.png){kind=icon}
返回
ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields
.png){kind=icon}
PRE
AI
DOI：10.1063/5.0247559.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine Learning Force Fields (MLFFs) require ongoing improvement and innovation to effectively address challenges across various domains. Developing MLFF models typically involves extensive screening, tuning, and iterative testing. However, existing packages based on a single mature descriptor or model are unsuitable for this process. Therefore, we developed a package named ABFML, based on PyTorch, which aims to promote MLFF innovation by providing developers with a rapid, efficient, and user-friendly tool for constructing, screening, and validating new force field models. Moreover, by leveraging standardized module operations and cutting-edge machine learning frameworks, developers can swiftly establish models. In addition, the platform can seamlessly transition to the graphics processing unit environments, enabling accelerated calculations and large-scale parallel simulations of molecular dynamics. In contrast to traditional from-scratch approaches for MLFF development, ABFML significantly lowers the barriers to developing force field models, thereby expediting innovation and application within the MLFF development domains.
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
.png){kind=icon}
PREAI
分享收藏分享收藏GPU-accelerated artificial neural network potential for molecular dynamics simulationGPU加速的人工神经网络在分子动力学模拟中的应用潜力
PREAI
分享收藏分享收藏DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicsDeepmd-kit: 用于多体势能表示和分子动力学的深度学习软件包
OAAI
OAAI分享收藏CHGNet as a pretrained universal neural network potential for charge-informed atomistic modellingCHGNet作为预训练的通用神经网络，具有用于电荷信息原子建模的潜力
OAAI
OAAI分享收藏Atom-centered symmetry functions for constructing high-dimensional neural network potentials以原子为中心的对称函数构造高维神经网络势
PREAI
分享收藏
查看更多内容