Getting noticed!

By September 11th, 2018

High-throughput experimentation is one part of the larger materials discovery journey. The RAMP centre was used/cited as an example in a recent MRS Bulletin review by Logan Ward et al. regarding the strategies for accelerating the adoption of materials informatics.

There is, however, substantial progress in high-throughput experimentation (HTE), which focuses on evaluating many materials in rapid succession and is uniquely suited to creating informatics-ready data—large resources of consistently measured and consistently processed samples. Beyond the technical limitations of automating certain types of experiments, adoption of HTE is slowed by limited access to HTE equipment, such as combinatorial synthesis machines (e.g., cosputtering chambers) or automated characterization equipment (e.g., robotic x-ray diffractometers). Consequently, collaborations that allow scientists to access a distributed ecosystem of HT synthesis, characterization, and data infrastructure are necessary62along with development of new HT experimental facilities.63,64


image taken from article

Our vision for the data infrastructure that will allow materials informatics to become more prevalent. The key requirement for materials informatics is a database that is machine-accessible (i.e., easy to use from software). We propose that the development and widespread use of automated experimentation tools, digital data management software (e.g., electronic lab notebooks), and automated methods for curating data will accelerate the growth and improve the availability of the well-curated data necessary for materials informatics.

Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.