Recent Advances in First-Principles Based Molecular Dynamics

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When two objects are rubbed together, they create friction, heat, and their surfaces might be worn away. However, the fundamental mechanisms behind these processes remain poorly understood. While empirical engineering models exist, they are tailored to spe ...

The world of nanoscales in fluidics is the frontier where the continuum of fluid mechanics meets the atomic, and even quantum, nature of matter. While water dynamics remains largely classical under extreme confinement, several experiments have recently rep ...

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotro ...

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis stat ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of phy ...

Type I fimbriae are the main adhesive organelles of uropathogenic Escherichia coli (UPEC), consisting of four different subunits. Their component with the most important role in establishing bacterial infections is the FimH adhesin located at the fimbrial ...

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction-namely, the metal-gas, metal-liquid, and metal-support interfaces. Indeed, ...

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel imp ...

Algorithms are everywhere. The recipe for the frangipane cake is an algorithm. If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven. Now, wh ...

Dispersion of objects in a fluid phase can be classified as solutions (Gibbs free energy of mixing, Delta G(mix) < 0) or suspensions (Delta G(mix) > 0) depending on their thermodynamic stability. Small objects tend to form solutions, larger ones suspension ...

Recent Advances in First-Principles Based Molecular Dynamics

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Wear across scales

Momentum tunnelling between nanoscale liquid flows

Advances in the modeling and simulation of NTPs using OpenFOAM and OpenModelica

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Predicting protein interactions using geometric deep learning on protein surfaces

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Towards Trustworthy Deep Learning for Image Reconstruction

DeePMD-kit v2: A software package for deep potential models

Bacterial Lectin FimH and Its Aggregation Hot-Spots: An Alternative Strategy against Uropathogenic Escherichia coli

Deep Learning Meets Sparse Regularization

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

Practical Byzantine-resilient Stochastic Gradient Descent

Experimental Method to Distinguish between a Solution and a Suspension