Showing papers by "Tsinghua University published in 2019"

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

Heterogeneous Graph Attention Network

Abstract: Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Selective Kernel Networks

Abstract: In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet.

The Roadmap to 6G: AI Empowered Wireless Networks

Abstract: The recent upsurge of diversified mobile applications, especially those supported by AI, is spurring heated discussions on the future evolution of wireless communications. While 5G is being deployed around the world, efforts from industry and academia have started to look beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented transformation that will make it substantially different from the previous generations of wireless cellular systems. In particular, 6G will go beyond mobile Internet and will be required to support ubiquitous AI services from the core to the end devices of the network. Meanwhile, AI will play a critical role in designing and optimizing 6G architectures, protocols, and operations. In this article, we discuss potential technologies for 6G to enable mobile AI applications, as well as AI-enabled methodologies for 6G network design and optimization. Key trends in the evolution to 6G will also be discussed.

Second-Order Attention Network for Single Image Super-Resolution

Abstract: Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and obtained remarkable performance. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper architecture design, neglecting to explore the feature correlations of intermediate layers, hence hindering the representational power of CNNs. To address this issue, in this paper, we propose a second-order attention network (SAN) for more powerful feature expression and feature correlation learning. Specifically, a novel train- able second-order channel attention (SOCA) module is developed to adaptively rescale the channel-wise features by using second-order feature statistics for more discriminative representations. Furthermore, we present a non-locally enhanced residual group (NLRG) structure, which not only incorporates non-local operations to capture long-distance spatial contextual information, but also contains repeated local-source residual attention groups (LSRAG) to learn increasingly abstract feature representations. Experimental results demonstrate the superiority of our SAN network over state-of-the-art SISR methods in terms of both quantitative metrics and visual quality.

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

Abstract: From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

ERNIE: Enhanced Language Representation with Informative Entities

Abstract: Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The code and datasets will be available in the future.

Optical vortices 30 years on: OAM manipulation from topological charge to multiple singularities

Abstract: Thirty years ago, Coullet et al. proposed that a special optical field exists in laser cavities bearing some analogy with the superfluid vortex. Since then, optical vortices have been widely studied, inspired by the hydrodynamics sharing similar mathematics. Akin to a fluid vortex with a central flow singularity, an optical vortex beam has a phase singularity with a certain topological charge, giving rise to a hollow intensity distribution. Such a beam with helical phase fronts and orbital angular momentum reveals a subtle connection between macroscopic physical optics and microscopic quantum optics. These amazing properties provide a new understanding of a wide range of optical and physical phenomena, including twisting photons, spin-orbital interactions, Bose-Einstein condensates, etc., while the associated technologies for manipulating optical vortices have become increasingly tunable and flexible. Hitherto, owing to these salient properties and optical manipulation technologies, tunable vortex beams have engendered tremendous advanced applications such as optical tweezers, high-order quantum entanglement, and nonlinear optics. This article reviews the recent progress in tunable vortex technologies along with their advanced applications.

A Survey on Network Embedding

Abstract: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.

Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China

Abstract: Observations of surface ozone available from ∼1,000 sites across China for the past 5 years (2013–2017) show severe summertime pollution and regionally variable trends. We resolve the effect of meteorological variability on the ozone trends by using a multiple linear regression model. The residual of this regression shows increasing ozone trends of 1–3 ppbv a−1 in megacity clusters of eastern China that we attribute to changes in anthropogenic emissions. By contrast, ozone decreased in some areas of southern China. Anthropogenic NOx emissions in China are estimated to have decreased by 21% during 2013–2017, whereas volatile organic compounds (VOCs) emissions changed little. Decreasing NOx would increase ozone under the VOC-limited conditions thought to prevail in urban China while decreasing ozone under rural NOx-limited conditions. However, simulations with the Goddard Earth Observing System Chemical Transport Model (GEOS-Chem) indicate that a more important factor for ozone trends in the North China Plain is the ∼40% decrease of fine particulate matter (PM2.5) over the 2013–2017 period, slowing down the aerosol sink of hydroperoxy (HO2) radicals and thus stimulating ozone production.

Advanced Carbon for Flexible and Wearable Electronics.

Abstract: Flexible and wearable electronics are attracting wide attention due to their potential applications in wearable human health monitoring and care systems. Carbon materials have combined superiorities such as good electrical conductivity, intrinsic and structural flexibility, light weight, high chemical and thermal stability, ease of chemical functionalization, as well as potential mass production, enabling them to be promising candidate materials for flexible and wearable electronics. Consequently, great efforts are devoted to the controlled fabrication of carbon materials with rationally designed structures for applications in next-generation electronics. Herein, the latest advances in the rational design and controlled fabrication of carbon materials toward applications in flexible and wearable electronics are reviewed. Various carbon materials (carbon nanotubes, graphene, natural-biomaterial-derived carbon, etc.) with controlled micro/nanostructures and designed macroscopic morphologies for high-performance flexible electronics are introduced. The fabrication strategies, working mechanism, performance, and applications of carbon-based flexible devices are reviewed and discussed, including strain/pressure sensors, temperature/humidity sensors, electrochemical sensors, flexible conductive electrodes/wires, and flexible power devices. Furthermore, the integration of multiple devices toward multifunctional wearable systems is briefly reviewed. Finally, the existing challenges and future opportunities in this field are summarized.

Tuning element distribution, structure and properties by composition in high-entropy alloys.

Abstract: High-entropy alloys are a class of materials that contain five or more elements in near-equiatomic proportions1,2. Their unconventional compositions and chemical structures hold promise for achieving unprecedented combinations of mechanical properties3–8. Rational design of such alloys hinges on an understanding of the composition–structure–property relationships in a near-infinite compositional space9,10. Here we use atomic-resolution chemical mapping to reveal the element distribution of the widely studied face-centred cubic CrMnFeCoNi Cantor alloy2 and of a new face-centred cubic alloy, CrFeCoNiPd. In the Cantor alloy, the distribution of the five constituent elements is relatively random and uniform. By contrast, in the CrFeCoNiPd alloy, in which the palladium atoms have a markedly different atomic size and electronegativity from the other elements, the homogeneity decreases considerably; all five elements tend to show greater aggregation, with a wavelength of incipient concentration waves11,12 as small as 1 to 3 nanometres. The resulting nanoscale alternating tensile and compressive strain fields lead to considerable resistance to dislocation glide. In situ transmission electron microscopy during straining experiments reveals massive dislocation cross-slip from the early stage of plastic deformation, resulting in strong dislocation interactions between multiple slip systems. These deformation mechanisms in the CrFeCoNiPd alloy, which differ markedly from those in the Cantor alloy and other face-centred cubic high-entropy alloys, are promoted by pronounced fluctuations in composition and an increase in stacking-fault energy, leading to higher yield strength without compromising strain hardening and tensile ductility. Mapping atomic-scale element distributions opens opportunities for understanding chemical structures and thus providing a basis for tuning composition and atomic configurations to obtain outstanding mechanical properties. In high-entropy alloys, atomic-resolution chemical mapping shows that swapping some of the atoms for larger, more electronegative elements results in atomic-scale modulations that produce higher yield strength, excellent strain hardening and ductility.

Properties of the Binary Neutron Star Merger GW170817

Abstract: On August 17, 2017, the Advanced LIGO and Advanced Virgo gravitational-wave detectors observed a low-mass compact binary inspiral. The initial sky localization of the source of the gravitational-wave signal, GW170817, allowed electromagnetic observatories to identify NGC 4993 as the host galaxy. In this work, we improve initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data. We extend the range of gravitational-wave frequencies considered down to 23 Hz, compared to 30 Hz in the initial analysis. We also compare results inferred using several signal models, which are more accurate and incorporate additional physical effects as compared to the initial analysis. We improve the localization of the gravitational-wave source to a 90% credible region of 16 deg2. We find tighter constraints on the masses, spins, and tidal parameters, and continue to find no evidence for nonzero component spins. The component masses are inferred to lie between 1.00 and 1.89 M when allowing for large component spins, and to lie between 1.16 and 1.60 M (with a total mass 2.73-0.01+0.04 M) when the spins are restricted to be within the range observed in Galactic binary neutron stars. Using a precessing model and allowing for large component spins, we constrain the dimensionless spins of the components to be less than 0.50 for the primary and 0.61 for the secondary. Under minimal assumptions about the nature of the compact objects, our constraints for the tidal deformability parameter Λ are (0,630) when we allow for large component spins, and 300-230+420 (using a 90% highest posterior density interval) when restricting the magnitude of the component spins, ruling out several equation-of-state models at the 90% credible level. Finally, with LIGO and GEO600 data, we use a Bayesian analysis to place upper limits on the amplitude and spectral energy density of a possible postmerger signal.

RepPoints: Point Set Representation for Object Detection

Abstract: Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present \textbf{RepPoints} (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 $AP_{50}$ on the COCO test-dev detection benchmark, using ResNet-101 model. Code is available at \href{https://github.com/microsoft/RepPoints}{\color{cyan}{https://github.com/microsoft/RepPoints}}.

Lithium-ion battery fast charging: A review

Abstract: In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

A review on the key issues of the lithium ion battery degradation among the whole life cycle

Abstract: The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Abstract: Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Engineering the electronic structure of single atom Ru sites via compressive strain boosts acidic water oxidation electrocatalysis

Abstract: Single-atom precious metal catalysts hold the promise of perfect atom utilization, yet control of their activity and stability remains challenging. Here we show that engineering the electronic structure of atomically dispersed Ru1 on metal supports via compressive strain boosts the kinetically sluggish electrocatalytic oxygen evolution reaction (OER), and mitigates the degradation of Ru-based electrocatalysts in an acidic electrolyte. We construct a series of alloy-supported Ru1 using different PtCu alloys through sequential acid etching and electrochemical leaching, and find a volcano relation between OER activity and the lattice constant of the PtCu alloys. Our best catalyst, Ru1–Pt3Cu, delivers 90 mV lower overpotential to reach a current density of 10 mA cm−2, and an order of magnitude longer lifetime over that of commercial RuO2. Density functional theory investigations reveal that the compressive strain of the Ptskin shell engineers the electronic structure of the Ru1, allowing optimized binding of oxygen species and better resistance to over-oxidation and dissolution. While Ru-based electrocatalysts are among the most active for acidic water oxidation, they suffer from severe deactivation. Now, Yuen Wu, Wei-Xue Li and co-workers report a core–shell Ru1–Pt3Cu catalyst with surface-dispersed Ru atoms for a highly active and stable oxygen evolution reaction in acid electrolyte.

Anti-tumour immunity controlled through mRNA m 6 A methylation and YTHDF1 in dendritic cells

Abstract: There is growing evidence that tumour neoantigens have important roles in generating spontaneous antitumour immune responses and predicting clinical responses to immunotherapies1,2. Despite the presence of numerous neoantigens in patients, complete tumour elimination is rare, owing to failures in mounting a sufficient and lasting antitumour immune response3,4. Here we show that durable neoantigen-specific immunity is regulated by mRNA N6-methyadenosine (m6A) methylation through the m6A-binding protein YTHDF15. In contrast to wild-type mice, Ythdf1-deficient mice show an elevated antigen-specific CD8+ T cell antitumour response. Loss of YTHDF1 in classical dendritic cells enhanced the cross-presentation of tumour antigens and the cross-priming of CD8+ T cells in vivo. Mechanistically, transcripts encoding lysosomal proteases are marked by m6A and recognized by YTHDF1. Binding of YTHDF1 to these transcripts increases the translation of lysosomal cathepsins in dendritic cells, and inhibition of cathepsins markedly enhances cross-presentation of wild-type dendritic cells. Furthermore, the therapeutic efficacy of PD-L1 checkpoint blockade is enhanced in Ythdf1-/- mice, implicating YTHDF1 as a potential therapeutic target in anticancer immunotherapy.

Tuning the Coordination Environment in Single-Atom Catalysts to Achieve Highly Efficient Oxygen Reduction Reactions.

Abstract: Designing atomically dispersed metal catalysts for oxygen reduction reaction (ORR) is a promising approach to achieve efficient energy conversion. Herein, we develop a template-assisted method to synthesize a series of single metal atoms anchored on porous N,S-codoped carbon (NSC) matrix as highly efficient ORR catalysts to investigate the correlation between the structure and their catalytic performance. The structure analysis indicates that an identical synthesis method results in distinguished structural differences between Fe-centered single-atom catalyst (Fe-SAs/NSC) and Co-centered/Ni-centered single-atom catalysts (Co-SAs/NSC and Ni-SAs/NSC) because of the different trends of each metal ion in forming a complex with the N,S-containing precursor during the initial synthesis process. The Fe-SAs/NSC mainly consists of a well-dispersed FeN4S2 center site where S atoms form bonds with the N atoms. The S atoms in Co-SAs/NSC and Ni-SAs/NSC, on the other hand, form metal-S bonds, resulting in CoN3S1 and NiN3S1 center sites. Density functional theory (DFT) reveals that the FeN4S2 center site is more active than the CoN3S1 and NiN3S1 sites, due to the higher charge density, lower energy barriers of the intermediates, and products involved. The experimental results indicate that all three single-atom catalysts could contribute high ORR electrochemical performances, while Fe-SAs/NSC exhibits the highest of all, which is even better than commercial Pt/C. Furthermore, Fe-SAs/NSC also displays high methanol tolerance as compared to commercial Pt/C and high stability up to 5000 cycles. This work provides insights into the rational design of the definitive structure of single-atom catalysts with tunable electrocatalytic activities for efficient energy conversion.

Ultrahigh–energy density lead-free dielectric films via polymorphic nanodomain design

Abstract: Dielectric capacitors with ultrahigh power densities are fundamental energy storage components in electrical and electronic systems. However, a long-standing challenge is improving their energy densities. We report dielectrics with ultrahigh energy densities designed with polymorphic nanodomains. Guided by phase-field simulations, we conceived and synthesized lead-free BiFeO3-BaTiO3-SrTiO3 solid-solution films to realize the coexistence of rhombohedral and tetragonal nanodomains embedded in a cubic matrix. We obtained minimized hysteresis while maintaining high polarization and achieved a high energy density of 112 joules per cubic centimeter with a high energy efficiency of ~80%. This approach should be generalizable for designing high-performance dielectrics and other functional materials that benefit from nanoscale domain structure manipulation.