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New protein bridges chemical divide for seamless bioelectronics devices

CENIC Recognizes CSU San Bernardino s High Performance Computing Program for Improving CSU Access to National Research Platform Resources

The High Performance Computing Program (HPCP) at CSU San Bernardino has been selected to receive the CENIC 2024 Innovations in Networking Award for Research Applications in recognition of its work supporting faculty who wish to avail themselves of.

CENIC Recognizes CSU San Bernardino s High Performance Computing Program for Improving CSU Access to National Research Platform Resources

CENIC Recognizes CSU San Bernardino s High Performance Computing Program for Improving CSU Access to National Research Platform Resources
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Patterns Lead the Way to Far-from-Equilibrium Materials

Patterns Lead the Way to Far-from-Equilibrium Materials
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How Machine Learning can speed up material design, testing and manufacturing, ET Auto

Machine Learning: The conventional alloy design process is multistage and thus multivariable, leading to tremendous effort. Further, alloy design is also about meeting a well-defined target of properties like yield strength, ultimate tensile strength, fracture toughness, fatigue life etc. ML method can eliminate long hit-and-trial-based expensive methodology of material design to smart discovery and deployment. A tangible effort is going on across the globe to curate, capture, generate and manage data and to come up with algorithms to predict the next processes and materials.

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