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Found 24 papers, 9 papers with code
On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials
In this letter, we present an extension to TensorNet, a state-of-the-art equivariant Cartesian tensor neural network potential, allowing it to handle charged molecules and spin states without architectural changes or increased costs.
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge.
PlayMolecule Viewer: a toolkit for the visualization of molecules and other data
PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts.
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation.
Machine Learning Small Molecule Properties in Drug Discovery
Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery.
Top-down machine learning of coarse-grained protein force-fields
Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales.
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.
Ranked #1 on
Formation Energy
on QM9
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics
Intrinsically disordered proteins participate in many biological processes by folding upon binding with other proteins.
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on.
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials
The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed.
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.
Equivariant Transformers for Neural Network based Molecular Potentials
The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed.
Efficient Reinforcement Learning Experimentation in PyTorch
Abstract: Deep reinforcement learning (RL) has proved successful at solving challenging environments but often requires long training times and very many samples.
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Solving sparse reward tasks through exploration is one of the major challenges in deep reinforcement learning, especially in three-dimensional, partially-observable environments.
Integrating Distributed Architectures in Highly Modular RL Libraries
In this work, we explore design choices to allow agent composability both at a local and distributed level of execution.
Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning.
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning
We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2).
Chemical Physics Biological Physics Computational Physics
Machine Learning of coarse-grained Molecular Dynamics Force Fields
We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface.
Dimensionality reduction methods for molecular simulations
Molecular simulations produce very high-dimensional data-sets with millions of data points.
