Energy-free machine learning force field for aluminum

Kruglov, Ivan A.; Sergeev, Oleg; Yanilkin, A. V.; Oganov, Artem R.

doi:10.1038/s41598-017-08455-3

Cited by 66 publications

(51 citation statements)

References 44 publications

(49 reference statements)

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“…1. The force errors of our NN potential are below those reported for other Al machine learning force fields 11,18 . We note that the same level of accuracy can be reached using a much smaller amount of training data.…”

Section: Potential Training and Testingcontrasting

confidence: 63%

Anharmonic thermodynamics of vacancies using a neural network potential

Bochkarev

Roekeghem

Mossa

et al. 2019

Phys. Rev. Materials

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Lattice anharmonicity is thought to strongly affect vacancy concentrations in metals at high temperatures. It is however non-trivial to account for this effect directly using density functional theory (DFT). Here we develop a deep neural network potential for aluminum that overcomes the limitations inherent to DFT, and we use it to obtain accurate anharmonic vacancy formation free energies as a function of temperature. While confirming the important role of anharmonicity at high temperatures, the calculation unveils a markedly nonlinear behavior of the vacancy formation entropy and shows that the vacancy formation free energy only violates Arrhenius law at temperatures above 600 K, in contrast with previous DFT calculations.

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Section: Potential Training and Testingcontrasting

confidence: 63%

Anharmonic thermodynamics of vacancies using a neural network potential

Bochkarev

Roekeghem

Mossa

et al. 2019

Phys. Rev. Materials

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“…[18][19][20] Applications of ML are becoming increasingly common in experimental and computational chemistry. Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces [23][24][25][26][27] , forces [28][29][30] , atomization energies [31][32][33] , atomic partial charges 32,[34][35][36] , molecular dipoles 26,37,38 , materials discovery [39][40][41] , and protein-ligand complex scoring 42 .…”

Section: Introductionmentioning

confidence: 99%

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Smith¹,

Nebgen²,

Zubatyuk³

et al. 2019

Preprint

135

205

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Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and druglike molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

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“…Another research direction intended to increase the accuracy of the interatomic potentials is the so-called machine-learning interatomic potentials [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. Ideologically, they are different from the empirical interatomic potentials in the way that machine-learning potentials attempt to increase accuracy not by putting more physics into the model, but through a flexible functional form that allows large amounts of DFT data to be used for the fitting.…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy of interatomic potentials: more physics or more data? A case study of silica

Novikov

Shapeev

2019

Materials Today Communications

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In this paper we test two strategies to improving the accuracy of machinelearning potentials, namely adding more fitting parameters thus making use of large volumes of available quantum-mechanical data, and adding a chargeequilibration model to account for ionic nature of the SiO 2 bonding. To that end, we compare Moment Tensor Potentials (MTPs) and MTPs combined with the charge-equilibration (QEq) model (MTP+QEq) fitted to a density functional theory dataset of α-quartz SiO 2 -based structures. In order to make a meaningful comparison, in addition to the accuracy, we assess the uncertainty of predictions of each potential. It is shown that adding the QEq model to MTP does not make any improvement over the MTP potential alone, while adding more parameters does improve the accuracy and uncertainty of its predictions.

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Energy-free machine learning force field for aluminum

Cited by 66 publications

References 44 publications

Anharmonic thermodynamics of vacancies using a neural network potential

Anharmonic thermodynamics of vacancies using a neural network potential

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Improving accuracy of interatomic potentials: more physics or more data? A case study of silica

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