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Vahid Attari

Texas A&M University - Department of Materials Science and Engineering

Langford Building A

798 Ross St.

College Station, TX 77843-3137

United States

SCHOLARLY PAPERS

8

DOWNLOADS

954

TOTAL CITATIONS

75

Scholarly Papers (8)

1.

Exploration of the Microstructure Space in Tialzrn Ultra-Hard Nanostructured Coatings

Number of pages: 34 Posted: 03 Jan 2019
Vahid Attari, Aitor Cruzado and Raymundo Arroyave
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Aerospace Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 172 (437,795)
Citation 4

Abstract:

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Transition Metal Nitrides; Spinodal Decomposition; Nanostructures; Phase Field Modeling

2.

Finite Interface Dissipation Phase Field Modeling of Ni-Nb Under Additive Manufacturing Conditions

Number of pages: 36 Posted: 20 Jun 2019
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Industrial and Systems Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Mechanical Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Industrial and Systems Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 166 (451,954)
Citation 3

Abstract:

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Additive manufacturing, Non-equilibrium phase field modeling, Rapid solidification, Microsegregation, Experimental validation, Cellular growth, Planar Growth, Absolute Stability

3.

Semi-Supervised Learning Approaches to Class Assignment in Ambiguous Microstructures

Number of pages: 26 Posted: 10 Dec 2019
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Electrical Engineering, Texas A&M University - Department of Electrical Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 158 (472,207)
Citation 2

Abstract:

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Machine learning, Microstructure classification, Support vector machines, Semi-supervised learning methods, Unsupervised error estimation

4.

Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-Field Model

Number of pages: 22 Posted: 31 Jul 2019
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Mechanical Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Mechanical Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 134 (544,251)
Citation 3

Abstract:

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Phase-field modeling, Uncertainty propagation, Uncertainty quantification, Thermoelectrics, Microstructure, Mass scattering, Phonon scattering

5.

Microstructure Classification in the Unsupervised Context

Number of pages: 30 Posted: 05 Oct 2020
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University- Department of Materials Science and Engineering Department, Texas A&M University- Department of Mechanical Engineering, Texas A&M University - Department of Materials Science and Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 118 (604,538)
Citation 4

Abstract:

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Micro-structure Classification, Unsupervised Learning, Phase Field Modeling

6.

Microstructure-Aware Bayesian Materials Design

Number of pages: 15 Posted: 06 Mar 2025
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 83 (783,685)

Abstract:

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Microstructure-Aware, Materials Design and Discovery, Bayesian Optimization, Gaussian Process

7.

Accelerated Multi-Objective Alloy Discovery Through Efficient Bayesian Methods: Application to the FCC High Entropy Alloy Space

Number of pages: 20 Posted: 04 Apr 2025
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 79 (809,932)

Abstract:

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Bayesian Optimization, Materials Discovery, high-throughput, Multi-Objective Optimization, Machine learning

8.

Data-Driven Insights into Composition–Property Relationships in FCC High-Entropy Alloys

Number of pages: 10 Posted: 25 Aug 2025
Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University, Georgia Institute of Technology, Georgia Institute of Technology, Georgia Institute of Technology, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering, Texas A&M University, Texas A&M University - Department of Materials Science and Engineering and Texas A&M University - Department of Materials Science and Engineering
Downloads 44 (1,133,231)
Citation 59

Abstract:

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mechanical properties, Modeling, Nanoindentation, High-Entropy Alloys, Machine learning