Insightful classification of crystal structures using deep learning

Ziletti, Angelo; Kumar, Divya; Scheffler, Matthias; Ghiringhelli, Luca M.

doi:10.1038/s41467-018-05169-6

Cited by 319 publications

(303 citation statements)

References 58 publications

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“…Recent examples of approaches that analyze machine learning models to reveal their mechanisms include the analysis of input feature importance, [200] explicit formulation of the input in algebraic form, [215,216] and analysis of convolutional neural network filters. [217]…”

Section: Introduction To Machine Learningmentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

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Section: Introduction To Machine Learningmentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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“…A recent work reported the identification of lattice symmetries by representing crystals via diffraction image calculations, which then serve to construct a deep learning neural network model for classification [418]. Not only to structural properties, recently the vibrational free energies and entropies of compounds were studied by ML models and achieved good accuracy with only chemical compositions [419].…”

Section: Discovery Energies and Stabilitymentioning

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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“…The result from the linear ML model (LR) is promising, prompting us to move to a more sophisticated deep learning model. Deep learning models (LeCun et al, 2015;Goodfellow et al, 2016) have been successfully applied to various fields, ranging from computer vision (He et al, 2016;Krizhevsky et al, 2012;Radford et al, 2015), natural language processing (Bahdanau et al, 2014;Sutskever et al, 2014;Kim, 2014) to material science (Ramprasad et al, 2017;Ziletti et al, 2018). In particular, we sought to use a CNN (Lecun et al, 1998).…”

Section: Space-group Determination Based On the Convolutional Neural mentioning

confidence: 99%

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function

Liu

Tao

Hsu

et al. 2019

Acta Crystallogr A Found Adv

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A method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top‐6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model‐independent assessment of PDF data on a wide class of materials.

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Insightful classification of crystal structures using deep learning

Cited by 319 publications

References 58 publications

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Data‐Driven Materials Science: Status, Challenges, and Perspectives

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

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function

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