Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2024An Interoperable Multi Objective Batch Bayesian Optimization Framework for High Throughput Materials Discoverycitations
  • 2023Bayesian optimization with active learning of design constraints using an entropy-based approach61citations

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Chart of shared publication
Arroyave, Raymundo
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Pharr, George
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Butler, Brady
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Lewis, Daniel
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Salas, Daniel
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Karaman, Ibrahim
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Hastings, Trevor
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Mulukutla, Mrinalini
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Khatamsaz, Danial
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Srivastava, Ankit
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Person, Nicole
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Skokan, Matthew
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Miller, Braden
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Vela, Brent
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2024
2023

Co-Authors (by relevance)

  • Arroyave, Raymundo
  • Pharr, George
  • Butler, Brady
  • Lewis, Daniel
  • Salas, Daniel
  • Karaman, Ibrahim
  • Hastings, Trevor
  • Mulukutla, Mrinalini
  • Khatamsaz, Danial
  • Srivastava, Ankit
  • Xu, Wenle
  • Person, Nicole
  • Skokan, Matthew
  • Miller, Braden
  • Vela, Brent
OrganizationsLocationPeople

article

Bayesian optimization with active learning of design constraints using an entropy-based approach

  • Vela, Brent
  • Allaire, Douglas
Abstract

<jats:title>Abstract</jats:title><jats:p>The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.</jats:p>

Topics
  • density
  • impedance spectroscopy
  • strength
  • thermal expansion
  • yield strength
  • thermal conductivity
  • solidification