Search for: All records

PurposeThe purpose of this article is to investigate particular aspects of the STEM job market in the US. In particular, we ask: could the possession of high performance computing (HPC) skills enhance the chances of a person getting a job and/or increase starting salaries for people receiving an undergraduate or graduate degree and entering the technical workforce (rather than academia)? We also estimate the value to the US economy of practical experience offered to US students through training about HPC and the opportunity to use HPC systems funded by the National Science Foundation (NSF) and accessible nationally. MethodsInterviews and surveys of employers of graduates in STEM fields were used to gauge demand for STEM graduates with practical HPC experience and the salary increase that can be associated with the possession of such skills. We used data from the XSEDE project to determine how many undergraduate and graduate students it enabled to acquire practical proficiency with HPC. ResultsPeople with such skills who had completed an undergraduate or graduate degree received an initial median hiring salary of approximately 7%–15% more than those with the same degrees who did not possess such skills. XSEDE added approximately $10 million or more per year to the US economy through the practical educational opportunities it offered. DiscussionPractical hands-on experience provided by the US federal government, as well as many universities and colleges in the US, holds value for students as they enter the workforce. ConclusionPractical training in HPC during the course of undergraduate and graduate programs has the potential to produce positive individual labor market outcomes (i.e., salary boosts, signing bonuses) as well as to help address the shortage of STEM workers in the private sector of the US. 

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs. 

Abstract Alkaline iron (Fe) batteries are attractive due to the high abundance, low cost, and multiple valent states of Fe but show limited columbic efficiency and storage capacity when forming electrochemically inert Fe₃O₄on discharging and parasitic H₂on charging. Herein, sodium silicate is found to promote Fe(OH)₂/FeOOH against Fe(OH)₂/Fe₃O₄conversions. Electrochemical experiments,operandoX‐ray characterization, and atomistic simulations reveal that improved Fe(OH)₂/FeOOH conversion originates from (i) strong interaction between sodium silicate and iron oxide and (ii) silicate‐induced strengthening of hydrogen‐bond networks in electrolytes that inhibits water transport. Furthermore, the silicate additive suppresses hydrogen evolution by impairing energetics of water dissociation and hydroxyl de‐sorption on iron surfaces. This new silicate‐assisted redox chemistry mitigates H₂and Fe₃O₄formation, improving storage capacity (199 mAh g⁻¹in half‐cells) and coulombic efficiency (94 % after 400 full‐cell cycles), paving a path to realizing green battery systems built from earth‐abundant materials.