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We recently proposed a method coupling quantum mechanics (QM) methods and molecular density functional theory (MDFT) to describe mixed quantum-classical systems [J. Chem. Phys. 161, 014113 (2024)]. This approach is particularly appropriate…

Chemical Physics · Physics 2025-11-21 Maxime Labat , Guillaume Jeanmairet , Emmanuel Giner

Density-functional theory (DFT) has revolutionized computer simulations in chemistry and material science. A faithful implementation of the theory requires self-consistent calculations. However, this effort involves repeatedly diagonalizing…

Quantum Physics · Physics 2023-07-17 Taehee Ko , Xiantao Li , Chunhao Wang

We present a quantum mechanical / molecular mechanics (QM/MM) to tackle chemical reactions with substantial molecular reorganization. For this, molecular dynamics simulations with smoothly switched interaction models are used to suggest…

Soft Condensed Matter · Physics 2020-11-06 Robert H. Meißner , Julian Konrad , Benjamin Boll , Bodo Fiedler , Dirk Zahn

A novel parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory…

There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generalizability, and interpretability of the recently proposed…

Chemical Physics · Physics 2022-03-14 Thijs Stuyver , Connor W. Coley

Recent years have seen vast improvements in the ability of rigorous quantum-mechanical methods to treat systems of interest to molecular biology. In this review article, we survey common computational methods used to study such large,…

Biological Physics · Physics 2012-07-05 Brian Kolb , T. Thonhauser

Quantum-mechanical methods are widely used for understanding molecular interactions throughout biology, chemistry, and materials science. Quantum diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple…

Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density…

Chemical Physics · Physics 2024-06-26 Hao Tang , Brian Xiao , Wenhao He , Pero Subasic , Avetik R. Harutyunyan , Yao Wang , Fang Liu , Haowei Xu , Ju Li

High-throughput approximations of quantum mechanics calculations and combinatorial experiments have been traditionally used to reduce the search space of possible molecules, drugs and materials. However, the interplay of structural and…

Quantum Physics · Physics 2019-10-29 Alain Tchagang , Julio Valdés

Density functional theory (DFT), the most widely adopted method in modern computational chemistry, fails to describe accurately the electronic structure of strongly correlated systems. Here we show that DFT can be formally and practically…

Chemical Physics · Physics 2022-04-18 Daniel Gibney , Jan-Niklas Boyn , David A. Mazziotti

There are several important solid-state systems, such as defects in solids, superconducting circuits and molecular qubits, for attractive candidates of quantum computations. Molecular qubits, which benefit from the power of chemistry for…

Quantum Physics · Physics 2017-06-29 Yingqiu Dai , Zhifu Shi , Yue Fu , Xi Qin , Shiwei Mu , Yang Wu , Ji-Hu Su , Lei Qin , Yuan-Qi Zhai , Yi-Fei Deng , Xing Rong , Jiangfeng Du

Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials…

Chemical Physics · Physics 2026-04-03 Raphael T. Husistein , Markus Reiher

A chemical reaction mechanism (CRM) is a sequence of molecular-level events involving bond-breaking/forming processes, generating transient intermediates along the reaction pathway as reactants transform into products. Understanding such…

Chemical Physics · Physics 2024-07-16 Ajnabiul Hoque , Manajit Das , Mayank Baranwal , Raghavan B. Sunoj

Conventional quantum chemical solvation theories are based on the mean-field embedding approximation. That is, the electronic wavefunction is calculated in the presence of the mean field of the environment. In this paper a direct quantum…

Chemical Physics · Physics 2009-11-13 Takeshi Yamamoto

Kohn-Sham density functional theory (DFT) is the workhorse of quantum chemistry, offering an attractive balance between accuracy and computational cost. Although exact in principle, DFT in practice relies on an approximation to the unknown…

Quantum Physics · Physics 2026-05-12 Karim K. Alaa El-Din , Antonius v. Strachwitz , Sam M. Vinko

A density functional theory (DFT) framework is presented that links functional derivatives of free-energy functionals to non-linear static density response functions in quantum many-body systems. Within this framework, explicit expressions…

A new density functional theory (DFT) exchange-correlation functional for the exploration of reaction mechanisms is proposed. This new functional, denoted BMK (Boese-Martin for Kinetics), has an accuracy in the 2 kcal/mol range for…

Chemical Physics · Physics 2007-05-23 A. Daniel Boese , Jan M. L. Martin

A QM/MM implementation for periodic systems is reported. This is done for the case of molecules and for systems with two and three-dimensional periodicity, which is suitable to model electrolytes in contact with electrodes. Tests on…

Materials Science · Physics 2015-11-12 K. Doll , T. Jacob

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is…

Machine Learning · Statistics 2024-03-12 He Zhang , Siyuan Liu , Jiacheng You , Chang Liu , Shuxin Zheng , Ziheng Lu , Tong Wang , Nanning Zheng , Bin Shao

Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular representations that effectively generalize in real-world scenarios…

Machine Learning · Computer Science 2024-05-28 Jose Arjona-Medina , Ramil Nugmanov