About

Dr. Wenjie Zhou is a materials scientist with a diverse training background that includes molecular and colloidal synthesis from Northwestern University, architected and topological materials from Caltech, and computation. His research focuses on understanding materials composed of many discrete parts, such as molecules, links, and grains, which interact through contact, entanglement, and interlocking rather than forming a continuous solid. He develops a unifying perspective that treats architecture and connectivity as primary design variables, alongside composition, to guide the design of materials with lifelike behaviors that are responsive, multifunctional, and efficient. At the University of Illinois Urbana-Champaign, Zhou leads the Intelligent Matter Lab within the Materials Science & Engineering department. His group integrates experiment, modeling, and AI to translate fundamental principles from mathematics and physics into practical material behaviors and demonstrators. His research aims to create intelligent matter—materials whose functions arise from the arrangement and interaction of their parts—enabling advancements in resilient aerospace components, durable energy interfaces, adaptable robotic elements, and other technologies where reliability and efficiency are critical.

Research topics

• Environmental science
• Computer Science
• Agronomy
• Geography
• Machine Learning
• Ecology
• Agroforestry
• Soil science
• Mathematics
• Remote sensing
• Engineering
• Physics
• Materials science
• Meteorology
• Chemistry
• Atmospheric sciences
• Agricultural engineering
• Geology
• Biology
• Environmental resource management

Selected publications

• Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems

  Hydrology and earth system sciences · 2025-11-18 · 2 citations

  articleOpen accessCorresponding

  Abstract. Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

  PublisherOA PDFDOI

• A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

  Journal of Advances in Modeling Earth Systems · 2025-04-01 · 1 citations

  articleOpen access

  Abstract Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply‐demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi‐arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

  PublisherOA PDFDOI

• OsERF7 Negatively Regulates Resistance to Sheath Blight Disease by Inhibiting Phytoalexin Biosynthesis

  Rice Science · 2025-03-08 · 7 citations

  articleOpen access

  Ethylene response factors (ERFs) are plant transcription factors that play a pivotal role in disease resistance throught the ethylene signaling pathway. However, whether and how ERFs regulate resistance to sheath blight (ShB), caused by Rhizoctonia solani in rice, remains largely unknown. Here, we demonstrated that OsERF7 negatively regulates rice resistance to ShB by inhibiting phytoalexin biosynthesis. Overexpression of OsERF7 ( OsERF7OE ) significantly decreases ShB resistance, whereas knockout of OsERF7 ( oserf7 ) enhanced it. Mechanistically, antioxidative enzyme activities are significantly reduced in OsERF7OE plants, but increased in oserf7 plants. Furthermore, transcriptome analysis revealed that oserf7 plants exhibited significant upregulation of pathogenesis-related (PR) and phytoalexin biosynthesis genes upon R. solani infection. Consistently, RNA levels of phytoalexin biosynthesis genes, including OsKSL7 , OsKSL8 , OsKOL5 , and OsCPS4 , were significantly elevated in oserf7 plants, but reduced in OsERF7OE plants in response to R. solani infection. Electrophoretic mobility shift assay and dual-luciferase (LUC) reporter assays further confirmed that OsERF7 directly binds to the promoters of OsKSL8 , OsKOL5 , and OsCPS4 , thereby repressing their expression. In summary, our study reveals that OsERF7 negatively regulates rice resistance to sheath blight primarily by inhibiting phytoalexin biosynthesis.

  PublisherDOI

• Non‐Continuous Flooding Outperforms Continuous Flooding in Rice Fields Net Carbon Sequestration Despite Losses in Soil Organic Carbon Sequestration

  Global Change Biology · 2025-06-01 · 11 citations

  review

  ABSTRACT Rice feeds more than 50% of the global population with significant greenhouse gas (GHG) emissions. Non‐continuous flooding (NCF) has been recognized as an effective practice for stabilizing rice yields, conserving water, and reducing GHG emissions from rice fields. However, the impacts of NCF on net carbon sequestration (NCS, defined as the total ecosystem GHG equivalent) in rice fields, including CH 4 emissions, N 2 O emissions, photosynthetic carbon sequestration linked to crop yield, and soil organic carbon (SOC) sequestration, have rarely been quantified comprehensively. This limitation hinders a complete understanding of the overall processes through which NCF affects NCS. This study conducted a meta‐analysis of 1075 data pairs from 72 studies worldwide to quantify the effects of NCF on GHG equivalent components and its overall NCS benefits. Results showed that compared to continuous flooding (CF), NCF significantly increased the average NCS per growing season by 4615 kg CO 2 ‐eq·ha −1 (95% CIs: 468 to 8761, p = 0.031). Specifically, NCF significantly reduced CH 4 emissions by 45.72% and significantly increased N 2 O emissions by 35.77%, with an insignificant increase of 1.93% and 3.16% in CO 2 emissions and yield, respectively. The ΔSOC (changes of SOC concentration before and after the growing season) significantly decreased with the mean difference effect size of −0.36 (95% CIs: −0.70 to −0.02), indicating smaller SOC changes for NCF. Meta‐regression and random forest importance analyses were used to explore the effects of climatic and soil properties and management practices on GHG equivalent components. Implementing controlled irrigation with appropriate limitation of total water input could achieve a win‐win situation of enhancing rice yield while mitigating GWP and Y‐GWP. This study further quantified the effects of NCF on all components of GHG equivalent and the NCS benefits in rice fields, providing guidance for irrigation management practices to achieve dual carbon goals.

  PublisherDOI

• Corrigendum to “An upscaling approach for estimating field-level irrigation water use through the Budyko framework” [J. Hydrol. 661 (2025) 133785]

  Journal of Hydrology · 2025-09-26

  article
  PublisherDOI
• RETRACTED

  RETRACTED: Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest

  Agricultural and Forest Meteorology · 2025-08-09 · 2 citations

  article

  This article has been retracted: please see Elsevier policy on article withdrawal ( https://www.elsevier.com/about/policies-and-standards/article-withdrawal ). This article is retracted at the request of the Authors. Following publication of this article, the authors found significant errors in model selection and data processing that affected the results and conclusions. After correcting the errors, key findings remain, but some conclusions are no longer reliable, and most figures need updating, which would be too extensive to address in a corrigendum. The authors apologize deeply for their oversight and any inconvenience brought to the readers and the journal, and intend to submit a thoroughly revised manuscript with extended analysis.

  PublisherDOI

• Comparing continuous-corn and soybean-corn rotation cropping systems in the U.S. central Midwest: Trade-offs among crop yield, nutrient losses, and change in soil organic carbon

  Agriculture Ecosystems & Environment · 2025-06-07 · 8 citations

  article
  PublisherDOI

• Estimation of Seasonal Net Carbon Sequestration Under Noncontinuous Flooding in Rice Fields

  Global Change Biology · 2025-09-01

  articleOpen access

  Non-continuous flooding (NCF) in rice was recently reported to improve the field-scale, seasonal carbon balance. In this response article, we clarify our system boundaries (seasonal, field-scale), address the role of yield carbon and microbial indicators, and add sensitivity checks with expanded data. Across these checks, NCF consistently reduces methane without penalizing yield, supporting our original conclusions. Wang and Zou (2025) have raised concerns regarding our recent contribution to Global Change Biology (Hou et al. 2025). In this work, we conducted a meta-analysis of 1075 data pairs from 72 studies worldwide to quantify the effects of noncontinuous flooding (NCF) on greenhouse gas (GHG) equivalent components and its overall net carbon sequestration (NCS) benefits in rice fields. In particular, they note that our conclusion warrants further scrutiny in terms of methodological assumptions and data representativeness, arguing this may lead to a systematic overestimation of the carbon sequestration potential of NCF in rice systems. In response, we address below the key concerns raised in their letter, focusing on three primary aspects: (1) the calculation framework of NCS, (2) the uncertainties of SOC sequestration estimation, and (3) dataset representativeness. We acknowledge the complexity and ongoing debate regarding the definition and accounting framework of NCS in agricultural systems. In our study, NCS refers to the net carbon balance of the rice cropping system during the growing season, encompassing both carbon uptake and release within system boundaries. Under this definition, PCS, calculated from crop yield, is treated as a component of seasonal carbon uptake. While we agree that PCS does not meet the IPCC's criteria for long-term carbon retention (Ogle et al. 2019), it reflects short-term carbon fixation and system productivity, providing a useful basis for comparing the carbon performance of continuous flooding (CF) and NCF irrigation regimes. Our system boundary excludes downstream emissions resulting from grain consumption, which occur outside the field-scale assessment. Although PCS does not imply long-term storage, it represents a temporally meaningful field-scale carbon sink during the cropping period. This treatment aligns with existing studies emphasizing the role of short-to medium-term carbon fluxes in field-scale assessments (Smith et al. 2020). In addition, we clearly separated PCS from ΔSOC (changes of SOC concentration before and after the growing season) to avoid double counting (see figure 7a in Hou et al. (2025)). PCS accounts only for carbon in harvested rice grain, whereas ΔSOC captures changes in soil carbon associated with nonharvested biomass. As shown in figure 7a in Hou et al. (2025), these two are estimated independently. We acknowledge that there are uncertainties associated with short-term SOC sequestration assessments. While long-term SOC monitoring remains the gold standard (Smith et al. 2020), single-season comparisons are widely used in meta-analysis due to field data limitations (Liu et al. 2024). In our dataset, positive SOC sequestration under CF was observed in systems that incorporated straw, organic fertilizer, and/or biochar application (see supporting information in Hou et al. (2025)), all of which enhance single-season SOC sequestration. Additionally, the reported mean SOC sequestration under CF (~2800 kg CO2-eq ha−1) falls within the range of recent field studies under comparable conditions (table 2 in Liu et al. (2024)). Regarding microbial biomass carbon (MBC), we agree that it is not a direct proxy for microbial activity or SOC mineralization. In our study, MBC was interpreted cautiously—as a reflection of shifts in microbial carbon pools under altered moisture and redox conditions, not as evidence of SOC sequestration loss. The co-occurrence of increased MBC and decreased dissolved organic carbon (DOC) under NCF (figure S7 in Hou et al. (2025)) may suggest enhanced microbial processing of labile substrates, consistent with mechanisms linked to drying-rewetting cycles (Blagodatskaya and Kuzyakov 2013). While we agree that process-based measures such as respiration or enzyme activity would offer stronger support (Blagodatskaya and Kuzyakov 2013), such data were rarely available in the included studies. As discussed in section 4.2 of Hou et al. (2025), we did not treat MBC as a conclusive indicator but rather as part of a broader pattern supporting our interpretation of SOC dynamics. We acknowledge the limitations posed by sample size and dataset completeness. Our study focused on literature published in the recent 5-year period (2019–2023) to guarantee comparability of irrigation practices, as standardized NCF techniques (e.g., alternating wet and dry, AWD, devices, controlled irrigation, CI) have only recently seen broader adoption (Bo et al. 2022). Earlier studies often lacked precise water management descriptions or consistent measurement protocols, which may affect data quality and classification accuracy. These constraints were noted in section 4.4 of Hou et al. (2025). The robustness of the conclusions of Hou et al. (2025) was demonstrated after extensive sensitivity analyses, publication bias tests, and comparisons with previous studies (Bo et al. 2022; Jiang et al. 2019) (Table 1). To address concerns about the temporal coverage, we expanded our analysis to include pre-2019 studies from Bo et al. (2022), yielding a broader dataset for sensitivity checks (Figure 1). While earlier data slightly changed the distribution of the effects of NCF on CH4 emissions and yield, the overall pattern remained consistent: NCF significantly reduces CH4 emissions and insignificantly enhances yield. The larger mitigation effects based on our dataset may also reflect improved irrigation techniques and cultivar advancement in recent years. We appreciate the additional effort by Wang and Zou (2025) in reconciling the data from the Nikolaisen et al. (2023). However, after re-screening 2301 records from Nikolaisen et al. (2023) dataset using our criteria, we found that only 247 observation pairs were eligible, among which 62 overlapped with our post-2019 dataset. This overlap highlights the risk of double-counting when combining datasets without deduplication, potentially inflating sample size and biasing effect estimates. It further emphasizes the importance of carefully tracking study-level duplication in meta-analytic integration. Moreover, the irrigation regimes in the Nikolaisen et al.'s (2023) dataset lacked standardization, complicating the classification of irrigation practices. In contrast, our meta-analysis applied consistent screening at the study level to ensure data traceability and comparability. We emphasize that collecting all the components of NCS remains challenging (section 4.4). Although complete NCS observations remain scarce (n = 23), we conducted component-level meta-analysis for yield, SOC, and non-CO2 GHGs. Importantly, CH4 reduction alone (−4314 kg CO2-eq ha−1) exceeds the combined increases in N2O emissions (+137 kg) and SOC loss (−2856 kg), supporting a robust NCS benefit of NCF. We appreciate the constructive efforts by Jinyang Wang and Jianwen Zou in extending the dataset and for this interesting debate. Yu Hou: formal analysis, methodology, visualization, writing – review and editing. Jingwen Zhang: conceptualization, funding acquisition, methodology, project administration, resources, writing – review and editing. Junjie Guo: conceptualization, methodology, writing – review and editing. Kairong Lin: conceptualization, writing – review and editing. Wang Zhou: conceptualization, writing – review and editing. Ziqi Qin: conceptualization, writing – review and editing. Qingsong Zhu: data curation. Qinxia He: data curation. We acknowledge the support from the National Key R&D Program of China (2023YFC3209400, 2023YFC3209401-03), the National Natural Science Foundation of China (52479034, 52309014), the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23hytd011), and the Guangdong Basic and Applied Basic Research Foundation (2024A1515010968). The authors declare no conflicts of interest. This article is a Response to a Letter to the Editor by jingyang Wang and Jianwen Zou https://doi.org/10.1111/gcb.70465 regarding Hou et al., https://doi.org/10.1111/gcb.70283. The data used to create Figure 1 in this response come from two publicly archived sources: Hou et al. (2025) dataset [https://doi.org/10.6084/m9.figshare.29117669] and Bo et al. (2022) dataset [https://doi.org/10.6084/m9.figshare.19164893].

  PublisherOA PDFDOI

• A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States

  Agricultural and Forest Meteorology · 2025-01-20 · 3 citations

  articleOpen access

  • Developed a framework combining process-based modeling with model-data fusion (MDF). • The MDF uses deep learning to integrate satellite and survey data for model calibration. • Our framework accurately quantifies US cotton carbon fluxes and lint yield. • High vapor pressure deficit limits productivity, especially for rainfed cotton. Cotton ( Gossypium hirsutum L.) cultivation contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is thus crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, ecosys , and a deep learning-based Model-Data Fusion (MDF) approach to quantify the magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the performance of the process-based model in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) values at production-scale farm sites. We then apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the ecosys model to generate the holistic carbon budget of cotton cropland at the county level across the U.S. from 2008 to 2019. Validation at the three EC sites indicates that the ecosys model achieves R 2 values of 0.9 and 0.8 for the simulated versus the EC daily GPP and respiration, respectively, and 0.9 for the simulated versus the experimentally measured leaf area index. The R 2 at county level in our framework is 0.8 for both cotton lint yield and GPP: the simulated versus survey-based cotton lint yield, and the simulated versus satellite-based monthly GPP. The spatio-temporal patterns of the simulated cotton lint yield, GPP, and their responses to climate factors (average temperature, average vapor pressure deficit (VPD), and cumulative precipitation during the growing season) are consistent with the observations, indicating that our framework approach captures the underlying processes relating environmental conditions to cotton growth. Our analysis shows that cotton productivity (lint yield and GPP) decreased with increasing average VPD during the growing season, especially under rainfed conditions. It also shows that the carbon budget terms, including predicted net primary productivity, crop yield, and soil heterotrophic respiration, decreased as the VPD increased. Conversely, the predicted change in soil organic carbon was less influenced by climate, which decreased with increasing initial soil organic carbon content and cation exchange capacity, and increased with increasing soil bulk density. The variable impacts of crop management practices, climatic factors, and soil characteristics on carbon budgets highlight the intricate interactions among these factors that shape carbon dynamics in cotton agroecosystems, and further emphasize the necessity of accurately simulating the carbon budgets of cotton agroecosystems across temporal and spatial scales. This study has established a framework that utilizes advanced MDF to assess climate mitigation strategies for U.S. cotton agroecosystems.

  PublisherDOI

• Scalable quantification of agroecosystem carbon budget and crop yield based on knowledge-guided machine learning

  2025-03-14 · 1 citations

  preprintOpen access1st author

  Quantifying carbon outcomes from agroecosystems plays an important role in mitigating global warming and ensuring food security through sustainable production. However, high spatial-temporal-resolution (e.g., ~100m, daily), accurate, well-resolved carbon budgets and crop yield in agroecosystems are extremely challenging to quantify due to the complexity of involved processes and large variations in environmental and management drivers. Traditional process-based-modeling approaches are computationally expensive to achieve field-scale resolution and contain large uncertainty due to underdetermined model structure and parameters. Knowledge-guided machine learning (KGML) is a hybrid modeling approach that leverages recent advances in machine learning combined with known physical principles and relationships to enhance the training and application processes, which helps open the “black box” of conventional ML models, and enable better predictions that capture variability in both time and space. Here we proposed a data-efficient KGML framework that effectively predicts daily variations in agricultural CO2 emissions, crop yields, and soil carbon storage at field scale, as successfully demonstrated for the US Midwest. Multi-source data and pretraining with outputs from a well-validated agroecosystem model were incorporated into a hierarchically structured deep learning neural network that greatly outperformed both process-based and pure machine learning models, especially in data-limited cases. This work demonstrates the advantages of integrating domain knowledge with state-of-the-art artificial intelligence in agroecosystem modeling that will lead toward broader use of KGML in geoscience.

  PublisherDOI

Frequent coauthors

• Bin Peng

  115 shared

• Kaiyu Guan

  108 shared

• Jiancheng Shi

  China Agricultural University

  79 shared

• Yuechi Yu

  Aerospace Information Research Institute

  57 shared

• Tianxing Wang

  Ministry of Natural Resources

  55 shared

• Rui Zhao

  Xihua University

  50 shared

• Da Pao Yu

  Institute of Applied Ecology

  37 shared

• Li Min Dai

  34 shared

Labs

• Intelligent Matter LabPI

  Designing Intelligent Materials Across Scales

Education

• Ph.D.

  University of the Chinese Academy of Sciences

  2019

• Bachelor

  Beijing Normal University

  2013

Awards & honors

• Materials Research Society Fellowships and Awards