Every day, enormous amounts of experimental and theoretical data are becoming available in the diverse research fields. For example, in chemistry the chemical space is explored, in physics the fundamental structures of particles that make up the world we know are examined, and in biology the paradigm is studied that leads from the DNA to structure, function and regulation.
Assuming the requirements for both high quality and access to data are met, the perspectives for the computational approach are promising but also raise a number of questions which will be addressed in the symposium.
Assuming the requirements for both high quality and access to data are met, the perspectives for the computational approach are promising but also raise a number of questions which will be addressed in the symposium.
- How much impact do AI and simulation have on future science?
- Can simulations replace experiments?
- What is the contribution of predictions to innovative processes?
- Can AI and ML drive knowledge and innovation?
- How can we resolve and control complexity?
- What does AI mean in the context of chemical and biological sciences?
- What’s the difference between AI and machine learning?
- How much is AI capable to solve chemical problems?
