python gpu-accelerated molecular dynamics software (Molecular Dynamics Inc)
90
Structured Review
Molecular Dynamics Inc
python gpu-accelerated molecular dynamics software
Python Gpu Accelerated Molecular Dynamics Software, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/python+gpu+accelerated+molecular+dynamics+software/gpu+accelerated+molecular+dynamics+simulations/pm39960300-146-15-17
Average 90 stars, based on 1 article reviews
Python Gpu Accelerated Molecular Dynamics Software, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/python+gpu+accelerated+molecular+dynamics+software/gpu+accelerated+molecular+dynamics+simulations/pm39960300-146-15-17
Average 90 stars, based on 1 article reviews
python gpu-accelerated molecular dynamics software - by Bioz Stars,
2026-10
90/100 stars
Images
Related Articles
other:Article Title: Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems. Article Snippet: Get e-Alerts MOLECULAR MECHANICS | February 27, 2025 Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems , , , , , , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2501–2514 https://doi.org/10.1021/acs.jctc.4c01449 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Zhaoxin Xie Yanheng Li Yijie Xia Jun Zhang Sihao Yuan Cheng Fan Yi Isaac Yang* Yi Qin Gao* Open PDF Supporting Information (1) Article Views 924 Altmetric 3 Citations Learn about these metrics Published February 27, 2025 5/29/25, 12:56 PM Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems | Journal of Chemical Theory and Comp... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01449 2/36 Inspired by the QM/MM methodology, the ML/MM approach introduces a new opportunity for multiscale simulation, improving the balance between accuracy and computational e ciency. Article Title: Diffusive to Barrier-Limited Transition in the Aqueous Ion Transport through Nanoporous 2D Materials Article Snippet: Article Title: The Atomic Density-Based Tight-Binding (aTB) Model: A Robust and Accurate Semiempirical Method Parametrized for H-Ra; Applied to Structures, Vibrational Frequencies, Noncovalent Interactions, and Excited States. Article Snippet: This work introduces a semiempirical method, named aTB, based on the tight-binding model and named for its zero-order Hamiltonian that utilizes density-fitting atomic densities.. This method can calculate the molecular structure, vibrational frequencies, noncovalent interactions, and excited states of large molecular systems.. The parameters of aTB cover elements from Hydrogen (H) to Radium (Ra), and for ground state calculations, it supports the analysis of firstand second-order derivatives. Article Title: Binding of Soluble Ligands to Membrane Receptors: A Molecular Dynamics Simulation Study. Article Snippet: MD simulations of the above coarsegrained model were performed using the Article Title: Core Flipping in Lead Optimization: Rank Ordering Using λ-Dynamics. Article Snippet: 2022, 18, 3894−3910. (24) Wang, K.; Chodera, J. D.; Yang, Y.; Shirts, M. R. Identifying Ligand Binding Sites and Poses Using Article Title: Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems. Article Snippet: Article Title: Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics. Article Snippet: We introduce an innovative machine learning (ML)-based framework for multiscale molecular modeling in which the ML subsystem is treated as an electrostatic entity interacting with its molecular mechanics (MM) environment through classical electrostatics.. The integration of ML accuracy with multiscale modeling is accomplished by leveraging the capabilities of the ANI neural networks to predict geometry-dependent atomic partial charges at the minimal basis iterative stockholder (MBIS) level, going beyond static mechanical embedding.. This ML/MM approach can closely approximate state-of-the-art multiscale quantum-classical (QM/MM) methods while significantly lowering computational requirements, thereby facilitating more efficient and precise simulations in computational chemistry. Article Title: Screening and Design of Aqueous Zinc Battery Electrolytes Based on the Multimodal Optimization of Molecular Simulation. Article Snippet: Aqueous batteries, such as aqueous zinc-ion batteries (AZIB), have garnered significant attention because of their advantages in intrinsic safety, low cost, and eco-friendliness.. However, aqueous electrolytes tend to freeze at low temperatures, which limits their potential industrial applications.. Thus, one of the core challenges in aqueous electrolyte design is optimizing the formula to prevent freezing while maintaining good ion conductivity. |