Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Zhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; Weinan, E

doi:10.1103/physrevlett.120.143001

Cited by 1,555 publications

(1,331 citation statements)

References 36 publications

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“…Commonly these ML potentials use as learning algorithms kernel ridge regression (KRR), neural networks (NN), or even support vector machines (SVM) [231], which are very efficient in mapping the complex PES. Notable examples are the Gaussian Approximation Potentials (GAPs) [212,232], Behler-Parrinello high-dimensional neural network potentials [233,234], and Deep Potential molecular dynamics [235]. Related to the structural representation, a scoring parameter to identify dimensionality of materials was recently developed [236].…”

Section: Representations and Descriptorsmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Representations and Descriptorsmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…Their ability to be universal function approximators makes them extremely useful for materials property predictions, if sufficient training data exist. MLPs have also been used in combination with local environment features to develop interatomic potentials . CNNs (Figure 3b) are rapidly gaining interest due to their recent feat of outperforming other ML algorithms by a considerable margin in image recognition .…”

Section: Model Selection and Trainingmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

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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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“…In the past decade, various forms of machine-learning interatomic potentials have become increasingly popular [24][25][26][27][28][29][30]. The main advantage of using machine learning to construct potentials is that a fixed analytical form is not assumed, which results in flexible potentials that can describe virtually any material and their properties.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning interatomic potential for radiation damage and defects in tungsten

et al. 2019

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We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiationinduced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been long-standing deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.

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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Cited by 1,555 publications

References 36 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

A Critical Review of Machine Learning of Energy Materials

Machine-learning interatomic potential for radiation damage and defects in tungsten

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