☆ 4.8 Review

Recent Advances in First-Principles Based Molecular Dynamics

ACCOUNTS OF CHEMICAL RESEARCH (2022)

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Review Chemistry, Multidisciplinary

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr et al.

Summary: This review focuses on how machine learning is used to speed up excited-state simulations and advance the research field. Applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, and others.

CHEMICAL REVIEWS (2021)

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Article Chemistry, Physical

Reduced scaling formulation of CASPT2 analytical gradients using the supporting subspace method

Chenchen Song et al.

Summary: A new method for CASPT2 analytical gradients is proposed in this study, utilizing the supporting subspace method and MP2, Fock derivatives, which can calculate gradients more efficiently, reduce computational costs, and has wide applications in fields such as ab initio molecular dynamics simulations and geometry optimization.

JOURNAL OF CHEMICAL PHYSICS (2021)

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Review Chemistry, Multidisciplinary

Learning to Approximate Density Functionals

Bhupalee Kalita et al.

Summary: Density functional theory (DFT) calculations are widely used in scientific research, with machine learning (ML) offering new possibilities for improving the accuracy and usefulness of these calculations. ML has the potential to propose or enhance approximations for DFT, but challenges such as generalization remain to be addressed for implementing ML-designed functionals in standard codes.

ACCOUNTS OF CHEMICAL RESEARCH (2021)

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Review Chemistry, Multidisciplinary

Machine Learning Force Fields

Oliver T. Unke et al.

Summary: The use of machine learning in computational chemistry has led to significant advancements, particularly in the development of machine learning-based force fields to bridge the gap between accuracy and efficiency. The key concept is to learn the statistical relations between chemical structure and potential energy, without preconceived notions of fixed bonds. Challenges remain for the next generation of machine learning force fields.

CHEMICAL REVIEWS (2021)

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Article Chemistry, Physical

Uncertainty estimation for molecular dynamics and sampling

Giulio Imbalzano et al.

Summary: Machine-learning models are an effective strategy to bypass time-consuming electronic-structure calculations and enable accurate simulations of larger scale and complexity. Uncertainty quantification plays a crucial role in improving the accuracy and resilience of simulations, and can be applied to different types of structural and thermodynamic properties across diverse systems.

JOURNAL OF CHEMICAL PHYSICS (2021)

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Article Chemistry, Physical

Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

Lennard Boeselt et al.

Summary: QM/MM molecular dynamics simulations are computationally expensive due to the need to explicitly treat all valence electrons and perform a self-consistent field procedure. Machine-learned models show promise in reducing the computational cost and improving accuracy.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Chemistry, Physical

Weighted-Graph-Theoretic Methods for Many-Body Corrections within ONIOM: Smooth AIMD and the Role of High-Order Many-Body Terms

Juncheng Harry Zhang et al.

Summary: The weighted-graph approach presented in this paper offers an adaptive method to calculate contributions from many-body approximations in highly fluxional chemical systems for post-Hartree-Fock ab initio molecular dynamics. By dynamically combining graphs and considering a range of neighboring graphical representations during dynamics, the approach improves dynamic trajectories using lower-order many-body interaction terms. This method outperforms traditional approaches in terms of accuracy and cost-effectiveness when computing dynamical properties.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Chemistry, Physical

Linear-Scaling Open-Shell MP2 Approach: Algorithm, Benchmarks, and Large-Scale Applications

P. Bernat Szabo et al.

Summary: A linear-scaling local second-order Moller-Plesset (MP2) method based on restricted open-shell (RO) reference functions is presented for high-spin open-shell molecules. The approach achieves a good balance between computational cost, accuracy, and efficiency, making it applicable to open-shell systems of unprecedented size and complexity.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Review Chemistry, Multidisciplinary

Physics-Inspired Structural Representations for Molecules and Materials

Felix Musil et al.

Summary: This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, emphasizing the deep underlying connections between different frameworks that lead to computationally efficient and universally applicable models. It provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

CHEMICAL REVIEWS (2021)

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Article Computer Science, Interdisciplinary Applications

From a week to less than a day: Speedup and scaling of coordinate-scaled exact exchange calculations in plane waves

Martin P. Bircher et al.

COMPUTER PHYSICS COMMUNICATIONS (2020)

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Article Chemistry, Physical

FCHL revisited: Faster and more accurate quantum machine learning

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Article Chemistry, Physical

Dynamical matrix propagator scheme for large-scale proton dynamics simulations

Christian Dressler et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Article Chemistry, Physical

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units

Stefan Seritan et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Article Chemistry, Physical

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Paraskevi Gkeka et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

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Article Chemistry, Physical

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

Punyaslok Pattnaik et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

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Article Chemistry, Physical

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Ryosuke Jinnouchi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Multidisciplinary Sciences

Machine learning accurate exchange and correlation functionals of the electronic density

Sebastian Dick et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Multidisciplinary

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

Bing Huang et al.

NATURE CHEMISTRY (2020)

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Article Multidisciplinary Sciences

Quantum chemical accuracy from density functional approximations via machine learning

Mihail Bogojeski et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Physical

Molecular Dynamics with Very Large Time Steps for the Calculation of Solvation Free Energies

Charlles R. A. Abreu et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

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Article Chemistry, Physical

Operators in quantum machine learning: Response properties in chemical space

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

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Article Chemistry, Physical

Combining Iteration-Free Polarization with Large Time Step Stochastic-Isokinetic Integration

Alex Albaugh et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Chemistry, Physical

MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry

Jogvan Magnus Haugaard Olsen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Chemistry, Physical

Pushing the Limits of Multiple-Time-Step Strategies for Polarizable Point Dipole Molecular Dynamics

Louis Lagardere et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

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Article Chemistry, Physical

Extreme Scalability of DFT-Based QM/MM MD Simulations Using MiMiC

Viacheslav Bolnykh et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Chemistry, Multidisciplinary

Machine learning enables long time scale molecular photodynamics simulations

Julia Westermayr et al.

CHEMICAL SCIENCE (2019)

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Article Chemistry, Physical

A Versatile Multiple Time Step Scheme for Efficient ab Initio Molecular Dynamics Simulations

Elisa Liberatore et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

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Article Multidisciplinary Sciences

Towards exact molecular dynamics simulations with machine-learned force fields

Stefan Chmiela et al.

NATURE COMMUNICATIONS (2018)

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Article Chemistry, Multidisciplinary

Machine learning molecular dynamics for the simulation of infrared spectra

Michael Gastegger et al.

CHEMICAL SCIENCE (2017)

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Article Chemistry, Physical

A Stochastic, Resonance-Free Multiple Time-Step Algorithm for Polarizable Models That Permits Very Large Time Steps

Daniel T. Margul et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)

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Review Chemistry, Multidisciplinary

Mixed Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulations of Biological Systems in Ground and Electronically Excited States

Elizabeth Brunk et al.

CHEMICAL REVIEWS (2015)

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Article Chemistry, Physical

Is It Possible To Obtain Coupled Cluster Quality Energies at near Density Functional Theory Cost? Domain-Based Local Pair Natural Orbital Coupled Cluster vs Modern Density Functional Theory

Dimitrios G. Liakos et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

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Article Physics, Multidisciplinary

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Zhenwei Li et al.

PHYSICAL REVIEW LETTERS (2015)

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Article Chemistry, Physical

Multiple time step integrators in ab initio molecular dynamics

Nathan Luehr et al.

JOURNAL OF CHEMICAL PHYSICS (2014)

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Article Chemistry, Physical

Communication: Multiple-timestep ab initio molecular dynamics with electron correlation

Ryan P. Steele

JOURNAL OF CHEMICAL PHYSICS (2013)

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Article Chemistry, Physical

Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances

Joseph A. Morrone et al.

JOURNAL OF CHEMICAL PHYSICS (2011)

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Article Chemistry, Physical

Colored-Noise Thermostats a la Carte

Michele Ceriotti et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2010)

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Article Physics, Multidisciplinary

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

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Article Chemistry, Physical

Ab initio molecular dynamics using hybrid density functionals

Manuel Guidon et al.

JOURNAL OF CHEMICAL PHYSICS (2008)

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Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

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Article Chemistry, Physical

A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations

Luca Maragliano et al.

CHEMICAL PHYSICS LETTERS (2006)

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Article Chemistry, Physical

The multiple time step r-RESPA procedure and polarizable potentials based on induced dipole moments

M Masella

MOLECULAR PHYSICS (2006)

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Article Physics, Multidisciplinary

Long time molecular dynamics for enhanced conformational sampling in biomolecular systems

P Minary et al.

PHYSICAL REVIEW LETTERS (2004)

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Article Mathematics, Applied

Verlet-I/r-RESPA/Impulse is limited by nonlinear instabilities

Q Ma et al.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2003)

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Article Multidisciplinary Sciences

Escaping free-energy minima

A Laio et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)

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Article Chemistry, Physical

A Hamiltonian electrostatic coupling scheme for hybrid Car-Parrinello molecular dynamics simulations

A Laio et al.

JOURNAL OF CHEMICAL PHYSICS (2002)

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Article Chemistry, Physical

Canonical adiabatic free energy sampling (CAFES): A novel method for the exploration of free energy surfaces

J VandeVondele et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2002)

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Article Chemistry, Physical

Ab initio molecular dynamics:: Propagating the density matrix with Gaussian orbitals.: II.: Generalizations based on mass-weighting, idempotency, energy conservation and choice of initial conditions

SS Iyengar et al.

JOURNAL OF CHEMICAL PHYSICS (2001)

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