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Machine learning interatomic potentials like MACE (NeurIPS 2022) have reached a point where they rival density functional theory in accuracy while running orders of magnitude faster. But using them still requires Python scripting, command-line fluency, and environment setup that shuts out a large number of researchers, especially those with accessibility needs, those in under-resourced labs, or students encountering computational chemistry for the first time.
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中信证券表示，AI算力需求爆发推动光通信产业升级，海外云厂商及台积电资本开支持续扩张，印证AI基础设施需求强劲，带来旺盛的高速率光模块需求。尽管高速光芯片等物料短期供给存在缺口，但上游厂商积极扩产，叠加硅光方案渗透率提升，供应链瓶颈有望缓解。
Compressed transfers: Uses zlib compression for significantly faster flashing