Accelrys has released the Materials Studio Collection for Pipeline Pilot, which enables scientific R&D organisations to integrate predictive analytics for materials properties seamlessly into their scientific workflows. Materials Studio Collection extends Accelrys' Pipeline Pilot scientific informatics platform to include key materials modelling and simulation tools from Accelrys' Materials Studio application - offering a powerful combination that greatly increases research productivity across the scientific enterprise.
Predictive analytics with Materials Studio Collection allows for a streamlined approach to materials discovery, improving researchers' productivity so that scientists can spend more time on innovation and less on costly, unnecessary, laboratory experimentation. Compared to trial and error approaches, predictive models enable researchers to explore a broad range of compound candidates virtually, identify those not meeting the required specifications, and quickly narrow the search for the most promising leads. The integration of Accelrys Materials Studio with the Accelrys Pipeline Pilot platform makes it possible to capture best practices in the materials domain within automated scientific workflows - eliminating human error, improving collaboration, while increasing productivity and research success rates.
With the Materials Studio Collection, researchers can easily predict key properties, simulate analytical instrumentation and provide insights into the behavior of materials in areas such as catalysis, electronics, polymers and pharmaceutical discovery and development. Materials researchers now have the ability to seamlessly capture, automate and share their work and thus speed innovation and increase competitive advantage. Through the integration of different data types - including solid-state materials data, in silico modelling results, reports, analytical instrumentation and other information - Materials Studio Collection for Pipeline Pilot delivers a broad range of synergies previously unavailable to the scientific enterprise. Furthermore, informatics IT costs can be reduced as predictive analytics technology and other scientific informatics applications can be developed, deployed and integrated on the same platform.
