  Reliable predictions of the behaviour of chemical systems are essential
across many industries, from nanoscale engineering over validation of advanced
materials to nanotoxicity assessment in health and medicine. For the future we
therefore envision a paradigm shift for the design of chemical simulations
across all length scales from a prescriptive to a predictive and quantitative
science. This paper presents an integrative perspective about the
state-of-the-art of modelling in computational and theoretical chemistry with
examples from data- and equation-based models. Extension to include reliable
risk assessments and quality control are discussed. To specify and broaden the
concept of chemical accuracy in the design cycle of reliable and robust
molecular simulations the fields of computational chemistry, physics,
mathematics, visualisation science, and engineering are bridged. Methods from
electronic structure calculations serve as examples to explain how
uncertainties arise: through assumed mechanisms in form of equations, model
parameters, algorithms, and numerical implementations. We provide a full
classification of uncertainties throughout the chemical modelling cycle and
discuss how the associated risks can be mitigated. Further, we apply our
statements to molecular dynamics and partial differential equations based
approaches. An overview of methods from numerical mathematics and statistics
provides strategies to analyse risks and potential errors in the design of new
materials and compounds. We also touch on methods for validation and
verification. In the conclusion we address cross-disciplinary open challenges.
In future the quantitative analysis of where simulations and their prognosis
fail will open doors towards predictive materials engineering and chemical
modelling.
