SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Schütt, Kristof T.; Kessel, Pan; Gastegger, Michael; Nicoli, Kim A.; Tkatchenko, Alexandre; Müller, Klaus‐Robert

doi:10.1021/acs.jctc.8b00908

Cited by 362 publications

(412 citation statements)

References 40 publications

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“…One of the first deep learning architectures to learn to represent molecules or materials is the family of Deep Tensor Neural Networks (DTNN) [14], with its recent addition SchNet [54,105]. While in kernel-based learning methods [106,2] chemical compounds are compared in terms of pre-specified kernel functions [8,107,108,109], DTNN and its extension SchNet learn a multi-scale representation of the properties of molecules or materials from large data sets.…”

Section: Deep Tensor Neural Nets Schnet and Continuous Convolutionsmentioning

confidence: 99%

“…DTNN and SchNet have both reached highly competitive prediction quality both across chemical compound space and across configuration space in order to simulate molecular dynamics. In addition to their prediction quality, their scalability to large data sets [105] and their ability to extract novel chemical insights by means of their learnt representation make the DTNN family an increasingly popular research tool.…”

Section: Deep Tensor Neural Nets Schnet and Continuous Convolutionsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Molecular Simulation

Noé

Tkatchenko

Müller

et al. 2020

Annu. Rev. Phys. Chem.

680

476

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Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. 1 arXiv:1911.02792v1 [physics.chem-ph] 7 Nov 2019 complex relationship between the input (the pixels) and the output (the labels) that is unknown in its explicit form but can be inferred by a suitable algorithm. Clearly, such an operating principle can be very useful in the description of atomic and molecular systems as well. We know that atomistic configurations dictate the chemical properties, and the machine can learn to associate the latter to the former without solving first principle equations, if presented with enough examples. Although different machine learning tools are available and have been applied to molecular simulation (e.g., kernel methods [2]), here we mostly focus on the use of neural networks, now often synonymously used with the term "deep learning". We assume the reader has basic knowledge of machine learning and we refer to the literature for an introduction to statistical learning theory [3,4] and deep learning [5,6].One of the first applications of machine learning in Chemistry has been to extract classical potential energy surfaces from quantum mechanical (QM) calculations, in order to efficiently perform molecular dynamics (MD) simulations that can incorporate quantum effects. The seminal work of Behler and Parrinello in this direction [7] has opened the way to a now rapidly advancing area of research [8,9,10,11,12,13,14,15]. In addition to atomistic force fields, it has been recently shown that, in the same spirit, effective molecular models at resolution coarser than atomistic can be designed by ML [16,17,18]. Analysis and simulation of MD trajectories has also been affected by ML, for instance for the definition of optimal reaction coordinates [19,20,21,22,23,24], the estimate of free energy surfaces [25,26,27,22], the construction of Markov State Models [21,23,28], and for enhancing MD sampling by learning bias potentials [29,30,31,32,33] or selecting starting configurations by active learning [34,35,36]. Finally, ML can be used to generate samples from the equilibrium distribution of a molecular system without performing MD altogether, as proposed...

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Section: Deep Tensor Neural Nets Schnet and Continuous Convolutionsmentioning

confidence: 99%

Section: Deep Tensor Neural Nets Schnet and Continuous Convolutionsmentioning

confidence: 99%

Machine Learning for Molecular Simulation

Noé

Tkatchenko

Müller

et al. 2020

Annu. Rev. Phys. Chem.

680

476

View full text Add to dashboard Cite

show abstract

“…Besides general purpose ML tools such as scikit‐learn, tensorflow, and Pytorch, there has been an explosion of customized open‐source ML software libraries for materials science. A nonexhaustive list includes AutoMatminer, PROPhet for general materials ML; amp, ænet, and ANI for developing neural network potentials; CGCNN, MEGNet, and SchnetPack are graph‐based deep learning model packages for accurate crystal and/or molecule property modeling.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

425

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…A very successful example is the Gaussian Approximation Potential (GAP) by Bartók et al that is based on Gaussian process regression [38]. Various other MLP approaches and implementations have been developed in recent years [51][52][53][54][55][56][57][58][59].…”

Section: Progress In Machine Learning Methods For Materials Simulationsmentioning

confidence: 99%

Machine learning for the modeling of interfaces in energy storage and conversion materials

Artrith

2019

J. Phys. Energy

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The properties and atomic-scale dynamics of interfaces play an important role for the performance of energy storage and conversion devices such as batteries and fuel cells. In this topical review, we consider recent progress in machine-learning (ML) approaches for the computational modeling of materials interfaces. ML models are computationally much more efficient than first principles methods and thus allow to model larger systems and extended timescales, a necessary prerequisites for the accurate description of many interface properties. Here we review the recent major developments of ML-based interatomic potentials for atomistic modeling and ML approaches for the direct prediction of materials properties. This is followed by a discussion of ML applications to solid-gas, solid-liquid, and solid-solid interfaces as well as to nanostructured and amorphous phases that commonly form in interface regions. We then highlight how ML has been used to obtain important insights into the structure and stability of interfaces, interfacial reactions, and mass transport at interfaces. Finally, we offer a perspective on the current state of ML potential development and identify future directions and opportunities for this exciting research field.

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SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Cited by 362 publications

References 40 publications

Machine Learning for Molecular Simulation

Machine Learning for Molecular Simulation

A Critical Review of Machine Learning of Energy Materials

Machine learning for the modeling of interfaces in energy storage and conversion materials

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