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Related papers: Exploration of Quantum Computing in Materials Disc…

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High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale…

The theoretical investigation of non-adiabatic processes is hampered by the complexity of the coupled electron-nuclear dynamics beyond the Born-Oppenheimer approximation. Classically, the simulation of such reactions is limited by the…

Quantum Physics · Physics 2021-01-06 Pauline J. Ollitrault , Guglielmo Mazzola , Ivano Tavernelli

Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials…

Quantum Physics · Physics 2021-06-08 Alicia B. Magann , Matthew D. Grace , Herschel A. Rabitz , Mohan Sarovar

We propose a practical method for accurately evaluating molecular energies using a hybrid approach that integrates fault-tolerant quantum computers with classical computing. Our scheme comprises two complementary methods: quantum dominant…

Quantum Physics · Physics 2026-03-04 Nobuki Inoue , Hisao Nakamura

New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable…

Quantum Physics · Physics 2024-02-13 Mathieu Garrigues , Victor Onofre , Noé Bosc-Haddad

The real world obeys quantum physics and quantum computing presents an alternative way to map physical problems to systems that follow the same laws. Such computation fundamentally constitutes a better way to understand the most challenging…

Quantum Physics · Physics 2024-11-22 Elena Chachkarova , Terence Tse , Yordan Yordanov , Yao Wei , Cedric Weber

Due to its high energy efficiency and environmental friendliness, solid-state cooling based on the barocaloric (BC) effect represents a promising alternative to traditional refrigeration technologies relying on greenhouse gases. Plastic…

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex…

Engineering apparatus that harness quantum theory offers practical advantages over current technology. A fundamentally more powerful prospect is the long-standing prediction that such quantum technologies could out-perform any future…

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and…

Recent advances in quantum sensors, including atomic clocks, enable searches for a broad range of dark matter candidates. The question of the dark matter distribution in the Solar system critically affects the reach of dark matter direct…

High Energy Physics - Phenomenology · Physics 2022-12-08 Yu-Dai Tsai , Joshua Eby , Marianna S. Safronova

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the…

Understanding the evolution of electrified solid-liquid interfaces during electrochemical reactions is crucial. However, capturing the dynamic behavior of the interfaces with high temporal resolution and accuracy over long timescales…

Chemical Physics · Physics 2024-11-26 Letian Chen , Yun Tian , Xu Hu , Suya Chen , Huijuan Wang , Xu Zhang , Zhen Zhou

The ability of quantum computers to overcome the exponential memory scaling of many-body problems is expected to transform quantum chemistry. Quantum algorithms require accurate representations of electronic states on a quantum device, but…

Chemical Physics · Physics 2024-04-26 Hugh G. A. Burton

Complex systems with nonlinear response mechanisms can be applied as reservoir computers for energy-efficient machine learning tasks. Historically explored at the macro- and meso-scale, physical reservoir computing has recently been…

Disordered Systems and Neural Networks · Physics 2026-02-23 Guy Greenbaum , Will R. Branford , Andrew L. Goodwin

We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to…

Materials Science · Physics 2024-12-30 Olga Klimanova , Nikita Rybin , Alexander Shapeev

In this paper, we introduce a novel, adapted approach for computing gas adsorption properties in porous materials. We analyze the Dubinin-Polanyi's adsorption model and investigate various frameworks to estimate its required essential…

Materials Science · Physics 2023-10-25 F. Stavarache , A. Luna-Triguero , S. Calero , J. M. Vicent-Luna

Dynamics of adsorption and desorption of (4S)-N on amorphous solid water are analyzed using molecular dynamics simulations. The underlying potential energy surface was provided by machine-learned interatomic potentials. Binding energies…

Astrophysics of Galaxies · Physics 2020-09-22 Germán Molpeceres , Viktor Zaverkin , Johannes Kästner

Quantum computers promise significant speedups in solving problems intractable for conventional computers but, despite recent progress, remain limited in scaling and availability. Therefore, quantum software and hardware development heavily…

Quantum Physics · Physics 2023-11-08 Stefan Hillmich , Igor L. Markov , Robert Wille

Molecular hydrogen is at the core of hydrogen energy applications and has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use…

Materials Science · Physics 2025-01-22 Yasmine S. Al-Hamdani , Andrea Zen , Dario Alfé
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