Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

Shi, Yu; Lam, Stephen T.; Beck, Thomas L.

doi:10.1039/d2sc02227c

Cited by 6 publications

(5 citation statements)

References 72 publications

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“…DeePMD-kit 55 is an example of a soware package, which implements this architecture. While this type of approach has shown promise in various applications 52,[56][57][58][59][59][60][61][62][63] it can have large training data requirements, which limits its usefulness as the generation of sufficiently large training data is still computationally very demanding.…”

Section: Neural Network Potential Molecular Dynamics (Nnp-md)mentioning

confidence: 99%

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

2022

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Section: Neural Network Potential Molecular Dynamics (Nnp-md)mentioning

confidence: 99%

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

2022

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“…1) Accurate Modeling: Quantum simulation accounts for the quantum behavior of molecules, enabling more accurate predictions of their interaction with each other and with biological systems [32]. 2) Understanding Complex Reactions: Quantum simulation can provide insights into chemical reactions and processes vital for drug development, such as enzyme interactions and protein folding [33].…”

Section: Quantum Simulations In Drug Discoverymentioning

confidence: 99%

Recent Advances in Quantum Computing for Drug Discovery and Development

Kumar,

Yadav,

Mukherjee

et al. 2024

IEEE Access

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The preservation of human health is of utmost importance, and unrestricted availability of medications is essential for the sustenance of overall wellness. Pharmaceuticals, which consist of a wide range of therapeutic substances utilized to diagnose, treat, and improve various diseases and conditions, play a crucial part in the field of healthcare. However, the drug research and development process is widely recognized for its lengthy duration, demanding nature, and substantial expenses. To enhance the effectiveness of this complex process, interdisciplinary groups have converged, giving rise to the field known as "Bioinformatics". The emergence and future advancements of Quantum Computing (QC) technologies have the potential to significantly enhance and accelerate the complex process of drug discovery and development. This paper explores various disciplines, such as Computer-Aided Drug Design (CADD), quantum simulations, quantum chemistry, and clinical trials, that stand to gain significant advantages from the rapidly advancing field of quantum technology. This study aims to explore a range of fundamental quantum principles, intending to facilitate a thorough understanding of this revolutionary technology.

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“…DeePMD-kit 55 is an example of a software package, which implements this architecture. While this type of approach has shown promise in various applications 52,[56][57][58][59][59][60][61][62][63] it can have large training data requirements, which limits its usefulness as the generation of sufficiently large training data is still computationally very demanding.…”

Section: Architecturesmentioning

confidence: 99%

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks.

Zhang

Pagotto

Duignan

2022

Preprint

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Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon dioxide conversion/capture. Unfortunately, the determination of even their most basic properties from first principles remains an unsolved problem. As a result, the discovery and optimisation of electrolyte solutions for these applications largely relies on chemical intuition, experimental trial and error or empirical models. The challenge is that the dynamic nature of liquid electrolyte solutions require long simulation times to generate trajectories that sufficiently sample the configuration space; the long range electrostatic interactions require large system sizes; while the short range quantum mechanical (QM) interactions require an accurate level of theory. Fortunately, recent advances in the field of deep learning, specifically neural network potentials (NNPs), can enable significant accelerations in sampling the configuration space of electrolyte solutions. Here, we outline the implications of these recent advances for the field of materials chemistry and identify outstanding challenges and potential solutions.

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Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

Abstract: With dual goals of efficient and accurate modeling of solvation thermodynamics in molten salt liquids, we employ ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and...

Cited by 6 publications

References 72 publications

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

Recent Advances in Quantum Computing for Drug Discovery and Development

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks.

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