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Data-Driven Strategies for Accelerated Materials Design.

Robert Pollice^1,2, Gabriel Dos Passos Gomes^1,2, Matteo Aldeghi^1,2,3

¹Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada.

Accounts of Chemical Research

|February 2, 2021

Summary

This summary is machine-generated.

Machine learning accelerates the discovery of new materials for clean energy and advanced technologies by overcoming the limitations of traditional methods. Data-driven approaches like virtual screening and Bayesian optimization are key to this materials science revolution.

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Area of Science:

Material Science
Computational Chemistry
Data Science

Background:

The complexity of material discovery necessitates advanced computational tools.
Machine learning (ML) and artificial intelligence (AI) offer powerful alternatives to traditional methods for exploring vast chemical spaces.
The urgent need for new materials in clean energy and advanced technologies drives innovation in ML for materials science.

Purpose of the Study:

To review recent contributions in machine learning for material science.
To highlight data-driven approaches for accelerating materials discovery and design.
To discuss methodologies including high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning.

Main Methods:

Data-driven approaches for property prediction and optimization.
High-throughput virtual screening for rapid material evaluation.
Inverse molecular design for targeted material synthesis.
Bayesian optimization and supervised learning for efficient exploration of chemical space.

Main Results:

Demonstrated acceleration in the discovery and design of organic electronic materials and crystalline materials.
Successful implementation of various ML methodologies for property prediction and chemical space exploration.
Identified successful use cases and provided examples of data-driven material discovery.

Conclusions:

Machine learning is revolutionizing material science, enabling faster discovery and design of novel materials.
Data-driven methodologies are crucial for tackling challenges in clean energy and advanced technology development.
Continued adaptation and implementation of large-scale data-driven approaches are foreseen for future material discovery campaigns.