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Discovering and understanding materials through computation.

Steven G Louie1,2, Yang-Hao Chan3,4,5, Felipe H da Jornada6

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Computational materials modeling, using quantum and classical methods, is crucial for scientific discovery. Future advances in computational approaches and machine learning will accelerate the search for new materials.

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Area of Science:

  • Condensed matter physics, chemistry, and materials science.
  • Computational materials science and quantum mechanics.
  • Interdisciplinary research combining computation and experimentation.

Background:

  • Computational modeling is a well-established research pillar alongside experiments and analytical theories.
  • Significant advancements in computational methodologies have been made over recent decades.
  • These methods are used to predict material properties across various scales, from molecules to bulk systems.

Purpose of the Study:

  • To provide an overview of progress in computational materials modeling.
  • To discuss future challenges and opportunities in the field.
  • To highlight the role of computational approaches in materials discovery.

Main Methods:

  • Computational quantum and classical approaches for materials modeling.
  • Methodology development and application for property prediction.
  • Review of existing literature and expert perspectives.

Main Results:

  • Computational modeling is essential for understanding and predicting material properties.
  • The field has seen tremendous advances in methodology and applications.
  • Future computational tools will be increasingly powerful and versatile.

Conclusions:

  • Computational approaches, enhanced by machine learning, will guide future materials discovery.
  • Continued development of computational methods is vital for scientific and technological advancement.
  • Interdisciplinary collaboration is key to unlocking the potential of materials modeling.