Ab initio phase diagram and nucleation of gallium

Niu, Haiyang; Bonati, Luigi; Piaggi, Pablo M.; Parrinello, Michele

doi:10.1038/s41467-020-16372-9

Cited by 143 publications

(116 citation statements)

References 53 publications

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“…Later, a similar framework, also employing accurate reference data at the level of hybrid density-functional theory (DFT), reproduced several thermodynamic properties of solid and liquid water at ambient pressure 29 . Very recently, multithermal–multibaric simulations were used to compute the phase diagram and nucleation behaviour of gallium 30 , while simulations using MLPs provided evidence for the supercritical behaviour of high-pressure hydrogen 31 .…”

Section: Introductionmentioning

confidence: 99%

Quantum-mechanical exploration of the phase diagram of water

Reinhardt

Cheng

2021

Nat Commun

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The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.

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Section: Introductionmentioning

confidence: 99%

Quantum-mechanical exploration of the phase diagram of water

Reinhardt

Cheng

2021

Nat Commun

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“…(Reproduced with permission from [ 192 ]). b Phase diagram of gallium nucleation from the melt using metadynamics MD simulations with an ML potential [ 193 ]. The predicted phase diagram (red lines) is compared to the experimentally measured phase diagram (blue lines).…”

Section: Applications To Industrymentioning

confidence: 99%

“…Bonati and Parrinello investigated crystallization of silicon from the melt with well-tempered metadynamics [ 197 ] using an ANN potential [ 200 ], identifying a single collective variable derived from the Debye structure factor to steer the crystallization. A related approach was employed by Niu et al for the calculation of the phase diagram for gallium nucleation from the melt [ 193 ]. Gallium exhibits a complex phase behavior owing to the mixed covalent and metallic bonding, making the element a challenging benchmark case for phase diagram calculations.…”

Section: Applications To Industrymentioning

confidence: 99%

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Morawietz

Artrith

2020

J Comput Aided Mol Des

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Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

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“…3 Machine learning (ML) approaches have the potential to revolutionize force-field based simulations, aiming to provide the best of both worlds, [4][5][6] and have indeed begun to provide new insights into a range of challenging research problems. [7][8][9][10][11][12][13][14][15] The development of a truly general ML potential mapping nuclear coordinates to total energies and forces is, however, precluded by the curse of dimensionality. Within small chemical subspaces, models can be achieved using neural networks (NNs), 6,[16][17][18][19][20] kernel-based methods such as Gaussian processes (GP) 21,22 and gradient-domain machine learning (GDML), 23 and linear fitting with properly chosen basis functions, 24,25 each with different data requirements and transferability.…”

Section: Introductionmentioning

confidence: 99%

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

Young¹,

Johnston-Wood²,

Deringer³

et al. 2021

Preprint

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<p>Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (S<sub>N</sub>2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems. </p>

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Ab initio phase diagram and nucleation of gallium

Cited by 143 publications

References 53 publications

Quantum-mechanical exploration of the phase diagram of water

Quantum-mechanical exploration of the phase diagram of water

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

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