The cost of information processing in physical systems calls for a trade-off
between performance and energetic expenditure. Here we formulate and study a
computation-dissipation bottleneck in mesoscopic systems used as input-output
devices. Using both real datasets and synthetic tasks, we show how
non-equilibrium leads to enhanced performance. Our framework sheds light on a
crucial compromise between information compression, input-output computation
and dynamic irreversibility induced by non-reciprocal interactions.

在物理系统中信息处理的成本需要在性能和能量消耗之间做出平衡。我们在介观系统中制定和研究了计算 - 耗散瓶颈，并使用实验和人工合成任务展示了非平衡如何促进性能。我们的框架揭示了信息压缩、输入 - 输出计算和非可逆相互作用引起的动态不可逆性之间的重要权衡。

介观尺度机器学习：计算 - 消散瓶颈

Machine learning at the mesoscale: a computation-dissipation bottleneck

Many important tasks in chemistry revolve around molecules during reactions.
This requires predictions far from the equilibrium, while most recent work in
machine learning for molecules has been focused on equilibrium or
near-equilibrium states. In this paper we aim to extend this scope in three
ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more
accurate than the original DimeNet on the QM9 benchmark of equilibrium
molecules. Second, we validate DimeNet++ on highly reactive molecules by
developing the challenging COLL dataset, which contains distorted
configurations of small molecules during collisions. Finally, we investigate
ensembling and mean-variance estimation for uncertainty quantification with the
goal of accelerating the exploration of the vast space of non-equilibrium
structures. Our DimeNet++ implementation as well as the COLL dataset are
available online.

本文提出了 DimeNet++ 模型，比原 DimeNet 模型快 8 倍，并在非平衡体系 QM9 基准测试中准确率提高了 10%。同时，使用自定义的 COLL 数据集验证了 DimeNet++ 在高反应性分子上的效果，研究了集成学习以及方差均值估计来加速探索非平衡态结构。