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Found 5 papers, 1 papers with code
Rethinking the effects of data contamination in Code Intelligence
Our findings challenge the conventional belief that contamination inevitably leads to performance overestimation, providing new insights into the evaluation and deployment of code intelligence models.
DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
To address this gap, we propose an evaluation criteria for mathematical creativity and introduce DeepMath-Creative, a novel, high-quality benchmark comprising constructive problems across algebra, geometry, analysis, and other domains.
Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces.
Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte.
AutoMat: Accelerated Computational Electrochemical systems Discovery
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation.
