAS subject to DoS attacks. Numerical simulations are provided to validate the theoretical results. © 2014 IEEE.

关键词： Denial-of-service attack

Shear strain alters the structure and migration mechanism of self-interstitial atoms in copper

学校读者我要写书评

暂无评论

Physical Review materials 2022年第5期6卷 053605-053605页

作者： B. Zhang C. Wheatley P. Chen X. Qian m. J. Demkowicz Department of Materials Science and Engineering Texas A&M University College Station Texas 77845 USA Department of Materials Science and Engineering Penn State University State College Pennsylvania 16801 USA Key Laboratory of Automobile Materials of Ministry of Education School of Materials Science and Engineering Jilin University Changchun People's Republic of China Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77845 USA

We use atomistic modeling to show that externally applied shear strain causes the lowest energy self-interstitial atom (SIA) structure in copper (Cu) to change from a ⟨100⟩-type dumbbell to a ⟨110⟩-type dumbbell. Concurrently, SIA migration switches from the three-dimensional (3D) random walk characteristic of ⟨100⟩-type dumbbells to a 1D mechanism analogous to that of crowdion SIAs. Furthermore, the relative energies of these two dumbbell structures as a function of strain are well predicted using elastic dipole tensors computed at zero strain, indicating that examination of these tensors may be used to assess the likelihood of strain-induced SIA structure transitions in other materials. Changes in lowest energy SIA structures and associated migration mechanisms stand to impact predictions of SIA behavior in irradiated solids.

关键词： Interstitials Face-centered cubic Transition metals Density functional calculations

High-Throughput Alloy and Process Design for metal Additive manufacturing

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Sheikh, Sofia Vela, Brent Honarmandi, Pejman morcos, Peter Shoukr, David Kotb, Abdelrahman mostafa Karaman, Ibrahim Elwany, Alaa Arróyave, Raymundo Department of Materials Science and Engineering Texas A&M University College StationTX United States Department of Mechanical Engineering Texas A&M University College StationTX United States Department of Industrial and Systems Engineering Texas A&M University College StationTX United States

Designing alloys for additive manufacturing (Am) presents significant opportunities, but the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via Am) are difficult to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide the search for highly printable alloys and appropriate processing parameters. The framework uses material properties from state-of-the-art databases, processing parameters, and simulated melt pool profiles to predict process-induced defects, such as lack-of-fusion, keyholing, and balling. We accelerate the assessment of printability using a deep learning surrogate for a thermal model, enabling a 1,000-fold acceleration in the assessment of the printability of a given alloy at no loss in accuracy when compared with conventional physics-based thermal models. We verify and validate the framework by constructing printability maps for the CoCrFemnNi Cantor alloy system and comparing our predictions to an exhaustive 'in-house' database. The framework enables the systematic investigation of the printability of a wide range of alloys in the broader Co-Cr-Fe-mn-Ni HEA system. We identified the most promising alloys that were suitable for high-temperature applications and had the narrowest solidification ranges, and that was the least susceptible to balling, hot-cracking, and the formation of macroscopic printing defects. A new metric for the global printability of an alloy is constructed and is further used for the ranking of candidate alloys. It is expected that the proposed framework could be integrated into ICmE approaches to accelerate the discovery and optimization of novel high-performance, printable alloys. © 2023, The Authors. All rights reserved.

关键词： 3D printing

High-throughput Alloy and Process Design for metal Additive manufacturing

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Sheikh, Sofia Vela, Brent Honarmandi, Pejman morcos, Peter Shoukr, David Kotb, Abdelrahman mostafa Arróyave, Raymundo Karaman, Ibrahim Elwany, Alaa Department of Materials Science and Engineering Texas A&M University College StationTX United States Department of Mechanical Engineering Texas A&M University College StationTX United States Department of Industrial and Systems Engineering Texas A&M University College StationTX United States

Designing alloys for additive manufacturing (Am) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via Am) are challenging to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide the search for highly printable alloys and appropriate processing parameters. The framework uses material properties from state-of-the-art databases, processing parameters, and simulated melt pool profiles to predict process-induced defects, such as lack-of-fusion, keyholing, and balling. We accelerate the printability assessment using a deep learning surrogate for a thermal model, enabling a 1,000-fold acceleration in assessing the printability of a given alloy at no loss in accuracy when compared with conventional physics-based thermal models. We verify and validate the framework by constructing printability maps for the CoCrFemnNi Cantor alloy system and comparing our predictions to an exhaustive’in-house’ database. The framework enables the systematic investigation of the printability of a wide range of alloys in the broader Co-Cr-Fe-mn-Ni HEA system. We identified the most promising alloys that were suitable for high-temperature applications and had the narrowest solidification ranges, and that was the least susceptible to balling, hot-cracking, and the formation of macroscopic printing defects. A new metric for the global printability of an alloy is constructed and is further used for the ranking of candidate alloys. The proposed framework is expected to be integrated into ICmE approaches to accelerate the discovery and optimization of novel high-performance, printable alloys. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D printing

A materials Foundation model via Hybrid Invariant-Equivariant Architectures

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yan, Keqiang Bohde, montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arróyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Materials Science & Engineering Texas A&M University College StationTX United States Lambda Inc. United States J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University College StationTX United States Wm Michael Barnes’64 Department of Industrial and Systems Engineering Texas A&M University College StationTX United States Brookhaven National Laboratory UptonNY United States Department of Physics & Astronomy Texas A&M University College StationTX United States

materials foundation models can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform well when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant foundation model that integrates both invariant and equivariant message passing layers. HIENet is designed to achieve superior performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet. © 2025, CC BY-NC-SA.

关键词： Prediction models