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64 SMT007 MAGAZINE I AUGUST 2019 5. Leave a Lasting Impression Customers have several options in selecting suppliers to fulfill their needs. Becoming a pre- ferred supplier takes a concerted effort to be unique. The minimum expectation is for the supplier to deliver products with good qual- ity and on-time. The differentiator will be the overall customer experience a customer has with a supplier. When you re-engineer your quality system, make sure that all processes that have customer-facing interactions have been designed to provide your customer with a positive, lasting impression. Make your orga- nization stand out amongst your competitors with these two principles: 1. Constant communication with your customer 2. Proactively provide data, plans or technical reports before your customer asks for this; be one step ahead of your customer and two steps ahead of your competitors SMT007 References 1. "Quality Management Principles," The International Organization for Standardization, Edition 2, 2015. Alfred Macha is the president of AMT Partners. He can be reached at Alfred@amt-partners.com. To read past columns or contact Macha, click here. Researchers at Georgia Institute of Technology are using supercomputers and machine-learning techniques to find ways to build more capable capacitors. The researchers focused on finding a way to more quickly analyze the electronic structure at the atomic level of two capacitor materials—aluminum and polyethyl- ene—looking for features that could affect performance. The researchers used the Comet supercomputer at the San Diego Supercomputer Center, an Organized Research Unit of the University of California San Diego, for early cal- culations, and the Stampede2 supercomputer at the Texas Advanced Computing Center at the University of Texas at Austin for the later stages of this research. Both systems are funded by the National Science Foundation under multi-year awards. Using the new machine-learning method, the research- ers produced similar results several orders of magnitude faster than using the conventional technique based on quantum mechanics. While the study focused on aluminum and polyethylene, machine learning could be used to analyze the electronic structure of a wider range of materials. Beyond analyzing electronic structure, other aspects of material structure now analyzed by quantum mechanics could also be has- tened by the machine-learning approach. The faster processing allowed by the machine-learning method would also enable researchers to more quickly simulate how modifications to a material will impact its electronic structure, potentially revealing new ways to improve its efficiency. The method was described in Nature Partner Journal's Computational Materials. (Source: Georgia Tech) Machine Learning Helps Create More Capable Capacitors