Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

All physical laws are described as mathematical relationships between state variables.
These variables give a complete and non-redundant description of the relevant system …

The use of statistical/machine learning (ML) approaches to materials science is
experiencing explosive growth. Here, we review recent work focusing on the generation and …

The success of deep machine learning in processing of large amounts of data, for example,
in image or voice recognition and generation, raises the possibilities that these tools can …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

Synthetic Biology has overcome many of the early challenges facing the field and is entering
a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches …

Integrating automation and autonomy into self-driving laboratories promises more efficient
and reproducible experimentation while freeing scientists to focus on intellectual challenges …

Simultaneously capturing material structure and chemistry in the form of accessible data is
often advantageous for drawing correlations and enhancing our understanding of …

Abstract Robotic Process Automation (RPA) is a technological revolution in the offing and is
aimed at taking up the mundane and repetitive tasks from people's daily workload. It throws …