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Open access Sep 2026

Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD

Simulating chemical reactivity remains a central challenge in molecular dynamics, historically constrained by the trade-off between ab initio accuracy and classical efficiency. We present QuantaMind, a machine learning force field (MLFF) framework that attains density functional theory (DFT)–level accuracy, enabling fu...

Song Xia, De-Qiang Zhang, Xue-Dong Shang et al. · 0 citations
Book Open access Aug 2026

The Forgetting-Learning Trade-off: Making Reinforcement Learning Work for Protein Language Models

Diagnostics reveal that RL on PLMs is governed by two reward properties: verifiability, whether the reward is a fixed environment or a learned surrogate vulnerable to distribution shift, and coverage, the fraction of sequence space giving an informative gradient.

Hanqun Cao, Hongrui Zhang, Junde Xu et al. · 0 citations

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