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Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of ab initio theory and/or to apply highly expensive…

Ab initio molecular dynamics (AIMD) is a powerful tool to predict properties of molecular and condensed matter systems. The quality of this procedure is based on accurate electronic structure calculations. The development of quantum…

We have recently developed a breakthrough methodology for rapidly computing entropy in both solids and liquids by integrating a multiscale entropy approach (known as zentropy theory) with molecular dynamics (MD) simulations. This approach…

We demonstrate an efficient and accurate, general-purpose first-principles blueprint for calculating anharmonic vibrational free energy and predicting structural phase transition temperatures of solids. Thermodynamic integration is…

Materials Science · Physics 2024-03-14 Junsoo Park , Zhigang Wu , John W. Lawson

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

This thesis describes several topics related to finite temperature studies of strongly correlated systems: finite temperature density matrix embedding theory (FT-DMET), finite temperature metal-insulator transition, and quantum algorithms…

Strongly Correlated Electrons · Physics 2023-03-01 Chong Sun

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

In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density…

Plasma Physics · Physics 2024-01-30 Tao Chen , Qianrui Liu , Yu Liu , Liang Sun , Mohan Chen

Molecular design requires systematic and broadly applicable methods to extract structure-property relationships. The focus of this study is on learning thermodynamic properties from molecular-liquid simulations. The methodology relies on an…

Materials Science · Physics 2023-07-04 Bernadette Mohr , Diego van der Mast , Tristan Bereau

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and…

Materials Science · Physics 2023-10-16 Olga Klimanova , Timofei Miryashkin , Alexander Shapeev

Starting from the Helmholtz free energy we calculate analytically first- and second-order derivatives, as internal energy and specific heats, for the ideal system and the exchange and correlation interactions covering a broad range of…

Astrophysics · Physics 2007-05-23 W. Stolzmann , T. Bloecker

Variational methods are a common approach for computing properties of ground states but have not yet found analogous success in finite temperature calculations. In this work we develop a new variational finite temperature algorithm (VAFT)…

Strongly Correlated Electrons · Physics 2017-05-17 Jahan Claes , Bryan K. Clark

Using conceptually and procedurally consistent density functional theory (DFT) calculations with an advanced meta-GGA exchange-correlation functional in ab initio molecular dynamics simulations, we determine the insulator-metal transition…

Accounting for electrons and nuclei simultaneously is a powerful capability of ab initio molecular dynamics (AIMD). However, AIMD is often unable to accurately reproduce properties of systems such as water due to inaccuracies in the…

Chemical Physics · Physics 2017-01-25 Andrew D. White , Chris Knight , Glen M. Hocky , Gregory A. Voth

Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anharmonicity and liquids, while molecular dynamics (MD) is…

Materials Science · Physics 2025-11-19 Ekaterina Spirande , Timofei Miryashkin , Andrei Kolmakov , Alexander Shapeev

We discuss how to include our recently proposed thermopotentiostat technique [Phys. Rev. Lett. 126, 136803 (2021)] into any existing ab initio molecular dynamics (AIMD) package. Using thermopotentiostat AIMD simulations in the canonical…

Materials Science · Physics 2022-09-12 F. Deißenbeck , S. Wippermann

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…

Ultra-fast electronic phenomena originating from finite temperature, such as nonlinear optical excitation, can be simulated with high fidelity via real-time time dependent density functional theory (rt-TDDFT) calculations with hybrid…

Materials Science · Physics 2025-01-07 Rongrong Liu , Zhuoqiang Guo , Qiuchen Sha , Tong Zhao , Haibo Li , Wei Hu , Lijun Liu , Guangming Tan , Weile Jia

Recently, we developed a method to construct polynomial interatomic potentials from ab-initio calculations in order to accurately describe laser excited solids [PRL 124, 085501 (2020)]. However, ab-initio methods, and therefore analytical…

Materials Science · Physics 2021-10-07 Bernd Bauerhenne , Martin E. Garcia

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) has become a workhorse for studying the structure, dynamics, and reactions in condensed matter systems. Currently, AIMD simulations are primarily carried out at…

Chemical Physics · Physics 2025-06-10 Ritama Kar , Sagarmoy Mandal , Vaishali Thakkur , Bernd Meyer , Nisanth N. Nair
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