Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

Rohrdanz, Mary A.; Zheng, Wenwei; Clementi, Cecilia

doi:10.1146/annurev-physchem-040412-110006

Cited by 203 publications

(229 citation statements)

References 125 publications

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“…As we will demonstrate, the combination of SOAP-based structural metrics and NLDR representation provides a broadly applicable protocol to generate an insightful representation of the structural and alchemical landscape of complex molecular and condensed-phase systems. Of course, one could use the SOAP-based global kernels, or the corresponding distances, as the basis of other non-linear dimensionality reduction techniques, such as multidimensional scaling 49 or diffusion maps 12,16,50 . We refer the reader to the relevant literature for a detailed explanation of the sketch-map algorithm [13][14][15] .…”

Section: E Representing (Al)chemical Landscapesmentioning

confidence: 99%

Comparing molecules and solids across structural and alchemical space

Bartók

Csänyi

et al. 2016

Phys. Chem. Chem. Phys.

666

909

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Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for better compounds and materials, and to drive the next generation of machine-learning techniques for predicting the stability and properties of molecules and materials. In the last few years several strategies have been designed to compare atomic coordination environments. In particular, the Smooth Overlap of Atomic Positions (SOAP) has emerged as a natural framework to obtain translation, rotation and permutation-invariant descriptors of groups of atoms, driven by the design of various classes of machine-learned inter-atomic potentials. Here we discuss how one can combine such local descriptors using a Regularized Entropy Match (REMatch) approach to describe the similarity of both whole molecular and bulk periodic structures, introducing powerful metrics that allow the navigation of alchemical and structural complexity within a unified framework. Furthermore, using this kernel and a ridge regression method we can also predict atomization energies for a database of small organic molecules with a mean absolute error below 1kcal/mol, reaching an important milestone in the application of machine-learning techniques to the evaluation of molecular properties.

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Section: E Representing (Al)chemical Landscapesmentioning

confidence: 99%

Comparing molecules and solids across structural and alchemical space

Bartók

Csänyi

et al. 2016

Phys. Chem. Chem. Phys.

666

909

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“…This makes our definition of a pattern recognition function well-suited for use as a collective variable in accelerated sampling methods, [34] possibly in conjunction with other machine learning techniques to characterize the overall connectivity induced by the selected molecular pattern [35], or similar fingerprint metrics that are guaranteed to distinguish dissimilar structures [36]. The PAMM variables corresponding to different structural descriptor can also be analyzed to yield a coarse-grained, lowdimensional map [37][38][39]. If necessary, one can artificially "soften" the transition between clusters, by dividing all the covariance matrices Σ k in the Gaussian model by a scaling factor α.…”

Section: Analysis Of the Simulationmentioning

confidence: 99%

Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond

Gasparotto

Ceriotti

2014

The Journal of Chemical Physics

103

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The concept of chemical bonding can ultimately be seen as a rationalization of the recurring structural patterns observed in molecules and solids. Chemical intuition is nothing but the ability to recognize and predict such patterns, and how they transform into one another. Here we discuss how to use a computer to identify atomic patterns automatically, so as to provide an algorithmic definition of a bond based solely on structural information. We concentrate in particular on hydrogen bonding -a central concept to our understanding of the physical chemistry of water, biological systems and many technologically important materials. Since the hydrogen bond is a somewhat fuzzy entity that covers a broad range of energies and distances, many different criteria have been proposed and used over the years, based either on sophisticate electronic structure calculations followed by an energy decomposition analysis, or on somewhat arbitrary choices of a range of structural parameters that is deemed to correspond to a hydrogen-bonded configuration. We introduce here a definition that is univocal, unbiased, and adaptive, based on our machine-learning analysis of an atomistic simulation. The strategy we propose could be easily adapted to similar scenarios, where one has to recognize or classify structural patterns in a material or chemical compound.

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“…In addition, the object-oriented style allows us to write reusable objects whose functions are described in a developer manual that is generated from the code. This makes it far easier to code the complex, multi-layered, nested collective variables (CVs) that are increasingly being used to perform dimensionality reduction [19]. Finally, the new code structure allows one to use the same code for both on-the-fly biasing/analysis and post-processing thus minimizing redundancy.…”

Section: Introductionmentioning

confidence: 99%

PLUMED 2: New feathers for an old bird

Tribello

Bonomi

Branduardi

et al. 2014

Computer Physics Communications

2,790

2,841

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Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardwares, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here -a complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field.

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Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

Cited by 203 publications

References 125 publications

Comparing molecules and solids across structural and alchemical space

Comparing molecules and solids across structural and alchemical space

Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond

PLUMED 2: New feathers for an old bird

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