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Related Concept Videos

Phase Diagram01:19

Phase Diagram

5.9K
The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).
5.9K
Phase Diagrams02:39

Phase Diagrams

41.6K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
41.6K

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Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven

Emily M Williamson1, Zhaohong Sun1, Bryce A Tappan1

  • 1Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.

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Data-driven learning accelerates copper selenide phase mapping. This approach uses machine learning to predict material phases, enabling efficient synthesis of specific copper selenide structures with high accuracy.

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Area of Science:

  • Materials Science
  • Inorganic Chemistry
  • Crystallography

Background:

  • Copper selenides are vital materials with diverse applications.
  • The copper-selenium phase diagram is complex, featuring multiple stable and metastable crystal structures.
  • Synthetically controlling copper selenide phases is challenging due to this complexity.

Purpose of the Study:

  • To demonstrate data-driven learning for mapping the complex copper selenide phase space.
  • To develop a predictive model for determining copper selenide phases with minimal experiments.
  • To enable accelerated and targeted synthesis of specific copper selenide phases.

Main Methods:

  • Utilized soft chemistry (chimie douce) synthetic methods.
  • Employed multivariate analyses with classification techniques for predictive phase determination.
  • Constructed a surrogate model using experimental data from four variables: precursor bond strength, time, temperature, and solvent composition.

Main Results:

  • Generated 11 distinct copper selenide phase combinations within the surrogate model.
  • Trained a classification model achieving 95.7% accuracy in phase prediction.
  • Developed a decision tree model identifying key experimental variables influencing phase formation.

Conclusions:

  • Data-driven learning effectively maps complex material phase spaces.
  • The predictive model provides prescriptive synthetic conditions for targeted phase isolation.
  • Successfully guided accelerated synthesis of klockmannite copper selenide (CuSe), validating the approach.