This paper summarizes the vast body of literature (over 200 documents) related to
vitrification of the low-activity waste (LAW) fraction of the Hanford tank wastes. Details are …

The widespread adoption and success of Machine Learning (ML) technologies depend on
thorough testing of the resilience and robustness to adversarial attacks. The testing should …

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal
control direction for multi-energy management systems. It only requires the environment …

A review of current trends in scientific computing reveals a broad shift to open-source and
higher-level programming languages such as Python and growing career opportunities over …

Thermophysical properties of organic compounds are used in countless scientific,
engineering, and industrial settings in developing theories, designing new systems and …

Glass is a versatile material with a remarkable history and many practical applications. It
plays a critical role in our everyday lives, the advancement of science, and the development …

Abstract The United States Department of Energy is responsible for managing the legacy
nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately …

A significant effort was invested in the past to develop and refine mathematical models that
relate the composition of nuclear waste glasses with their properties, such as viscosity …

This work presents a novel adaptable framework for multi-objective optimization (MOO) in
metal additive manufacturing (AM). The framework offers significant advantages by …

Informatics-driven approaches, such as machine learning and sequential experimental
design, have shown the potential to drastically impact next-generation materials discovery …