DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models

To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and systematically evaluating its mathematical creativity. This paper represents the initial contribution of this initiative. While recent developments in mathematical LLMs have predominantly emphasized reasoning skills, as evidenced by benchmarks on elementary to undergraduate-level mathematical tasks, the creative capabilities of these models have received comparatively little attention, and evaluation datasets remain scarce. 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. We conduct a systematic evaluation of mainstream LLMs' creative problem-solving abilities using this dataset. Experimental results show that even under lenient scoring criteria -- emphasizing core solution components and disregarding minor inaccuracies, such as small logical gaps, incomplete justifications, or redundant explanations -- the best-performing model, O3 Mini, achieves merely 70% accuracy, primarily on basic undergraduate-level constructive tasks. Performance declines sharply on more complex problems, with models failing to provide substantive strategies for open problems. These findings suggest that, although current LLMs display a degree of constructive proficiency on familiar and lower-difficulty problems, such performance is likely attributable to the recombination of memorized patterns rather than authentic creative insight or novel synthesis.

Differentiable Chemical Physics by Geometric Deep Learning for Gradient-based Property Optimization of Mixtures

Chemical mixtures, satisfying multi-objective performance metrics and constraints, enable their use in chemical processes and electrochemical devices. In this work, we develop a differentiable chemical-physics framework for modeling chemical mixtures, DiffMix, where geometric deep learning (GDL) is leveraged to map from molecular species, compositions and environment conditions, to physical coefficients in the mixture physics laws. In particular, we extend mixture thermodynamic and transport laws by creating learnable physical coefficients, where we use graph neural networks as the molecule encoder and enforce component-wise permutation-invariance. We start our model evaluations with thermodynamics of binary mixtures, and further benchmarked multicomponent electrolyte mixtures on their transport properties, in order to test the model generalizability. We show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, we demonstrate the efficient optimization of electrolyte transport properties, built on the gradient obtained using DiffMix auto-differentiation. Our simulation runs are then backed up by the data generated by a robotic experimentation setup, Clio. By combining mixture physics and GDL, DiffMix expands the predictive modeling methods for chemical mixtures and provides low-cost optimization approaches in large chemical spaces.

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. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.