检索结果-内蒙古大学图书馆

SSRN 2025年

作者： Wu, Yudan Ba, Yihan Chen, Chen Wang, Qi Chen, Qinsheng Zhang, Jinxian Chen, Longlong Zhao, Yining Shi, Biru Ye, Lisong Chen, Wei Wang, Chengyan Hu, Zixin Zhang, Jun Feng, Kai Wang, Yan Liu, Yuehua Qi, Yuan Tang, Huiru Luo, Jingchun Tian, Mei State Key Laboratory of Genetic Engineering School of Life Sciences Human Phenome Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai201203 China Department of Endocrinology and Metabolism Zhongshan Hospital Fudan University Shanghai200032 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Chinese Academy of Sciences Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of the Chinese Academy of Sciences Chinese Academy of Sciences Shanghai200031 China Fudan University Shanghai201203 China Department of Radiology Huashan Hospital State Key Laboratory of Medical Neurobiology National Center for Neurological Disorders Fudan University Shanghai200040 China Department of Preventive Dentistry Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200433 China Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China

Background: Obstructive sleep apnea (OSA) prevalence is increasing among Asian populations, yet effective biomarker-based screening tools remain underdeveloped. Metabolic dysregulation and adiposity patterns show potential as novel biomarkers for OSA risk stratification, but their predictive utility has not been comprehensively evaluated in Chinese populations. We aimed to identify key metabolic and adiposity biomarkers for OSA and subsequently develop an integrated prediction model to enhance risk assessment in a Chinese ***: In this prospective cohort study (2018-2023), we enrolled 972 adults (aged 20-60 years) from the China Human Phenobank. All participants underwent overnight polysomnography, sleep questionnaires, metabolomic profiling (1,840 metabolites), and DXA-derived body fat distribution analysis (58 adiposity parameters). OSA was defined according to international clinical criteria as apnea hypopnoea index (AHI) ≥5/h accompanied by characteristic symptoms (excessive daytime sleepiness, fatigue, or impaired cognition) or associated comorbidities. Participants were randomly allocated to training (n=777) and test (n=195) cohorts. We employed a multi-staged feature selection approach integrating differential analysis, correlation filtering, and machine learning algorithms (mRMR and LASSO) to identify key biomarkers. Selected features were integrated into a predictive model combining clinical, metabolomic, and adiposity data. Metabolic pathway enrichment analysis identified biological mechanisms underlying OSA *** identified two critical fat distribution markers (visceral adipose tissue volume and gynoid fat) and ten metabolic signatures (including specific TAG-FAs, lipoprotein subtypes, and steroid derivatives) significantly associated with OSA. The gender-specific fat distribution patterns and protective effects of gynoid fat distribution provided new insights into OSA pathophysiology. Notably, we identified H2FC, a specific H

关键词： Risk assessment

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Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

arXiv

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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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${\sf DeepNC}$DeepNC: Deep Generative Network Completion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2020年第4期44卷 1837-1852页

作者： Cong Tran Won-Yong Shin Andreas Spitz Michael Gertz Department of Computer Science and Engineering Dankook University Yongin Republic of Korea Machine Intelligence & Data Science Laboratory Yonsei University Seoul Republic of Korea School of Mathematics and Computing (Computational Science and Engineering) Yonsei University Seoul Republic of Korea School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Lausanne Switzerland Institute of Computer Science Heidelberg University Heidelberg Germany

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient ${\sf DeepNC}$ algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.

关键词： Matrix decomposition Prediction algorithms Deep learning Pattern analysis Social networking (online) Predictive models

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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Design and simulation of a SiPM-on-tile ZDC for the future EIC, and its performance with graph neural networks

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Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2025年 1079卷

作者： Ryan Milton Sebouh J. Paul Barak Schmookler Miguel Arratia Piyush Karande Aaron Angerami Fernando Torales Acosta Benjamin Nachman Department of Physics and Astronomy University of California Riverside CA 92521 USA Computational Engineering Division Lawrence Livermore National Laboratory Livermore CA 94550 USA Nuclear and Chemical Science Division Lawrence Livermore National Laboratory Livermore CA 94550 USA Physics Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA Berkeley Institute for Data Science University of California Berkeley CA 94720 USA

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design’s high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the report on science requirements and detector requirements for the EIC (Yellow Report), laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods.

关键词： Calorimeter EIC Highly-granular calorimetry Zero-degree calorimeter

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Antisymmetry rules of response properties in certain chemical spaces

arXiv

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arXiv 2025年

作者： Shiraogawa, Takafumi Krug, Simon León Ehara, Masahiro von Lilienfeld, O. Anatole Institute for Molecular Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Research Center for Computational Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan The Graduate University for Advanced Studies 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Machine Learning Group Technische Universität Berlin Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoM5S3H6 Ontario Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoM5S 3E4 Ontario Canada Vector Institute for Artificial Intelligence TorontoM5S 1M1 Ontario Canada Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Physics University of Toronto St. George Campus TorontoM5S 1A7 Ontario Canada Acceleration Consortium University of Toronto TorontoM5R 0A3 Ontario Canada

Understanding chemical compound space (CCS), a set of molecules and materials, is crucial for the rational discovery of molecules and materials. Concepts of symmetry have recently been introduced into CCS to account for near degeneracies and differences in electronic energies between iso-electronic materials. In this work, we present approximate relationships of response properties based on a first-principles view of CCS. They have been derived from perturbation theory and antisymmetry considerations involving nuclear charges. These rules allow approximate predictions of relative response properties of pairs of distinct compounds with opposite nuclear charge variations from a highly symmetric reference material, without the need for experiments or quantum chemical calculations of each compound. We numerically and statistically verified these rules for electric and magnetic response properties (electric dipole moment, polarizabilities, hyperpolarizabilities, and magnetizabilities) among charge-neutral and iso-electronic BN-doped polycyclic aromatic hydrocarbon derivatives of naphthalene, anthracene, and pyrene. Our analysis indicates that, despite their simplicity, antisymmetry rule-based predictions are remarkably accurate, enabling dimensionality reduction of CCS. Response properties in alchemical perturbation density functional theory were investigated to clarify the origin of this predictive power. © 2025, CC BY.

关键词： Naphthalene

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Weisfeiler and Leman go Machine Learning: The Story so far

arXiv

引用

arXiv 2021年

作者： Morris, Christopher Lipman, Yaron Maron, Haggai Rieck, Bastian Kriege, Nils M. Grohe, Martin Fey, Matthias Borgwardt, Karsten Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain ViewCA United States Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler–Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm’s use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm’s connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING

arXiv

引用

arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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Is fixed-node diffusion quantum Monte Carlo reproducible?

arXiv

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arXiv 2025年

作者： Pia, Flaviano Della Shi, Benjamin X. Al-Hamdani, Yasmine S. Alfè, Dario Anderson, Tyler A. Barborini, Matteo Benali, Anouar Casula, Michele Drummond, Neil Dubecký, Matúš Filippi, Claudia Kent, Paul R.C. Krogel, Jaron T. Ríos, Pablo López Lüchow, Arne Luo, Ye Michaelides, Angelos Mitas, Lubos Nakano, Kousuke Needs, Richard Per, Manolo C. Scemama, Anthony Schultze, Jil Shinde, Ravindra Slootman, Emiel Sorella, Sandro Tkatchenko, Alexandre Towler, Mike Umrigar, C.J. Wagner, Lucas K. Wheeler, William A. Zhou, Haihan Zen, Andrea Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Department of Earth Sciences University College London LondonWC1E 6BT United Kingdom Dipartimento di Fisica Ettore Pancini Università di Napoli Federico II Monte S. Angelo NapoliI-80126 Italy Laboratory of Atomic and Solid State Physics Cornell University IthacaNY14853 United States HPC Platform University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg Computational Science Division Argonne National Laboratory LemontIL United States Sorbonne Université CNRS UMR 7590 MNHN 4 Place Jussieu Paris75252 France Department of Physics Lancaster University LancasterLA1 4YB United Kingdom Department of Physics Faculty of Science University of Ostrava 30. Dubna 22 Ostrava701 03 Czech Republic MESA+ Institute for Nanotechnology University of Twente Enschede7500 AE Netherlands Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Max Planck Institute for Solid State Research Heisenbergstr. 1 Stuttgart70569 Germany Institute of Physical Chemistry RWTH Aachen University Landoltweg 2 Aachen52074 Germany Department of Physics North Carolina State University RaleighNC27695-8202 United States 1-2-1 Sengen Tsukuba Ibaraki305-0047 Japan Theory of Condensed Matter Group Cavendish Laboratory J. J. Thomson Avenue CambridgeCB3 0HE United Kingdom CSIRO Data61 ClaytonVIC3168 Australia Université de Toulouse CNRS UPS Toulouse31062 France International School for Advanced Studies SISSA Trieste34136 Italy Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg The Apuan Alps Centre for Physics Vallico Sotto Italy Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engi

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted manybody method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalabilitywith system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology. © 2025, CC BY.

关键词： Wave functions

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