MATILDA.FT：面向非均匀软物质的介观模拟软件包
MATILDA.FT, a Mesoscale Simulation Package for Inhomogeneous Soft Matter
AI总结：
本研究公开发布首款整合粗粒化分子动力学与场论模拟的GPU加速介观软物质模拟软件MATILDA.FT，支持多类系统建模，易扩展且配套工具完善，源码已开源。
摘要：
本文宣布公开发布一款大规模并行、GPU加速的软件，它是首个将粗粒化分子动力学与场论模拟整合在同一模拟包中的工具。MATILDA.FT（介观、加速、理论 informed、朗之万、耗散粒子动力学与场论的缩写）从底层设计为在支持CUDA的GPU上运行，借助Thrust库加速，能够利用大规模并行能力高效模拟介观尺度的系统。\nMATILDA.FT是一款多功能软件，用户可在同一软件内选择朗之万动力学或场论方法对系统建模。它已被用于建模多种系统，涵盖聚合物溶液、纳米颗粒-聚合物界面、粗粒化肽模型以及液晶。\nMATILDA.FT采用CUDA/C++编写，为面向对象架构，使其源代码易于理解和扩展。该软件配备专用的后处理与分析工具，以及详细文档和相关示例。下文我们概述了当前可用的功能，详细解释了并行算法与方法的逻辑，提供了必要的理论背景，并展示了近期以MATILDA.FT作为模拟引擎的研究项目示例。我们还演示了如何轻松扩展该代码，并介绍了未来的开发计划。源代码、文档、附加工具及示例可在GitHub仓库获取。
英文摘要：
In this paper we announce the public release of a massively-parallel, GPU-accelerated software, which is the first to combine both coarse-grained molecular dynamics and field-theoretical simulations in one simulation package. MATILDA.FT (Mesoscale, Accelerated, Theoretically-Informed, Langevin, Dissipative particle dynamics, and Field Theory) was designed from the ground-up to run on CUDA-enabled GPUs, with the Thrust library acceleration, enabling it to harness the possibility of massive parallelism to efficiently simulate systems on a mesoscopic scale. MATILDA.FT is a versatile software, enabling the users to use either Langevin dynamics or Field Theory to model their systems - all within the same software. It has been used to model a variety of systems, from polymer solutions, and nanoparticle-polymer interfaces, to coarse-grained peptide models, and liquid crystals. MATILDA.FT is written in CUDA/C++ and is object oriented, making its source-code easy to understand and extend. The software comes with dedicated post-processing and analysis tools, as well as the detailed documentation and relevant examples. Below, we present an overview of currently available features. We explain in detail the logic of parallel algorithms and methods. We provide necessary theoretical background, and present examples of recent research projects which utilized MATILDA.FT as the simulation engine. We also demonstrate how the code can be easily extended, and present the plan for the future development. The source code, along with the documentation, additional tools and examples can be found on GitHub repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
