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Accounting for nuclear quantum effects (NQEs) can significantly alter material properties at finite temperatures. Atomic modeling using the path-integral molecular dynamics (PIMD) method can fully account for such effects, but requires…

Materials Science · Physics 2025-05-21 A. A. Solovykh , N. E. Rybin , I. S. Novikov , A. V. Shapeev

An accurate treatment of the structures and dynamics that lead to enhanced chemical reactivity in enzymes requires explicit treatment of both electronic and nuclear quantum effects. The former can be captured in ab initio molecular dynamics…

Chemical Physics · Physics 2022-08-26 Lu Wang , Christine M. Isborn , Thomas E. Markland

Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture…

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability…

It is now established that nuclear quantum motion plays an important role in determining water's hydrogen bonding, structure, and dynamics. Such effects are important to include in density functional theory (DFT) based molecular dynamics…

Chemical Physics · Physics 2018-12-20 Daniel C. Elton , Michelle Fritz , M. -V. Fernández-Serra

For nearly the past 30 years, Centroid Molecular Dynamics (CMD) has proven to be a viable classical-like phase space formulation for the calculation of quantum dynamical properties. However, calculation of the centroid effective force…

Chemical Physics · Physics 2022-09-15 Timothy D. Loose , Patrick G. Sahrmann , Gregory A. Voth

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with…

Computational Physics · Physics 2020-09-15 Weile Jia , Han Wang , Mohan Chen , Denghui Lu , Lin Lin , Roberto Car , Weinan E , Linfeng Zhang

The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to…

Computational Physics · Physics 2026-02-06 Porhouy Minh , Sapna Sarupria

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data.…

Computational Physics · Physics 2018-04-11 Linfeng Zhang , Jiequn Han , Han Wang , Roberto Car , Weinan E

Conventional molecular dynamics (MD) simulation approaches, such as $\textit{ab initio}$ MD (AIMD) and empirical force field MD (EFFMD), face significant trade-offs between physical accuracy and computational efficiency. This work presents…

Disordered Systems and Neural Networks · Physics 2026-05-12 Hongyu Yan , Yong Wei , Minghan Chen , Hanning Chen

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead…

Chemical Physics · Physics 2024-07-08 Iryna Zaporozhets , Félix Musil , Venkat Kapil , Cecilia Clementi

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to…

We present an accelerated ``on-the-fly'' coupled-cluster path-integral molecular dynamics (PIMD) method for finite-temperature simulations in which electron correlation and nuclear quantum effects are treated simultaneously. The approach is…

Chemical Physics · Physics 2026-05-20 Thomas Spura , Hossam Elgabarty , Thomas D. Kühne

A lot of progress has been made in recent times for simulating accurately the ground state energy of small molecules and their potential energy surface, using quantum-classical hybrid computing architecture. While these single point energy…

Quantum Physics · Physics 2022-10-18 Nirmal M R , Shampa Sarkar , Manoj Nambiar , Sriram Goverapet Srinivasan

Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in…

Materials Science · Physics 2014-10-14 Venkatesh Botu , Rampi Ramprasad

Quantum effects due to the spatial delocalization of light atoms are treated in molecular simulation via the path integral technique. Among several methods, Path Integral (PI) Molecular Dynamics (MD) is nowadays a powerful tool to…

Chemical Physics · Physics 2015-09-30 Animesh Agarwal , Luigi Delle Site

Path integral-based simulation methodologies play a crucial role for the investigation of nuclear quantum effects by means of computer simulations. However, these techniques are significantly more demanding than corresponding classical…

Statistical Mechanics · Physics 2018-01-17 Karsten Kreis , Kurt Kremer , Raffaello Potestio , Mark E. Tuckerman

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales…

The development and implementation of increasingly accurate methods for electronic structure calculations mean that, for many atomistic simulation problems, treating light nuclei as classical particles is now one of the most serious…

Materials Science · Physics 2016-02-12 Venkat Kapil , Joost VandeVondele , Michele Ceriotti

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as…

Chemical Physics · Physics 2018-09-26 Stefan Chmiela , Huziel E. Sauceda , Klaus-Robert Müller , Alexandre Tkatchenko
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