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

Gkeka, Paraskevi; Stoltz, Gabriel; Farimani, Amir Barati; Belkacemi, Zineb; Ceriotti, Michele; Chodera, John D.; Dinner, Aaron R.; Ferguson, Andrew L.; Maillet, Jean‐Bernard; Minoux, Hervé; Peter, Christine; Pietrucci, Fabio; Silveira, Ana; Tkatchenko, Alexandre; Trstanova, Zofia; Wiewiora, Rafal; Lelièvre, Tony

doi:10.1021/acs.jctc.0c00355

Cited by 161 publications

(171 citation statements)

References 223 publications

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“…Not surprisingly, significant progress has been made in this regard as summarized by recent excellent reviews. [98][99][100][101][102][103][104] Cutoff and attention to local interactions remains the DC strategy for development of machine learning potentials.…”

Section: Machine Learning Improves "Caching"mentioning

confidence: 99%

"Dividing and Conquering" and "Caching" in Molecular Modeling

Cao¹,

Tian²

2021

Preprint

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Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

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Section: Machine Learning Improves "Caching"mentioning

confidence: 99%

"Dividing and Conquering" and "Caching" in Molecular Modeling

Cao¹,

Tian²

2021

Preprint

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“…The only solution to that problem is avoiding (1) and employing representations of the energy with descriptors capable to describe long-range interactions. We do not go into details here but refer to current developments in this respect (Chmiela et al, 2017;Grisafi and Ceriotti, 2019;Gkeka et al, 2020). The second aspect involves the representation of the atomic structure and therefrom incurred short-range manybody interactions.…”

Section: Challenges For Mlffmentioning

confidence: 99%

Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces

Paier

2020

Front. Chem.

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The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach. This work applies the recently published on-the-fly machine-learned force field techniques using a variant of the Gaussian approximation potentials combined with Bayesian regression and molecular dynamics as efficiently implemented in the Vienna ab initio simulation package, VASP. The generation of these force fields follows active-learning schemes. We apply these force fields to simple oxides such as MgO and more complex reducible oxides such as iron oxide, examine their generalizability, and further increase complexity by studying water adsorption on these metal oxide surfaces. We successfully examined surface properties of pristine and reconstructed MgO and Fe3O4 surfaces. However, the accurate description of water–oxide interfaces by machine-learned force fields, especially for iron oxides, remains a field offering plenty of research opportunities.

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“…Meanwhile, deep learning has had a major impact on many areas of biology, achieving state of the art performance in fields such as protein structure prediction [10]. A number of groups have applied these advances to molecular simulations [11, 12, 13] including learning coarsegrained potentials [14, 15, 16, 17, 18], learning quantum mechanical potentials [19, 20, 21, 22], improving sampling [23], and improving atom typing [24]. Whilst promising, many of these approaches show limited success when used on systems they were not trained on.…”

Section: Introductionmentioning

confidence: 99%

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Greener

Jones

2021

Preprint

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Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of roteins are discussed along with possible solutions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

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

Cited by 161 publications

References 223 publications

"Dividing and Conquering" and "Caching" in Molecular Modeling

"Dividing and Conquering" and "Caching" in Molecular Modeling

Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

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