我们提出了一种方案，可获取原子与分子性质机器学习模型预测相关的低成本且可靠的不确定性估计值。该方案基于重采样技术，通过对同一训练数据进行子采样生成多个模型。不确定性预测的准确性可通过最大似然估计进行基准测试，该方法还可用于校正重采样模型间的相关性，并通过交叉验证流程提升不确定性估计的性能。对于稀疏高斯过程回归（Gaussian Process Regression）模型，这种重采样估计器的计算成本可忽略不计。我们在分子能量预测以及分子晶体中核化学屏蔽的估计任务上验证了这些估计值的可靠性。将其扩展用于估计能量差、力或其他相关预测的不确定性也十分简便。该方法可轻松应用于其他机器学习方案，有助于提升数据驱动预测的可靠性，并推动训练集优化与主动学习策略的发展。
英文摘要：
We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated based on sub-sampling of the same training data. The accuracy of the uncertainty prediction can be benchmarked by maximum likelihood estimation, which can also be used to correct for correlations between resampled models, and to improve the performance of the uncertainty estimation by a cross-validation procedure. In the case of sparse Gaussian Process Regression models, this resampled estimator can be evaluated at negligible cost. We demonstrate the reliability of these estimates for the prediction of molecular energetics, and for the estimation of nuclear chemical shieldings in molecular crystals. Extension to estimate the uncertainty in energy differences, forces, or other correlated predictions is straightforward. This method can be easily applied to other machine learning schemes, and will be beneficial to make data-driven predictions more reliable, and to facilitate training-set optimization and active-learning strategies.
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Technical University of Munich（慕尼黑工业大学）；Aalto University（阿尔托大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute（慕尼黑数据科学研究所）；Ruhr University（鲁尔大学）
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Department of Physics, New York University（纽约大学物理系）
A Fast, Parallelized, GPU-Accelerated Photochemical Model, XODIAC, with Built-in Equilibrium Chemistry and Multiple Chemical Networks for Exoplanetary Atmospheres
National Institute of Science Education and Research（印度科学教育研究所）；Homi Bhabha National Institute（霍米·巴巴国立研究所）；University of Cambridge（剑桥大学）
