Energy-conserving molecular dynamics is not energy conserving

Zhang, Lina; Hou, Yifan; Ge, Fuchun; Dral, Pavlo O.

doi:10.1039/d3cp03515h

Cited by 5 publications

(20 citation statements)

References 58 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have previously shown that the high quality of the AIQM1 method results in rather accurate IR spectra obtained from MD simulations compared to spectra obtained with a representative DFT (which is also substantially slower; see example in Figure ) or a semiempirical QM method.…”

Section: Simulationsmentioning

confidence: 99%

“…MLatom has a native implementation of MD supporting any kind of model that provides forces, not necessarily conservative . Currently, simulations in NVE and NVT ensembles, based on the velocity Verlet algorithm, are possible.…”

Section: Simulationsmentioning

confidence: 99%

“…The Nosé–Hoover thermostat is a deterministic thermostat that couples the system to a thermal bath through extra terms in the Hamiltonian. Its theory and implementation details are described elsewhere . Here, we briefly mention the relevant methodology , used in the Andersen thermostat.…”

Section: Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Dral,

Ge,

Hou

et al. 2024

J. Chem. Theory Comput.

Self Cite

View full text Add to dashboard Cite

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

show abstract

Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Dral,

Ge,

Hou

et al. 2024

J. Chem. Theory Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, our related study on learning the sequential information in the dissipative quantum dynamics revealed that the specialized NN models are not necessarily better choices than simpler models such as feed-forward NN used in the GICnet. 43 Long-time trajectory propagation is a challenge even for traditional MD, especially propagated with 3D-MLPs, 44,45 because the molecules sometimes unphysically explode as we also observed in many reference trajectories during the preparation of the training set (unphysical explosions happen because the 3D-MLP does not describe a dissociation part of PES correctly by severely underestimating the potential energies; 57 we removed such exploding reference trajectories and did not use them for training; see the Supporting Information). Thus, when we trained the preliminary versions of the GICnet models without physical constraints introduced below, we observed that the molecules may explode during long-time MD.…”

mentioning

confidence: 99%

“…The simulations can be performed online using the MLatom@XACS () cloud computing service. MLatom implementation of MD with 3D-MLPs is also publicly available as described in ref .…”

mentioning

confidence: 99%

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

Ge,

Zhang,

Hou

et al. 2023

J. Phys. Chem. Lett.

Self Cite

View full text Add to dashboard Cite

We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.

show abstract

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods

Zhang,

Pios,

Martyka

et al. 2024

J. Chem. Theory Comput.

Self Cite

View full text Add to dashboard Cite

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau−Zener−Belyaev−Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-thebox. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

show abstract

Energy-conserving molecular dynamics is not energy conserving

Abstract: Molecular dynamics (MD) is a widely-used tool for simulating the molecular and materials properties. It is a common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots...

Cited by 5 publications

References 58 publications

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods

Contact Info

Product

Resources

About