Digital Discovery

Digital Discovery is an open access journal that publishes both theoretical and experimental research at the intersection of chemistry, materials science and biotechnology. We focus on the development and application of machine learning, AI and automation tools to unravel scientific problems, and we put data first to ensure reproducibility and faster progress for everyone. Chemists, biologists, engineers, physicists – if your work is driving digital transformation, you are home.

Scope

Digital Discovery welcomes both experimental and computational work on all topics related to the acceleration of discovery such as screening, robotics, databases and advanced data analytics, broadly defined, but anchored in chemistry.  

The journal publishes research related to chemical, materials, biochemical, biomedical, or biophysical sciences and specific topics include: 

• Artificial intelligence and other high throughput computational methodologies for molecular, materials and formulation design
• Advanced data workflows
• Novel experimental automation
• Papers at the interface of chemistry and other sciences

Papers that will not be considered are in the areas of low-throughput structural or mechanistic studies using computational chemistry, QM/MM studies of biochemical mechanisms at low throughput, traditional analysis of molecular dynamics trajectory simulations to understand biological conformations, reports or comparisons of electronic structure methods that do not involve machine learning, interpretations of chemical bonding models, and quantum dynamics and spectroscopy simulations at low throughput. 

 The "digital transformation" of the chemical industry is a huge driver for the twenty-first century and we want this journal to be the premier venue for papers related to this topic.

Alán Aspuru-Guzik, Editor-in-Chief