Concrete needs from our current and potential customers define Paladin Drill’s primary pathways for technological innovations. To meet or even exceed our clients’ expectations on the performances of our products under challenging environments, we have been constantly applying various techniques in computational chemistry to assist the traditional “trial-and-error” approach adopted in experimental chemistry, thus significantly facilitating the design and development of new drilling fluid additives. Specifically, we take advantage of a variety of computational methods (molecular dynamics, density functional method, ab initio method, etc.) that cover the entire spectra of spatial and temporal scales of interests to chemists; Numerous machine learning algorithms are used to train and optimize the product-specific models based on these computational methods. The evolving models we build can serve to predict the product properties (such as viscosity in media, thermal stability, ion resistance and adsorption on surfaces) that are relevant to drilling applications, and can therefore guide our design of suitable chemicals to achieve a remarkably short timeframe for product development.