Why Do We Need AI in R&D?

The future of materials science is being shaped by AI, revolutionizing how researchers approach material discovery and design. Traditional trial-and-error methods are slow and resource-intensive, but AI is unlocking a new era—where discovery is faster, smarter, and more targeted than ever before.

Machine learning force fields (MLFFs) are transforming molecular dynamics simulations by increasing the accuracy and reducing the computational costs of classical simulation techniques, enabling more accurate, efficient simulations across materials science, chemistry and biology.

Solid-state batteries may one day outperform lithium-ion batteries, offering higher energy density, improved safety, and longer lifespans. However, there are still major challenges to overcome before this technology can compete in the global market.

Despite the impact the solid electrolyte interphase (SEI) has on the performance of modern batteries, studying it remains a major challenge in battery R&D.

The primordial soup experiment was the first to provide proof of the origins of life. We set out to simulate one of the most important experiments in the history of modern science using the quantum nanoreactor feature within QuantistryLab.

To make the batteries of the future, R&D scientists are looking at materials sourced from nature. But can sustainable materials truly replace the battery materials we use today?