Rapid advances in the capabilities of large language models and the broad accessibility of
tools powered by this technology have led to both excitement and concern regarding their …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might
improve research. Why are AI tools so attractive and what are the risks of implementing them …

Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …

Hybrid learning is a complex combination of face-to-face and online learning. This model
combines the use of multimedia materials with traditional classroom work. Virtual hybrid …

We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …

In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …