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arXiv 2025年

作者： Hamid, Md Abdul Smith, Kyle C. Department of Mechanical Science and Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Program Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Hydrodynamic dispersion determines the extent of solute mixing and apparent diffusive flux in various chemical, geological, and biological systems that possess flow with strong velocity gradients. Though established dispersion theory captures the transient interplay between diffusion, flow, and reaction rates, a long-time approximation is commonly adopted where the characteristic time scale of flow substantially exceeds that of pore-scale diffusion. Here, we introduce a frequency-based theory to model omni-temporal dispersion through porous media that simultaneously captures fast and slow components of dispersion. To create this theory, we formally volume-average the Fourier-transformed pore-scale advection-diffusion equation for solute and obtain up-scaled transport coefficients for a periodic unit cell: a dispersion tensor, an advection suppression vector, and reaction-rate coefficients. These coefficients serve dually as transfer functions that correlate certain output with input quantities in the frequency domain, enabling the prediction of up-scaled temporal dynamics using inverse Fourier transformation. Their utility is demonstrated by deriving analytical expressions for the dispersion coefficient for cases of Poiseuille flow between parallel plates and through circular tubes. This omni-temporal theory produces breakthrough curves for a fast solute pulse between inactive parallel plates that agree with the solute propagation rates predicted by direct numerical simulation (DNS), in contrast with conventional asymptotic theory that overpredicts such by orders of magnitude. Deviations in solute peak magnitude from DNS are shown to arise from solute back-diffusion into the inlet plane as well as entry-region effects that are evidenced by a non-periodic variation of the closure variable *** Codes 76 © 2025, CC BY-NC-ND.

关键词： Inverse transforms

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Identifying viruses from metagenomic data using deep learning

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Quantitative Biology 2020年第1期8卷 64-77页

作者： Jie Ren Kai Song Chao Deng Nathan AAhlgren Jed AFuhrman Yi Li Xiaohui Xie Ryan Poplin Fengzhu Sun Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA School of Mathematics and Statistics Qingdao UniversityQingdao 266071China Department of Biology Clark UniversityWorcesterMA 01610USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089USA Department of Computer Science University of CaliforniaIrvineCA 92697USA Google Inc. Mountain ViewCA 94043USA

Background:The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory *** reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic ***:Here we developed a reference-free and alignment-free machine learning method,DeepVirFinder,for identifying viral sequences in metagenomic data using deep ***'.Trained based on sequences from viral RefSeq discovered before May 2015,and evaluated on those discovered after that date,DeepVirFinder outperformed the state-of-the-art method VirFinder at all contig lengths,achieving AUROC 0.93,0.95,0.97,and 0.98 for 300,500,1000,and 3000 bp sequences *** the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are *** DeepVirFinder to real human gut metagenomic samples,we identified 51,138 viral sequences belonging to 175 bins in patients with colorectal carcinoma(CRC).Ten bins were found associated with the cancer status,suggesting viruses may play important roles in ***:Powered by deep learning and high throughput sequencing metagenomic data,DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics.

关键词： metagenome deep learning virus identification machine learning

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Diamine Surface Passivation and Post-Annealing Enhance Performance of Silicon-Perovskite Tandem Solar Cells

arXiv

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arXiv 2024年

作者： Taddei, Margherita Contreras, Hannah Doan, Hai-Nam McCarthy, Declan P. Seo, Seongrok Westbrook, Robert J.E. Graham, Daniel J. Datta, Kunal Carroy, Perrine Muñoz, Delfina Correa-Baena, Juan-Pablo Barlow, Stephen Marder, Seth R. Smith, Joel A. Snaith, Henry J. Ginger, David S. Department of Chemistry University of Washington SeattleWA98195 United States Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Institute of Materials Science Technical University of Munich München85748 Germany Department of Chemistry University of Colorado-Boulder BoulderCO80309 United States Department of Bioengineering University of Washington SeattleWA98195 United States School of Materials Science and Engineering Georgia Institute of Technology North Ave NW AtlantaGA30332 United States Université Grenoble Alpes CEA Liten Campus Ines Le Bourget du Lac73375 France Renewable and Sustainable Energy Institute University of Colorado-Boulder BoulderCO80309 United States Department of Chemical and Biological Engineering and Materials Science Program University of Colorado-Boulder BoulderCO80309 United States Physical Sciences Division Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99352 United States

We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, primarily by boosting the open-circuit voltage (VOC) from 1.06 V to 1.15 V. Adding a post-processing annealing step after C60 evaporation, further improves the fill factor (FF) by 20% from the control to the DAP + post-annealing devices. Using hyperspectral photoluminescence microscopy, we demonstrate that annealing helps improve compositional homogeneity at the top and bottom interfaces of the solar cell, which prevents detrimental bandgap pinning in the devices and improves C60 adhesion. Using time-of-flight secondary ion mass spectrometry, we show that DAP reacts with formamidinium present near the surface of the perovskite lattice to form a larger molecular cation, 1,4,5,6-tetrahydropyrimidinium (THP) that remains at the interface. Combining the use of DAP and the annealing of C60 interface, we fabricate Si-perovskite tandems with PCE of 25.29%, compared to 23.26% for control devices. Our study underscores the critical role of chemical reactivity and thermal post-processing of the C60/Lewis-base passivator interface in minimizing device losses and advancing solar-cell performance of wide-bandgap mixed-cation mixed-halide perovskite for tandem application. © 2024, CC BY.

关键词： Perovskite

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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science China(Life sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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Simultaneous biomonitoring of volatile organic compounds’ metabolites in human urine samples using a novel in-syringe based fast urinary metabolites extraction (FaUMEx) technique coupled with UHPLC-MS/MS analysis

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Chemosphere 2023年第1期329卷 138667页

作者： Gurrani, Swapnil Prakasham, Karthikeyan Huang, Po-Chin Wu, Ming-Tsang Wu, Chia-Fang Lin, Yu-Chia Tsai, Bongee Krishnan, Anbarasu Tsai, Pei-Chien Ponnusamy, Vinoth Kumar Kaohsiung City807 Taiwan Miaoli County 35053 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Kaohsiung City807 Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung City Taiwan International Master Program of Translational Medicine College of Engineering and Science National United University Miaoli Taiwan Corporation No.392 Yucheng Rd. Zuoying District. Kaohsiung City813 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan

Assessing the impact of human exposure to environmental toxicants is often crucial to biomonitoring the exposed dose. In this work, we report a novel fast urinary metabolites extraction (FaUMEx) technique coupled with UHPLC-MS/MS analysis for the highly sensitive and simultaneous biomonitoring of the five major urinary metabolites (thiodiglycolic acid, s-phenylmercapturic acid, t,t-muconic acid, mandelic acid, and phenyl glyoxylic acid) of common volatile organic compounds' (VOCs) exposure (vinyl chloride, benzene, styrene, and ethylbenzene) in human. FaUMEx technique comprises of two-steps, liquid-liquid microextraction was performed first in an extraction syringe using 1 mL of methanol (pH 3) as an extraction solvent and then, the extractant was passed through a clean-up syringe (pre-packed-with various sorbents including 500 mg anhydrous MgSO4, 50 mg C18, and 50 mg SiO2) to obtain the high order of matrice clean-up and preconcentration efficiency. The developed method displayed excellent linearity, and the correlation coefficients were >0.998 for all the target metabolites with detection and quantification limits of 0.02–0.24 ng mL−1 and 0.05–0.72 ng mL−1, respectively. Furthermore, the matrix effects were © 2023 Elsevier Ltd

关键词： Metabolites

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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ChemicalUniverseMachine I: Uncovering the Cosmic Evolution of Metals in the Galaxy-ISM-CGM Ecosystem

arXiv

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arXiv 2025年

作者： Nishigaki, Moka Behroozi, Peter Ouchi, Masami Guo, Hong Somerville, Rachel S. Gallazzi, Anna R. Nakajima, Kimihiko Watanabe, Kuria National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomical Science The Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomy Steward Observatory University of Arizona TucsonAZ85721 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8583 Japan Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States INAF-Osservatorio Astrofisico di Arcetri Largo Enrico Fermi 5 FirenzeI-50125 Italy Institute of Liberal Arts and Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan

We present an empirical chemical evolution model that explains the distribution of metals in the interstellar medium (ISM) and the circumgalactic medium (CGM) of galaxies based on the UniverseMachine and NeutralUniverseMachine models in the framework of ΛCDM structure formation. We parameterize the fractions of outflowing metals returned and mixed into the multi-phase ISM of the star-forming regions (fH2) and into the neutral gas regions (fHI);metal production, transfer, and dilution are caused by star formation, galaxy mergers, and gas inflow from the inter-galactic medium, respectively, with rates determined by the (Neutral)UniverseMachine models. Using a Markov Chain Monte Carlo algorithm, we explore the posterior distributions of metal return and mixing consistent with observed mass-metallicity relations in H ii regions (at 0 H2 + fHI, increases with halo mass from ∼ 20% at 1010M☉ to ∼ 80% at 1013M☉. These fractions increase mildly at higher redshifts, to ∼ 30% at 1010M☉ and ∼ 80% at 1013M☉ at z = 5. Interestingly, there is no significant redshift evolution of fH2 + fHI at fixed circular velocity, suggesting that metal distribution between the ISM and CGM is universally determined by the halo potential well depth. CGM metal enrichment is thus slow in high-z halos with deep potential wells. While fH2 monotonically increases with halo mass, fHI peaks at ∼ 1012 − 1013M☉, suggesting that reinfall may be inefficient in larger-mass halos. © 2025, CC BY.

关键词： Cosmology

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. MacDermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, David L. Chodera, John D. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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Study of self-interaction errors in density functional predictions of dipole polarizabilities and ionization energies of water clusters using perdew-zunger and locally scaled self-interaction corrected methods

arXiv

引用

arXiv 2020年

作者： Akter, Sharmin Yamamoto, Yoh Diaz, Carlos M. Jackson, Koblar A. Zope, Rajendra R. Baruah, Tunna Computational Science Program University of Texas at El Paso El PasoTX79968 United States Department of Physics University of Texas El PasoTX79968 United States Physics Department and Science of Advanced Materials Program Central Michigan University Mt. Pleasant Michigan48859 United States

We studied the effect of self-interaction error (SIE) on the static dipole polarizabilities of water clusters modelled with three increasingly sophisticated, non-empirical density functional approximations (DFAs), viz. the local spin density approximation (LDA), the Perdew-Burke-Ernzherof (PBE) generalized-gradient approximation (GGA), and the strongly constrained and appropriately normed (SCAN) meta-GGA, using the Perdew-Zunger self-interaction-correction (PZ-SIC) energy functional in the Fermi-Löwdin orbital SIC (FLO-SIC) framework. Our results show that while all three DFAs overestimate the cluster polarizabilities, the description systematically improves from LDA to PBE to SCAN. The self-correlation free SCAN predicts polarizabilities quite accurately with a mean absolute error (MAE) of 0.58 Bohr3 with respect to coupled cluster singles and doubles (CCSD) values. Removing SIE using PZ-SIC correctly reduces the DFA polarizabilities, but over-corrects, resulting in underestimated polarizabilities in SIC-LDA, -PBE, and -SCAN. Finally, we applied a recently proposed local-scaling SIC (LSIC) method using a quasi self-consistent scheme and using the kinetic energy density ratio as an iso-orbital indicator. The results show that the LSIC polarizabilities are in excellent agreement with mean absolute error of 0.08 Bohr3 for LSIC-LDA and 0.06 Bohr3 for LSIC-PBE with most recent CCSD polarizabilities. Likewise, the ionization energy estimates as an absolute of highest occupied energy eigenvalue predicted by LSIC are also in excellent agreement with CCSD(T) ionization energies with MAE of 0.4 eV for LSIC-LDA and 0.04 eV for LSIC-PBE. The LSIC-LDA predictions of ionization energies are comparable to the reported GW ionization energies while the LSIC-PBE ionization energies are more accurate than reported GW results. Copyright © 2020, The Authors. All rights reserved.

关键词： Numerical methods

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