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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Inverse molecular design using machine learning: Generative models for matter engineering.

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  • 1Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA.

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Summary
This summary is machine-generated.

Exploring new materials is key for progress but computationally challenging. This review covers inverse design methods, using artificial intelligence and deep generative models to discover materials with desired functions efficiently.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Discovering novel materials drives societal and technological advancement.
  • The vast search space of potential materials makes exhaustive exploration computationally intractable.
  • Inverse design offers a paradigm shift, focusing on desired functionality to guide material discovery.

Purpose of the Study:

  • To review current methods for inverse material design.
  • To highlight the impact of artificial intelligence (AI) and machine learning (ML) on this field.
  • To showcase applications of deep generative models in discovering tailored materials.

Main Methods:

  • Review of inverse design strategies.
  • Application of deep generative models (a subset of ML/AI).
  • Analysis of AI-driven approaches for materials discovery.

Main Results:

  • AI, particularly deep generative models, accelerates inverse molecular design.
  • These methods are successfully applied to diverse material classes, including drugs, organic compounds, photovoltaics, batteries, and solid-state materials.
  • Successful examples demonstrate the potential for rational design of materials with specific properties.

Conclusions:

  • Inverse design, powered by AI, is a transformative approach to materials discovery.
  • Deep generative models offer powerful tools for efficiently identifying materials with targeted functionalities.
  • This methodology promises significant advancements across various scientific and technological domains.