Big Data issues in Computational Chemistry

Digital data have become a torrent engulfing every area of business, science and engineering disciplines. In the age of Big Data, deriving values and insights from large amounts of data using rich analytics becomes an important differentiating capability for competitiveness, success and leadership in every field. Scientists and engineers of many different domains are increasingly clamouring for mechanisms to manage and analyse the massive quantities of information now available in order to obtain new answers and extract from it maximum value. Computational modelling and simulation is the central technology to numerous of these domains. Molecular Dynamics (MD) is a computational simulation technique that describes the physical forces and movements of interacting microscopic elements such atoms and molecules. MD has important applications in the fields of chemistry, biotechnology, pharmaceutical industry, energy, climate or materials science, among others. Advanced MD algorithms include not only Molecular Mechanics (MM), but also Quantum Mechanics (QM) approaches, raising important big data challenges still to be sorted out. MD simulations perform an iterative process generating large amounts of data in streaming. Current software technology is far from being able to manage, analyze and visualize the extremely large and complex data sets generated by important molecular processes. This paper analyzes the current big data limits in the Computational Chemistry field, especially in the MD processes. To overcome these challenging situations, this work provide guidance for future research including advances in scalable algorithms for data analysis, dynamic query technology, data models and storage strategies, parallel executions, I/O optimization, and interactive visual exploration and analysis of MD data.