Machine Learning in Materials Science

Mueller, Tim; Kusne, A. Gilad; Ramprasad, Rampi

doi:10.1002/9781119148739.ch4

Cited by 235 publications

(179 citation statements)

References 165 publications

(209 reference statements)

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“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] This emerging trend is fueled by the availability and emergence of large materials databases, [16][17][18] as well as our ability to progressively accumulate materials data via high-throughput computations 19,20 and experiments. [16][17][18] Data-driven strategies aimed at rapid property predictions, and ultimately at rational or informed materials design, rely on exploiting the information content of past data, and using such information within heuristic or statistical interpolative learning models to provide estimates of properties of a new material.…”

Section: Introductionmentioning

confidence: 99%

“…This approach is entirely analogous to similar pursuits undertaken within chem-and bio-informatics wherein lead candidates worthy of further in-depth investigations are identified rapidly in a first-level of screening. 4,5,14 Data-driven property prediction strategies have two steps. The first involves representing materials numerically via descriptors, attribute vectors, or fingerprints.…”

Section: Introductionmentioning

confidence: 99%

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Accelerated materials property predictions and design using motif-based fingerprints

2015

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Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in chem-informatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict relevant properties of a material, given that its fingerprint can be defined. Two simple machine-learning based procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then, to reconstruct molecules which possess a set of targeted properties.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accelerated materials property predictions and design using motif-based fingerprints

2015

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“…A new scheme is presented that systematically learns in an interpolative manner to predict atomic forces in environments encountered during the dynamical evolution of materials from a set of high-level calculations performed on reference atomic configurations with modest system sizes. This concept is resonant with emerging data-driven (or "big data" [2][3][4][5][6] ) approaches aimed at materials discovery in general 7,8 , as well as at accelerating materials simulations [9][10][11][12][13] . Machine learning (ML) methods using neural networks 9,10 and Gaussian processes 11,12 have been successful in the development of interatomic potentials, wherein the potential energy surface is learned from a set of higher-level (quantum mechanics based) reference calculations.…”

mentioning

confidence: 99%

Learning scheme to predict atomic forces and accelerate materials simulations

Botu¹,

Ramprasad²

2015

Phys. Rev. B

200

211

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The behavior of an atom in a molecule, liquid or solid is governed by the force it experiences. If the dependence of this vectorial force on the atomic chemical environment can be learned efficiently with high-fidelity from benchmark reference results-using "big data" techniques, i.e., without resorting to actual functional forms-then this capability can be harnessed to enormously speed up in silico materials simulations. The present contribution provides several examples of how such a force field for Al can be used to go far beyond the length-scale and time-scale regimes accessible presently using quantum mechanical methods. It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements.

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“…At present, regression models are used to realise precise predictions thanks to advances in machine learning techniques, and such techniques can be applied to the intelligent design of spectroscopy experiments. 9,10 Machine learning techniques have recently been introduced to materials science. 11 Materials informatics 12 is regarded as the fourth paradigm in the field of materials science following the previous paradigms of experiment, theory, and computation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

et al. 2018

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Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.

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Machine Learning in Materials Science

Cited by 235 publications

References 165 publications

Accelerated materials property predictions and design using motif-based fingerprints

Accelerated materials property predictions and design using motif-based fingerprints

Learning scheme to predict atomic forces and accelerate materials simulations

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

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