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Transient Thermal Analysis of Welding Fixtures Design

IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： M T Anual T R Sahroni S Akmal F A Nasution Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Faculty of Manufacturing Engineering Innovative Software System & Services Research Group Centre for Advanced Computing Technology Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia. Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480

This paper presents the design and analysis of welding fixtures based on transient thermal analysis. Welding fixtures is designed to hold and support the workpiece securely during the welding process. The heat transformation from the workpiece could lead the temperature rises and impacts to the welding fixtures. Therefore, the study introduces the development of welding fixtures as one of a safety measure prevents the welder from injury while handling welding process. The conceptual design of welding fixtures and material were applied to conduct the simulation. Transient thermal analysis was performed on temperature and total heat flux by using computer aided engineering software. The result shows the analysis of equivalent stress, total deformation, factors of safety, fatigue life and fatigue damage. Furthermore, the equivalent stress failure theory used to predict yielding in welding fixture design. The design of welding fixtures and material selection were proposed to hold and support the workpiece securely.

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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INNOVATIONS IN COMPUTER-AIDED-DESIGN OF MARINE TURBINES USING INTERACTIVE GRAPHICS

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 207-217页

作者： GINGRICH, JK WINTER, RL Mr. John K. Gingrich joined the General Electric Company after his graduation from Lafayette College in 1948. His early assignments were with the General Engineering Laboratory and the Knolls Atomic Power Laboratory in Schenectady. N. Y. He joined the Marine Turbine and Gear Department in 1968 as Manager Engineering Administration. and assumed his present assignment as Manager Engineering Resource Planning & Administration in 1976. Mr. Gingrich was instrumental in procuring the Interactive Graphics System for the Medium Steam Department. having headed up the Study Team and is now responsible for the Interactive Graphics Systems management including new procurements. He is a member of the American Institute of Design Drafting and is currently the Chairman of the Computer-Aided Drafting Committee in that organization. Mr. Reinhold L. Winter is a graduate of the General Electric Apprentice Program and Salem State College from which he received his B.S. degree in Business Administration. He has nineteen years experience with the General Electric Company including eight years in Business Systems and Programming two years in Computer Operations Management and six years in his present position as Manager Engineering Business Systems. He was a member of the original Study Team that investigated and selected the present Applicon Interactive Graphics (IAG) equipment and currently is responsible for the management of software and hardware support for the ZAG Facility.

This paper discusses the Interactive Graphics System used by the General Electric Company, Medium Steam Turbine Department (engineering & Manufacturing) for designing, drafting, and manufacturing applications. A brief overview of the hardware malting up the system is described, followed by a more detailed description of the actual applications. Two-dimensional applications described include a Heat Balance Analysis, Flow Diagrams, and Electrical Schematics. A more fruitful area for increased productivity gains is described in the three-dimensional or mechanical applications including turbine design & layout and bucket design. coordination of the design with manufacturing for numerical control tape generation is described through CAM and Plate Frame Cutting applications. Finally, a short review of the engineering design work using Interactive Graphics is discussed. Productivity gains of 2.6 to 1 are being realized, and the overall savings to the Medium Steam Department are outlined.

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Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging

arXiv

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arXiv 2023年

作者： Stanley, Emma A.M. Souza, Raissa Winder, Anthony J. Gulve, Vedant Amador, Kimberly Wilms, Matthias Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary CalgaryABT2N 1N4 Canada Department of Radiology University of Calgary CalgaryABT2N 4N1 Canada Hotchkiss Brain Institute University of Calgary CalgaryABT2N 4N1 Canada Alberta Children’s Hospital Research Institute University of Calgary CalgaryABT2N 4N1 Canada Department of Pediatrics University of Calgary CalgaryABT2N 4N1 Canada Department of Community Health Sciences University of Calgary CalgaryABT2N 4N1 Canada Department of Clinical Neuroscience University of Calgary CalgaryABT2N 4N1 Canada Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada

Objective: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. Materials and Methods: Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. Results: The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Discussion: The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. Conclusion: Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible. © 2023, CC BY.

关键词： Convolutional neural networks

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Refining the E+A Galaxy: A spatially resolved spectrophotometric sample of nearby post-starburst systems in SDSS-IV MaNGA (MPL-5)

arXiv

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arXiv 2021年

作者： Greene, Olivia A. Anderson, Miguel R. Marinelli, Mariarosa Holley-Bockelmann, Kelly Campbell, Lauren E.P. Liu, Charles T. Department of Physics & Astronomy Vanderbilt University PMB 401807 2301 Vanderbilt Place NashvilleTN37206 United States Department of Software Engineering Bloomberg LP 731 Lexington Avenue New YorkNY10022 United States Department of Planetarium Education Science Museum of Virginia 2500 West Broad Street RichmondVA23220 United States Department of Life & Physical Sciences Fisk University 1000 17th Avenue North NashvilleTN37208 United States Department of Physics & Astronomy City University of New York College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States Department of Astrophysics & Hayden Planetarium American Museum of Natural History Central Park West at 79th Street New YorkNY10024-5192 United States Physics Program The Graduate Center CUNY New YorkNY10016 United States

Post-starburst galaxies are crucial to disentangling the effect of star formation and quenching on galaxy demographics. They comprise, however, a heterogeneous population of objects, described in numerous ways. To obtain a well-defined and uncontaminated sample, we take advantage of spatially-resolved spectroscopy to construct an unambiguous sample of E+A galaxies - post-starburst systems with no observed ongoing star formation. Using data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey, in the fourth generation of the Sloan Digital Sky Survey (SDSS-IV), we have identified 30 E+A galaxies that lie within the green valley of color–stellar mass space. We first identified E+A candidates by their central, single-fiber spectra and (u-r) color from SDSS DR15, and then further required each galaxy to exhibit E+A properties throughout the entirety of the system to 3 effective radii. We describe our selection criteria in detail, note common pitfalls in E+A identification, and introduce the basic characteristics of the sample. We will use this E+A sample, which has been assembled with stringent criteria and thus re-establishes a well-defined sub-population within the broader category of post-starburst galaxies, to study the evolution of galaxies and their stellar populations in the time just after star formation within them is fully quenched. Copyright © 2021, The Authors. All rights reserved.

关键词： Galaxies

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The Jimmie Hamilton Award

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Naval Engineers Journal 1971年第3期83卷 36-38页

作者： NACHTSHEIM, JOHN J. BALLOU, L. DENNIS John J. Nachtsheim:is currently the Deputy Assistant Administrator for Research & Development for the Maritime Administration. His duties are the planning coordinating organizing evaluating and directing of the R&D activities of MarAd. His past experiences include: Naval Architect for the Naval Ship Engineering Center 1959 Deputy Chief Design Engineer for the Puget Sound Naval Shipyard 1958 to 1959 and Naval Architect the former Bureau of Ships 1948 to 1958. His education is comprised of a B.S. degree from the Webb Institute of Naval Architecture an L.L.B. degree from the George Washington University Law School completion of the Advanced Management Program at Harvard University and current study of Transportation at the American University. He is a Registered Professional Engineer in the District of Columbia and a Member of the Bar in the District of Columbia and the State of Maryland. In addition to ASNE his other professional memberships include the Society of Naval Architects and Marine Engineers the Society of Aeronautical Weight Engineers and the Association of Senior Engineers of the Naval Ships Systems Command (Honorary). USN Commander L. Dennis Ballou: USN is the Head of the Engineering Service Office Naval Ship Engineering Center. He is involved in computer hardware and software services to support engineering design automatic data processing systems design work study and quality assurance. Prior to NavSec duty Commander Ballou served in various billets afloat and ashore: tours on the USS Skagit and Tang supervision of the USS Skipjack's first overhul construction of the USS Nathanael Greene and helping to establish the Polaris overhaul program. He is a graduate of the U.S. Naval Academy Officers' Submarine School and the Webb Institute of Naval Architecture. He holds BS and MS degrees in marine engineering and naval architecture respectively. He has also completed many graduate

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ADVANCED AVIONICS ARCHITECTURE - THE NAVAIR STUDY

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NAVAL ENGINEERS JOURNAL 1994年第6期106卷 31-40页

作者： KATZ, RS JAHNKE, L JEWETT, CE Cdr. Larry Jahnke USN:is presently Head of the Architecture Branch of the Avionics Engineering division AIR-546 of the Naval Air Systems Command. Among his current responsibilities is to lead implementation activities of the NAVAIR Advanced Avionics Architecture study described in this paper. Cdr. Jahnke graduated from the University of Minnesota with a B.S. degree in aeronautical engineering and was commissioned in 1974. After flight training as a Naval flight officer he was assigned to Naval Air Station Barbers Point Hawaii where he served as Tactical Coordinator for P-3B aircraft. He was assigned to the Communications Directorate of the Joint Staff in 1990 where he participated in support of Desert Shield/Desert Storm and was part of the original cadre of officers responsible for the “C41 for the Warrior” concept. Cdr. Jahnke also has a Master of Science degree from the University of Southern California and is a 1990 graduate of the Industrial College of the Armed Forces. Cdr. Charles E. Jewett USN:is currently the Common Avionics Requirements Officer for Naval Aircraft Programs. He has served the Navy as an Aeronautical Engineering Duty Officer since 1982 with previous defense acquisition assignments as the Avionics Architecture and Engineering Branch Head Fighter/Attack Avionics systems Engineering Branch Head and A-12 Avionics Officer and A-6F Deputy Program Manager and the A-6 Avionics Officer. Cdr. Jewett entered the Navy as an Aviation Officer Candidate in 1971 receiving his commission and earning his wings as a Naval Flight Officer the same year. After graduating from the U.S. Naval Test Pilot School in 1976 he was assigned to the Strike Aircraft Test Directorate of the Naval Air Test Center where he participated in various electronic warfare electro-optics and software update evaluations for A-6 EA-6B and OV-10 aircraft. In Cdr. Jewett's previous assignment at NAVAIRSYSCOM he led a major Avionics Architecture Study (the subject of this paper) that surveyed cutting-edge avionics technol

To establish a planning basis for future avionics systems, the Naval Air Systems Command (NAVAIR) conducted an avionics architecture investigation during 1992-1993, culminating in a final report published in August 1993. In the course of the study, U.S. Industry provided significant information to a NAVAIR avionics database for both technologies and systems integration methods. From the study emerged an implementation strategy to allow NAVAIR to develop effective avionics systems in the future that use commercial products and standards where applicable but also allow the ready use of new and emerging technologies. Recommended strategies concentrate on the development process, especially the use of sound systems engineering techniques and the maximum practical use of commercial standards and products. This paper reviews the methodology employed during the NAVAIR investigation, and presents the key findings and resulting implementation strategies. The paper concludes with a brief summary of current implementation plans at NAVAIR.

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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RATIONALE FOR AN ADA software engineering ENVIRONMENT FOR NAVY MISSION CRITICAL APPLICATIONS

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NAVAL ENGINEERS JOURNAL 1984年第4期96卷 133-145页

作者： PAIGE, KK CONVERSE, RA USN LCdr. Kathleen K. Paige USN:graduated with a BA from the University of New Hampshire in 1970. She received her commission from Officer Candidate School in April 1971 and performed her first tour of duty with VFP-63 NAS Miramar. LCdr. Paige then received her MS from the Naval Post Graduate School in June 1976 and returned to San Diego to serve as Head Support Software Division at the Fleet Combat Direction System Support Activity. In May 1981 she reported to NA VSEA (PMS-408) where she served initially as Chairman of the NAVMAT Software Engineering Environment Working Group. She has been assigned as Deputy AN/UYK-43 Acquisition Manager since October 1981. LCdr. Paige was designated a fully qualified Engineering Duty Officer in December 1983. Robert A. Converse:is presently the Acquisition Manager for the Ada Language System/Navy (ALS/N) for the Naval Sea Systems Command Tactical Embedded Computer Resources Project. As such he is responsible for the definition and development of the ALS/N to be provided as a Navy standard computer programming system for Navy mission critical applications. Mr. Converse received a Bachelor of Science degree in Physics from Wheaton College Wheaton II. He spent fourteen years with the Naval Underwater Systems Center Newport Rhode Island during which time he designed and developed the Fortran compiler for the Navy Standard AN/UYK-7 computer. Also during that period he received a Master of Science degree in Computer Science from the University of Rhode Island. His thesis for that degree was entitled “Optimization Techniques for the NUSC Fortran Cross-Compiler”. Mr. Converse started his involvement with the Ada program in 1975 with the initial “Strawman” requirements review. Subsequently he was named as the Navy Ada Distinguished Reviewer and was intimately involved in the selection and refinement of the Ada language as it evolved to become ANSI/MIL-STD-1815A.

The U.S. Navy introduced the use of digital computers in mission critical applications over a quarter of a century ago. Today, virtually every system in the current and planned Navy inventory makes extensive use of computer technology. Computers embedded in mission critical Navy systems are integral to our strategic and tactical defense capabilities. Thus, the military power of the U.S. Navy is inextricably tied to the use of programmable digital computers. The computer program is the essential element that embodies the system “intelligence”. In addition, it provides the flexibility to respond to changing threats and requirements. However, this very flexibility and capability poses a host of difficulties hindering full realization of the advantages. This paper describes the lessons learned about computer program development over the past twenty five years and discusses a software engineering process that addresses these lessons. It then describes how Ada and its related Ada programming Support and Run-Time Environments foster this software engineering process to improve computer program productivity and achieve greater system reliability and adaptibility. Finally, the paper discusses how the use of Ada and its environments can enhance the interoperability and transferability of computer programs among Navy projects and significantly reduce overall life cycle costs for Navy mission critical computer programs.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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