A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy

Pretti, Evan; Shell, M. Scott

doi:10.1063/5.0057104

Cited by 29 publications

(64 citation statements)

References 93 publications

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“…Furthermore, the depth decreases with temperature, showing a clear temperature dependence of the pair potential. This observation agrees with the water potentials obtained in the past literature 71,72 , suggesting that an explicit temperature dependence is required for the pair potential to be transferable.…”

Section: Learning Transferable Coarse-grained Potentialssupporting

confidence: 90%

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Learning Pair Potentials using Differentiable Simulations

Wang¹,

Wu²,

Gómez‐Bombarelli³

2022

Preprint

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Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared to traditional methods like Iterative Boltzmann Inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.

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Section: Learning Transferable Coarse-grained Potentialssupporting

confidence: 90%

“…Specifically, we optimize pair potentials with explicit temperature dependence. Inspired by previous work studying the energy and entropy decomposition of CG potentials, 72,73 we propose the following form of neural pair potential.…”

Section: Learning Transferable Coarse-grained Potentialsmentioning

confidence: 99%

Learning Pair Potentials using Differentiable Simulations

Wang¹,

Wu²,

Gómez‐Bombarelli³

2022

Preprint

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“…In contrast to the force-based metric, Shell and co-workers have identified and implemented the information-theoretic relative entropy as a target metric. − Relative entropy is defined as the differences between the FG and CG probability distributions, given by the Kullback–Leibler divergence where ⟨ S map ⟩ CG denotes the mapping entropy (introduced in Section ) defined by a mapping operator, S map = ln ∫δ[ M ( r ) – R ] d r . Thus, minimizing S rel enforces minimizing the log difference between the FG and CG probability distributions.…”

Section: Basics Of Bottom-up Coarse-grained Modelingmentioning

confidence: 99%

“…To complement these experimental approaches, theorists leverage classical molecular dynamics (MD) simulations to investigate dynamical phenomena at high spatial resolution, most commonly at the atomistic level . However, within the space of MD simulation techniques, coarse-grained (CG) modeling and simulation are particularly attractive for the study of systems with hierarchical length and time scales such as biomolecular systems − (including UNRES, − OPEP, , PRIMO, SIRAH, , MARTINI, − MS-CG, − and REM − ).…”

Section: Introductionmentioning

confidence: 99%

Bottom-up Coarse-Graining: Principles and Perspectives

Jin

Pak

Durumeric

et al. 2022

J. Chem. Theory Comput.

129

147

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Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

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“…These lower-dimensional CG models exhibit smoother potential energy surfaces than their AA counterparts, accelerating the sampling of long spatiotemporal scales. The effective potentials governing the thermodynamics of CG models can be inferred by sampling trajectories of AA systems using rigorous statistical mechanical techniques, [8][9][10][11][12][13][14] with recent approaches benefiting from advances in machine learning (ML). [15][16][17][18][19] Furthermore, removing "irrelevant" degrees of freedom often leads to more interpretable models that directly highlight the underlying structure-function relationships, rendering CG modeling both a computationally and conceptually efficient strategy.…”

Section: Introductionmentioning

confidence: 99%

Bypassing Backmapping: Coarse-Grained Electronic Property Distributions Using Heteroscedastic Gaussian Processes

Maier

Jackson

2022

Preprint

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We employ deep kernel learning electronic coarse-graining (DKL-ECG) with approximate Gaussian Processes as a flexible and scalable framework for learning heteroscedastic electronic property distributions as a smooth function of coarse-grained (CG) configuration. The appropriateness of the Gaussian prior on the predictive CG property distributions is justified as a function of CG model resolution by examining the statistics of the target distributions. The certainties of the predictive CG distributions are shown to be limited by CG model resolution, with DKL-ECG predictive noise converging to the intrinsic physical noise induced by the CG mapping operator for multiple chemistries. Further analysis of the resolution dependence of the learned CG property distributions allows for the identification of CG mapping operators that capture CG degrees of freedom with strong electron-phonon coupling. We further demonstrate the ability to construct the exact quantum chemical valence electronic density of states (EDOS), including behavior in the tails of the EDOS, from an entirely CG model by combining iterative boltzmann inversion and DKL-ECG. DKL-ECG provides a means of learning CG distributions of all-atom properties that are traditionally ``lost" in CG model development, introducing a robust methodological alternative to backmapping algorithms commonly employed to recover all-atom property distributions from CG simulations.

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A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy

Cited by 29 publications

References 93 publications

Learning Pair Potentials using Differentiable Simulations

Learning Pair Potentials using Differentiable Simulations

Bottom-up Coarse-Graining: Principles and Perspectives

Bypassing Backmapping: Coarse-Grained Electronic Property Distributions Using Heteroscedastic Gaussian Processes

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