Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning

DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values

Human Migration through Bottlenecks from Southeast Asia into East Asia during Last Glacial Maximum Revealed by Y Chromosomes

Molecular anthropological studies of the populations in and around East Asia have resulted in the discovery that most of the Y-chromosome lineages of East Asians came from Southeast Asia. However, very few Southeast Asian populations had been investigated, and therefore, little was known about the purported migrations from Southeast Asia into East Asia and their roles in shaping the genetic structure of East Asian populations. Here, we present the Y-chromosome data from 1,652 individuals belonging to 47 Mon-Khmer (MK) and Hmong-Mien (HM) speaking populations that are distributed primarily across Southeast Asia and extend into East Asia. Haplogroup O3a3b-M7, which appears mainly in MK and HM, indicates a strong tie between the two groups. The short tandem repeat network of O3a3b-M7 displayed a hierarchical expansion structure (annual ring shape), with MK haplotypes being located at the original point, and the HM and the Tibeto-Burman haplotypes distributed further away from core of the network. Moreover, the East Asian dominant haplogroup O3a3c1-M117 shows a network structure similar to that of O3a3b-M7. These patterns indicate an early unidirectional diffusion from Southeast Asia into East Asia, which might have resulted from the genetic drift of East Asian ancestors carrying these two haplogroups through many small bottle-necks formed by the complicated landscape between Southeast Asia and East Asia. The ages of O3a3b-M7 and O3a3c1-M117 were estimated to be approximately 19 thousand years, followed by the emergence of the ancestors of HM lineages out of MK and the unidirectional northward migrations into East Asia

Upper ocean biogeochemistry of the oligotrophic North Pacific Subtropical Gyre : from nutrient sources to carbon export

Subtropical gyres cover 26–29% of the world’s surface ocean and are conventionally regarded as ocean deserts due to their permanent stratification, depleted surface nutrients, and low biological productivity. Despite tremendous advances over the past three decades, particularly through the Hawaii Ocean Time-series and the Bermuda Atlantic Time-series Study, which have revolutionized our understanding of the biogeochemistry in oligotrophic marine ecosystems, the gyres remain understudied. We review current understanding of upper ocean biogeochemistry in the North Pacific Subtropical Gyre, considering other subtropical gyres for comparison. We focus our synthesis on spatial variability, which shows larger than expected dynamic ranges of properties such as nutrient concentrations, rates of N2 fixation, and biological production. This review provides new insights into how nutrient sources drive community structure and export in upper subtropical gyres. We examine the euphotic zone in subtropical gyres as a two-layered vertically structured system: a nutrient-depleted layer above the top of the nutricline in the well-lit upper ocean and a nutrient-replete layer below in the dimly lit waters. These layers vary in nutrient supply and stoichiometries and physical forcing, promoting differences in community structure and food webs, with direct impacts on the magnitude and composition of export production. We evaluate long-term variations in key biogeochemical parameters in both of these euphotic zone layers. Finally, we identify major knowledge gaps and research challenges in these vast and unique systems that offer opportunities for future studies

Solving Set Cover and Dominating Set via Maximum Satisfiability

The Set Covering Problem (SCP) and Dominating Set Problem (DSP) are NP-hard and have many real world applications. SCP and DSP can be encoded into Maximum Satisfiability (MaxSAT) naturally and the resulting instances share a special structure. In this paper, we develop an efficient local search solver for MaxSAT instances of this kind. Our algorithm contains three phrase: construction, local search and recovery. In construction phrase, we simplify the instance by three reduction rules and construct an initial solution by a greedy heuristic. The initial solution is improved during the local search phrase, which exploits the feature of such instances in the scoring function and the variable selection heuristic. Finally, the corresponding solution of original instance is recovered in the recovery phrase. Experiment results on a broad range of large scale instances of SCP and DSP show that our algorithm significantly outperforms state of the art solvers for SCP, DSP and MaxSAT

Meta-Reinforcement Learning via Exploratory Task Clustering

Meta-reinforcement learning (meta-RL) aims to quickly solve new RL tasks by leveraging knowledge from prior tasks. Previous studies often assume a single-mode homogeneous task distribution, ignoring possible structured heterogeneity among tasks. Such an oversight can hamper effective exploration and adaptation, especially with limited samples. In this work, we harness the structured heterogeneity among tasks via clustering to improve meta-RL, which facilitates knowledge sharing at the cluster level. To facilitate exploration, we also develop a dedicated cluster-level exploratory policy to discover task clusters via divide-and-conquer. The knowledge from the discovered clusters helps to narrow the search space of task-specific policy learning, leading to more sample-efficient policy adaptation. We evaluate the proposed method on environments with parametric clusters (e.g., rewards and state dynamics in the MuJoCo suite) and non-parametric clusters (e.g., control skills in the Meta-World suite). The results demonstrate strong advantages of our solution against a set of representative meta-RL methods