# Data-driven materials science: status, challenges and perspectives

## TL;DR

This paper reviews the evolution, current state, and future challenges of data-driven materials science, emphasizing the importance of data resources, machine learning, and infrastructure for discovering new materials.

## Contribution

It provides a comprehensive overview of the development, successes, and challenges in data-driven materials science, highlighting future perspectives.

## Key findings

- Growth of materials databases and high-throughput methods
- Challenges in data veracity and standardization
- Gap between industrial and academic research efforts

## Abstract

Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials data sets that are too big or complex for traditional human reasoning - typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, we discuss the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures. We also review key successes and challenges so far, providing a perspective on the future development of the field.