Machine Learning in the Cultivation of Next Generation Technology

Presenter

Ethan T. Vaughan

Campus

Berkshire Community College

Sponsor

Colin Wilson, Department of Science, Berkshire Community College

Schedule

Session 1, 10:30 AM - 11:15 AM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A6, Campus Center Auditorium, Row 1 (A1-A20) [Poster Location Map]

Abstract

The development of technology with the potential and understanding that comes with it is necessary for the advancement of the human race. By creating new polymers and compounds using next generational materials, we strive towards more clean and renewable energy. With the novel approach of using machine learning in Artificial Intelligence(A.I.) models to decipher and manage complex problems and equations, we have made leaps and bounds towards more efficient materials. Based on advancement of material science, A.I. is used as a shortcut for mathematical problems and probability assessments. It is beginning to evolve the methodology that scientists use for the discovery of new materials. For applications in centrifugal manufacturing, alternative power supplies, and space age batteries, Material Scientists have been hard at work, but are hindered by crystalline structure defects of graphene which can be addressed and minimized using machine learning A.I.. With the synthesis of new age materials such as carbon graphene, a nuanced approach is needed. NASA has been testing the possibilities of manufacturing in space like centrifugal manufacturing for an improved formation of crystalline structures. If we were to harness the power of Machine learning A.I. to help us map this new method and more efficiently path crystalline structures in a fraction of the time, the sky would no longer be the limit.

Keywords

Chemistry, Physics, Experimental Physics, Technology, Space Technology

Research Area

Engineering

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