@unpublished{19552,
  abstract     = {Particle creation terms in quantum Hamiltonians are usually ultraviolet
divergent and thus mathematically ill defined. A rather novel way of solving
this problem is based on imposing so-called interior-boundary conditions on the
wave function. Previous papers showed that this approach works in the
non-relativistic regime, but particle creation is mostly relevant in the
relativistic case after all. In flat relativistic space-time (that is,
neglecting gravity), the approach was previously found to work only for certain
somewhat artificial cases. Here, as a way of taking gravity into account, we
consider curved space-time, specifically the super-critical
Reissner-Nordstr\"om space-time, which features a naked timelike singularity.
We find that the interior-boundary approach works fully in this setting; in
particular, we prove rigorously the existence of well-defined, self-adjoint
Hamiltonians with particle creation at the singularity, based on
interior-boundary conditions. We also non-rigorously analyze the asymptotic
behavior of the Bohmian trajectories and construct the corresponding Bohm-Bell
process of particle creation, motion, and annihilation. The upshot is that in
quantum physics, a naked space-time singularity need not lead to a breakdown of
physical laws, but on the contrary allows for boundary conditions governing
what comes out of the singularity and thereby removing the ultraviolet
divergence.},
  author       = {Henheik, Sven Joscha and Poudyal, Bipul and Tumulka, Roderich},
  booktitle    = {arXiv},
  title        = {{How a space-time singularity helps remove the ultraviolet divergence problem}},
  doi          = {10.48550/arXiv.2409.00677},
  year         = {2025},
}

@article{22450,
  abstract     = {Background: Residential exposure to trees has been associated with reduced mortality risks. We hypothesise that in addition to tree canopy cover, tree canopy configuration also plays a role in exposure–mortality relationships. As there is limited evidence on this hypothesis, especially longitudinal evidence, we performed a nationwide study to investigate the residential tree canopy configuration–mortality associations in the Swiss population.
Methods: In this longitudinal study, the tree canopy cover and configuration metrics within 500 m of individuals’ residences were quantified using high-resolution tree canopy data (1 × 1 m) from 2010 to 2019. We developed single-exposure and multi-exposure time-varying Cox regression models to estimate the associations between the different exposure metrics and natural-cause and cause-specific mortality in Swiss adults (aged from 20 years to 90 years). Mortality and census data were taken from the Swiss National Cohort (SNC). We estimated the hazard ratios (HRs) and corresponding 95% CIs per IQR increase in the metrics adjusting for personal sociodemographic and contextual covariates. We also explored the effect modification by tree canopy cover, PM10, air temperature, urbanisation level, age, sex, and area-based local socioeconomic position.
Findings: Our analyses included 6 215 073 individuals from the SNC between 2010 and 2019. In the fully adjusted single-exposure models, we observed protective associations between natural-cause mortality risk and tree canopy cover (IQR 12·4%, HR 0·979 [95% CI 0·975–0·983]) and configuration metrics describing the aggregation (6·3%, 0·831 [0·823–0·840]), and connectedness (2·9%, 0·946 [0·938–0·953]); and detrimental associations with two metrics describing the fragmentation (211 patches per 100 ha, 1·073 [1·066–1·080]) and shape complexity (1·9, 1·094 [1·089–1·100]) of patches. The associations were generally preserved with other common causes of death. According to the multi-exposure models, the HR (95% CI) for the combination of one IQR decrease in aggregation and one IQR increase in fragmentation and shape complexity was 1·366 (1·343–1·390). Analyses on modification effects suggested a stronger association in people living in areas with a higher level of tree canopy cover, PM10 concentration, air temperature, and urbanisation level.
Interpretation: Aggregated, connected, and less fragmented forested greenspaces might offer stronger health benefits than isolated, fragmented ones, but are difficult to implement in cities. Our study provided valuable insights into optimising forested greenspaces and highlighted future directions for the planning and management of urban forests towards healthy and green cities.},
  author       = {Chi, Dengkai and Manoli, Gabriele and Lin, Brenda and Aerts, Raf and Yang, Jun and Hahs, Amy and Richards, Daniel and Meili, Naika and Zhu, Yue and Qiu, Yeshan and Wang, Jing and Burlando, Paolo and Fatichi, Simone and Tan, Puay Yok},
  issn         = {2542-5196},
  journal      = {The Lancet Planetary Health},
  number       = {3},
  pages        = {e186--e195},
  publisher    = {Elsevier},
  title        = {{Residential tree canopy configuration and mortality in 6 million Swiss adults: A longitudinal study}},
  doi          = {10.1016/s2542-5196(25)00022-1},
  volume       = {9},
  year         = {2025},
}

@article{22469,
  abstract     = {Accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and urban areas, where the availability of high-resolution topographic data is especially limited. While publicly available digital elevation model (DEM) datasets are increasingly accessible, their spatial resolution is often insufficient for reflecting fine-scaled elevation details, which hinders the ability to simulate pluvial floods in built environments. To address this issue, we implemented a deep-learning-based method, which efficiently enhances the spatial resolution of DEM data, and quantified the effect of the improved DEM on flood simulation. The method employs a tailored multi-source input module, enabling it to effectively integrate and learn from diverse data sources. By utilising publicly accessible global datasets, such as low-resolution DEM datasets (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution multispectral imagery (e.g. Sentinel-2A), our approach allows us to produce a super-resolution DEM, which exhibits superior performance compared to conventional methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral satellite images. We evaluated the performance of the super-resolution DEM in flood simulations. Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations.},
  author       = {Zhu, Yue and Burlando, Paolo and Tan, Puay Yok and Geiß, Christian and Fatichi, Simone},
  issn         = {1684-9981},
  journal      = {Natural Hazards and Earth System Sciences},
  number       = {7},
  pages        = {2271--2286},
  publisher    = {Copernicus Publications},
  title        = {{Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method}},
  doi          = {10.5194/nhess-25-2271-2025},
  volume       = {25},
  year         = {2025},
}

@article{22463,
  abstract     = {Glacier retreat as a consequence of climate change creates new ice-free terrain and soil development that prompt plant colonization and ecological succession. These processes impact terrestrial ecosystems with profound ecological and societal consequences. However, quantification of how the carbon cycle evolves in deglaciated areas in response to these processes remains limited. We examined the impacts of forest expansion and soil development on the carbon cycle under climate change in a deglaciated area in the Swiss Alps. Using the mechanistic ecohydrological T&C model, we computed the changes in vegetation, soil, and carbon from 1981 to 2099 under climate change, revealing complex carbon cycle responses in deglaciating ecosystems. Vegetation growth, soil organic matter, and plant nutrient uptake are projected to increase by mid-century and then stabilize, indicating that plant growth is relatively limited by nutrient availability. The amount of carbon stored in plant biomass will increase toward the end of the century at a faster rate than that of carbon stored in soil and litter. The carbon cycle is projected to continue its current accelerating trend characterized by enhanced vegetation photosynthesis, increased plant and soil respiration, and higher net ecosystem production (NEP) by mid-century. Alpine ecosystems have already been serving as carbon sinks and have the potential to increase their carbon sink capacity, but at varying rates depending on how climate will evolve: NEP will stabilize around 24 gC m^−2 y^−1 in RCP4.5 or might elevate to 55 gC m^−2 y^−1 by end-century in RCP8.5. Even under the most extreme scenario, this increase in stored carbon in the proglacial areas of the Swiss Alps is still a drop in the ocean, as it represents only 0.9% of overall Swiss carbon emissions, thus highlighting the need for additional carbon management and mitigation efforts.},
  author       = {Jiang, Fuxiao and Fatichi, Simone and Losapio, Gianalberto and Peleg, Nadav},
  issn         = {1873-2240},
  journal      = {Agricultural and Forest Meteorology},
  publisher    = {Elsevier},
  title        = {{Future glacier retreat and forest expansion in the Swiss Alps provide limited benefits for carbon sinks}},
  doi          = {10.1016/j.agrformet.2025.110682},
  volume       = {372},
  year         = {2025},
}

@article{22462,
  abstract     = {Enhanced rock weathering (ERW) is an emerging carbon dioxide removal (CDR) strategy that can support net-zero emission targets. However, current ERW modelling efforts rely on assumptions that introduce substantial variation in CDR estimates across varying ecosystems and hydroclimatic conditions. They typically ignore or oversimplify plant–soil interactions and high-frequency hydrological dynamics, obscuring short-term weathering responses and biotic feedbacks to soil moisture dynamics. Here, we introduce an integrated, process-based modelling framework, T&C-SMEW, which represents ecohydrological and ERW dynamics, along with microbially explicit biogeochemical processes. We compared framework simulations against a controlled mesocosm experiment and long-term field observations, demonstrating its ability to reproduce feedstock cation release, soil pH dynamics, gross primary production, and CO2 fluxes. T&C-SMEW reveals hydrological constraints and vegetation effects on ERW-mediated CDR by quantifying impacts on ecosystem respiration, net ecosystem exchange, and alkalinity export, emphasising the importance of ecohydrological modelling for ecosystem-level CDR estimation. These advances provide a modelling framework for identifying optimal deployment scenarios to establish ERW as a viable and operationally feasible CDR approach.},
  author       = {Zhang, Ziyan and Jones, Gregory and Calabrese, Salvatore and Bertagni, Matteo and Fatichi, Simone and Waring, Bonnie and Paschalis, Athanasios},
  issn         = {1365-2486},
  journal      = {Global Change Biology},
  number       = {12},
  publisher    = {Wiley},
  title        = {{An integrated modelling framework to determine terrestrial carbon dioxide removal via enhanced rock weathering}},
  doi          = {10.1111/gcb.70650},
  volume       = {31},
  year         = {2025},
}

@article{22483,
  abstract     = {Agrivoltaic systems are characterized by the co‐existence of photovoltaic panels on agricultural land, allowing simultaneous solar energy and food production without need for further land. Agrivoltaic installations alter the local microclimatic conditions of the land surface, impacting the performance of the agricultural systems embedded in them. In this study we develop an ecohydrological modeling framework combining a module that simulates changes in micrometeorology due to photovoltaic panel installations with a state‐of‐the‐art model that resolves land surface water, energy, and vegetation dynamics (i.e., the terrestrial biosphere model T&C). We demonstrate that the modeling framework is capable of reproducing grassland dynamics across a broad range of climates and agrivoltaic architectures. With the use of the model we evaluated grassland performance across the Mediterranean for two most commonly used architectures, namely mixed mounted solar panels and rotating solar tracking panels. We found that C3 grassland yields can be significantly enhanced only in climates where annual potential evapotranspiration exceeds annual rainfall. Changes in grassland productivity were attributed primarily to changes in the light environment at the land surface, with changes in surface aerodynamic roughness and rainfall redistribution due to drainage on panels playing a smaller negative role of comparable magnitudes.},
  author       = {Paschalis, Athanasios and Bonetti, Sara and Fatichi, Simone},
  issn         = {2328-4277},
  journal      = {Earth's Future},
  number       = {3},
  publisher    = {American Geophysical Union},
  title        = {{Controls of ecohydrological grassland dynamics in agrivoltaic systems}},
  doi          = {10.1029/2024ef005183},
  volume       = {13},
  year         = {2025},
}

@article{22431,
  abstract     = {The snow and glaciers of the Peruvian Andes provide vital water supplies in a region facing water scarcity and substantial glacier change. However, there remains a lack of understanding of snow processes and quantification of the contribution of melt to runoff. Here we apply a distributed glacio-hydrological model over the Rio Santa basin to disentangle the role of the cryosphere in the Andean water cycle. Only at the highest elevations (>5000 m a.s.l.) is the snow cover continuous; at lower elevations, the snowpack is thin and ephemeral, with rapid cycles of snowfall and melt. Due to the large catchment area affected by ephemeral snow, its contribution to catchment inputs is substantial (23% and 38% in the wet and dry season, respectively). Ice melt is crucial in the mid-dry season (up to 44% of inputs). Our results improve estimates of water fluxes and call for further process-based modelling across the Andes.},
  author       = {Fyffe, Catriona L. and Potter, Emily and Miles, Evan and Shaw, Thomas E. and McCarthy, Michael and Orr, Andrew and Loarte, Edwin and Medina, Katy and Fatichi, Simone and Hellström, Rob and Baraer, Michel and Mateo, Emilio and Cochachin, Alejo and Westoby, Matthew and Pellicciotti, Francesca},
  issn         = {2662-4435},
  journal      = {Communications Earth & Environment},
  publisher    = {Springer Nature},
  title        = {{Thin and ephemeral snow shapes melt and runoff dynamics in the Peruvian Andes}},
  doi          = {10.1038/s43247-025-02379-x},
  volume       = {6},
  year         = {2025},
}

@article{22438,
  abstract     = {The topography of a landscape regulates the spatial distribution of water and energy fluxes, which are main drivers of vegetation and soil carbon and nutrient dynamics. Despite the recognized role of topography in mediating such processes, quantifying and predicting the spatial distribution of carbon and nutrient fluxes and stocks in highly heterogeneous landscapes remains challenging. The main limitations stem from the prevalence of largely decoupled modeling approaches which fail to concurrently account for ecohydrological and biogeochemical processes as well as the lack of adequate frameworks describing the links among topography, water and energy balances, and soil biogeochemical dynamics. Here, we extend the capabilities of the mechanistic ecohydrological model Tethys-Chloris-Biogeochemistry (T&C-BG) by including a soil carbon and nutrient routing module in the distributed model version. The newly developed T&C-BG-2D model is validated against long-term hydrological and biogeochemical measurements from the Hafren catchment in Wales (UK) and the Erlenbach catchment in the Swiss pre-Alps. The model successfully captures carbon and nutrient concentrations and dynamics in these catchments, with relative differences between simulated and observed median values of between −4% and −0.3% for dissolved organic carbon, and between 1% and 20% for ammonia. A sensitivity analysis in the Erlenbach basin suggests that elevation explains over 80% of the observed spatial patterns, followed by topographic wetness index (12.6%), aspect (2.9%), and curvature (2.1%). These findings underscore topography's critical role in shaping water, carbon, and nutrient dynamics, which cannot be reflected in plot-scale simulations neglecting spatial interactions and topographic effects.},
  author       = {Lian, Taiqi and Fatichi, Simone and Stähli, Manfred and Bonetti, Sara},
  issn         = {1944-7973},
  journal      = {Water Resources Research},
  number       = {10},
  publisher    = {American Geophysical Union},
  title        = {{Assessing spatial patterns of carbon and nutrient dynamics in catchments of complex topography}},
  doi          = {10.1029/2025wr040260},
  volume       = {61},
  year         = {2025},
}

@article{19785,
  abstract     = {We consider a family of totally asymmetric simple exclusion processes (TASEPs), consisting of particles on a lattice that require binding by a “token” in various physical configurations to advance over the lattice. Using a combination of theory and simulations, we address the following questions: (i) How does token binding kinetics affect the current-density relation on the lattice? (ii) How does this current-density relation depend on the scarcity of tokens? (iii) How do tokens propagate the effects of the locally imposed disorder (such as a slow site) over the entire lattice? (iv) How does a shared pool of tokens couple concurrent TASEPs running on multiple lattices? and (v) How do our results translate to TASEPs with open boundaries that exchange particles with the reservoir? Since real particle motion (including in biological systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.},
  author       = {Kavcic, Bor and Tkačik, Gašper},
  issn         = {2470-0053},
  journal      = {Physical Review E},
  number       = {5},
  publisher    = {American Physical Society},
  title        = {{Token-driven totally asymmetric simple exclusion processes}},
  doi          = {10.1103/physreve.111.054122},
  volume       = {111},
  year         = {2025},
}

@misc{20641,
  abstract     = {Protein conformational energy landscapes are shaped not only by intramolecular interactions but also by their environment. In protein crystals and protein-protein complexes, intermolecular contacts alter this energy landscape, but the exact nature of this alteration is difficult to decipher. Understanding how the crystal lattice affects protein dynamics is crucial for crystallography-based studies of motion, yet its influence on collective motions remains unclear. Aromatic ring flips in the hydrophobic core represent sensitive probes of such dynamics. Here, we compare the kinetics of aromatic ring flips in the protein GB1 in crystals, in complex with its binding partner IgG, and in solution, combining advanced isotope labeling with quantitative NMR methods. We show that rings in the core flip nearly a thousand times less frequently in crystals than in solution. Enhanced-sampling molecular dynamics simulations, based on a new crystal structure, reproduce these elevated barriers and reveal how the crystal restrains motions. },
  author       = {Becker, Lea Marie and Schanda, Paul},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Data for "Aromatic Ring Flips Reveal Reshaping of Protein Dynamics in Crystals and Complexes"}},
  doi          = {10.15479/AT-ISTA-20641},
  year         = {2025},
}

@article{18705,
  abstract     = {Given a non-singular diagonal cubic hypersurface X⊂Pn−1 over Fq(t) with char(Fq)≠3, we show that the number of rational points of height at most |P| is O(|P|3+ε) for n=6 and O(|P|2+ε) for n=4. In fact, if n=4 and char(Fq)>3 we prove that the number of rational points away from any rational line contained in X is bounded by O(|P|3/2+ε). From the result in 6 variables we deduce weak approximation for diagonal cubic hypersurfaces for n≥7 over Fq(t) when char(Fq)>3 and handle Waring's problem for cubes in 7 variables over Fq(t) when char(Fq)≠3. Our results answer a question of Davenport regarding the number of solutions of bounded height to x31+x32+x33=x34+x35+x36 with xi∈Fq[t].},
  author       = {Glas, Jakob and Hochfilzer, Leonhard},
  issn         = {1432-1807},
  journal      = {Mathematische Annalen},
  pages        = {5485--5533},
  publisher    = {Springer Nature},
  title        = {{On a question of Davenport and diagonal cubic forms over Fq(t)}},
  doi          = {10.1007/s00208-024-03035-z},
  volume       = {391},
  year         = {2025},
}

@article{18820,
  abstract     = {Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy.},
  author       = {Wild, Romina and Wodaczek, Felix and Del Tatto, Vittorio and Cheng, Bingqing and Laio, Alessandro},
  issn         = {2041-1723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance}},
  doi          = {10.1038/s41467-024-55449-7},
  volume       = {16},
  year         = {2025},
}

@article{20926,
  abstract     = {Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.},
  author       = {Kim, Dongjin and Wang, Xiaoyu and Vargas, Santiago and Zhong, Peichen and King, Daniel S. and Inizan, Theo Jaffrelot and Cheng, Bingqing},
  issn         = {1549-9626},
  journal      = {Journal of Chemical Theory and Computation},
  number       = {24},
  pages        = {12709--12724},
  publisher    = {American Chemical Society},
  title        = {{A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials}},
  doi          = {10.1021/acs.jctc.5c01400},
  volume       = {21},
  year         = {2025},
}

@article{20990,
  abstract     = {Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite. This work thus extends the capability of MLIPs to predict electrical response –without training on charges or polarization or BECs– and enables accurate modeling of electric-field-driven processes in diverse systems at scale.},
  author       = {Zhong, Peichen and Kim, Dongjin and King, Daniel S. and Cheng, Bingqing},
  issn         = {2057-3960},
  journal      = {npj Computational Materials},
  publisher    = {Springer Nature},
  title        = {{Machine learning interatomic potential can infer electrical response}},
  doi          = {10.1038/s41524-025-01911-z},
  volume       = {11},
  year         = {2025},
}

@article{22500,
  abstract     = {Terrestrial ecosystems have been serving as a strong carbon sink that offsets one-quarter of anthropogenic CO2 emissions. Carbon use efficiency (CUE), the percentage of photosynthesized carbon that is available for biomass production and other secondary carbon products, is one factor determining the carbon sink size. The global variation in CUE remains unclear, however, as recent reports disagree over the responses of CUE to temperature, dryness, forest types and stand age, and there are limited direct observations to constrain the related uncertainty. Here, we propose to infer CUE from spatially distributed observations of land–atmosphere CO2 exchange from global eddy covariance sites based on the degree of ecosystem respiration–photosynthesis coupling. Across 2,737 site-years, CUE derived from eddy covariance observations is 0.43 ± 0.12, consistent with previous inventory-based estimates (0.47 ± 0.12, n = 301) but with a better representation of spatial–temporal variation in CUE. We find that CUE consistently decreases with temperature, precipitation, light availability and stand age, with a substantial difference in the baseline CUE among biomes. Importantly, CUE of deciduous forests is typically 15% higher than that of evergreen forests, suggesting that over the long-term deciduous forests are more efficient in using photosynthate. Our study advances the understanding of the global variation in CUE and provides insights to guide best practices of forest conservation, management and restoration for carbon sequestration.},
  author       = {Luo, Xiangzhong and Zhao, Ruiying and Chu, Housen and Collalti, Alessio and Fatichi, Simone and Keenan, Trevor F. and Lu, Xinchen and Nguyen, Ngoc and Prentice, I. Colin and Sun, Wu and Yu, Kailiang and Yu, Liyao},
  issn         = {2397-334X},
  journal      = {Nature Ecology & Evolution},
  pages        = {1414--1425},
  publisher    = {Springer Science and Business Media LLC},
  title        = {{Global variation in vegetation carbon use efficiency inferred from eddy covariance observations}},
  doi          = {10.1038/s41559-025-02753-0},
  volume       = {9},
  year         = {2025},
}

@article{20492,
  abstract     = {The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs3Bi2I6Cl3 are perplexing and lacking theoretical explanations. Here, we first experimentally measure its thermal transport behavior from 20 to 300 K, after synthesizing Cs3Bi2I6Cl3 single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs3Bi2I6Cl3 has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the Wigner formulation of thermal transport, we reproduce theexperimental thermal conductivities based on lattice-distorted structures. This studythus provides a framework for predicting and understanding glassy thermal transportin materials with strong lattice disorder.},
  author       = {Zeng, Zezhu and Fan, Zheyong and Simoncelli, Michele and Chen, Chen and Liang, Ting and Chen, Yue and Thornton, Geoff and Cheng, Bingqing},
  issn         = {1091-6490},
  journal      = {Proceedings of the National Academy of Sciences},
  number       = {41},
  pages        = {e2415664122},
  publisher    = {National Academy of Sciences},
  title        = {{Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3}},
  doi          = {10.1073/pnas.2415664122},
  volume       = {122},
  year         = {2025},
}

@article{20011,
  abstract     = {Heat transport in glasses over a wide temperature range is critical for applications in gate dielectrics and thermal insulators but remains poorly understood due to the challenges in modeling vibrational anharmonicity and configurational dynamics across the glass transition. Recent predictions show an unusual decrease in thermal conductivity (κ) with temperature in amorphous hafnia (a-HfO2), contrasting with the typical trend in glasses. Using molecular dynamics with a machine-learning-based neuroevolution potential, we compute κ of a-HfO2 from 50 K to 2000 K. At low temperatures, the Wigner transport equation captures both anharmonicity and quantum statistics. Above 1200 K, atomic diffusion invalidates the quasiparticle picture, and we resort to the Green–Kubo method to capture convective transport. We further extend the Wigner transport equation to supercooled a-HfO2, revealing the crucial role of low-frequency modes in facilitating heat transport. The computed κ, based on both Green–Kubo and Wigner transport theories, increases continuously with temperature up to 2000 K.},
  author       = {Zeng, Zezhu and Liang, Xia and Fan, Zheyong and Chen, Yue and Simoncelli, Michele and Cheng, Bingqing},
  issn         = {2639-4979},
  journal      = {ACS Materials Letters},
  pages        = {2695--2701},
  publisher    = {American Chemical Society},
  title        = {{Thermal transport of amorphous hafnia across the glass transition}},
  doi          = {10.1021/acsmaterialslett.5c00263},
  year         = {2025},
}

@article{19495,
  abstract     = {Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a hidden variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged and polar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs.},
  author       = {Cheng, Bingqing},
  issn         = {2057-3960},
  journal      = {npj Computational Materials},
  publisher    = {Springer Nature},
  title        = {{Latent Ewald summation for machine learning of long-range interactions}},
  doi          = {10.1038/s41524-025-01577-7},
  volume       = {11},
  year         = {2025},
}

@article{22501,
  abstract     = {Neglecting the temporal variations in population distribution can lead to significant discrepancies in exposure estimations for disaster management, especially in the face of increasing natural hazards due to climate change. Effective disaster management necessitates a nuanced understanding of how the urban environment influences the temporal variations in population distribution. This study addresses this knowledge gap by investigating the relationship between the spatial patterns of urban elements and daytime-nighttime population differences across eight European cities. The study reveals a substantial association between urban form indicators and daytime-nighttime population differences. Although the findings suggest that there is no one-size-fits-all set of indicators for different cities, ‘closeness centrality’, which measures the accessibility of a specific location within the overall street network, is identified as a key proxy for daytime-nighttime population differences across all cities analysed, which can be further linked to the accuracy of hazard exposure estimation. These findings can contribute to enhancing urban resilience by offering insights into spatio-temporal population dynamics and considering their implications for disaster management.},
  author       = {Zhu, Yue and Wang, Jing and Manoli, Gabriele and Zhang, Ye and Chi, Dengkai and Meili, Naika and Qiu, Yeshan and Lin, Guo-Shiuan and Tan, Puay Yok and Burlando, Paolo and Fatichi, Simone},
  issn         = {2661-8001},
  journal      = {npj Urban Sustainability},
  publisher    = {Springer Nature},
  title        = {{Influence of urban form and function on daytime-nighttime population differences and hazard risk assessments}},
  doi          = {10.1038/s42949-025-00282-0},
  volume       = {5},
  year         = {2025},
}

@article{20702,
  abstract     = {Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.},
  author       = {King, Daniel S. and Grzenda, Daniel and Zhu, Ray and Hudson, Nathaniel and Foster, Ian and Cheng, Bingqing and Gagliardi, Laura},
  issn         = {1091-6490},
  journal      = {Proceedings of the National Academy of Sciences},
  number       = {48},
  publisher    = {National Academy of Sciences},
  title        = {{Cartesian equivariant representations for learning and understanding molecular orbitals}},
  doi          = {10.1073/pnas.2510235122},
  volume       = {122},
  year         = {2025},
}