Machine Learning Discovery of Computational Model Efficacy Boundaries

Murillo, Michael S.; Marciante, Mathieu; Stanton, Liam

doi:10.1103/physrevlett.125.085503

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…As in previous work [35,47], the TFY model tends to underestimate the peak (relative to KS-MD) of g(r) at low temperatures, with a corresponding error in the self diffusion coefficient. Notionally the accuracy of the TFY model appears to follow the machine learning trend of Z /Z > 0.35 [48], although it was not possible to use all models at high enough temperatures to be quantitative. In contrast, the NPA model with its improved Kohn-Sham treatment and use of a pseudopotential in (6) eliminates most of these errors except for C and V at T = 0.5 eV, elements for which we would recommend NPA for T > 2 eV.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas

Stanek,

Clay,

Dharma-wardana

et al. 2020

Preprint

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Macroscopic simulations of dense plasmas rely on detailed microscopic information that can be computationally expensive and is difficult to verify experimentally. In this work, we delineate the accuracy boundary between microscale simulation methods by comparing Kohn-Sham density functional theory molecular dynamics (KS-MD) and radial pair potential molecular dynamics (RPP-MD) for a range of elements, temperature, and density. By extracting the optimal RPP from KS-MD data using force-matching, we constrain its functional form and dismiss classes of potentials that assume a constant power law for small interparticle distances. Our results show excellent agreement between RPP-MD and KS-MD for multiple metrics of accuracy at temperatures of only a few electron volts. The use of RPPs offers orders of magnitude decrease in computational cost and indicates that three-body potentials are not required beyond temperatures of a few eV. Due to its efficiency, the validated RPP-MD provides an avenue for reducing errors due to finite-size effects that can be on the order of ∼ 20%.

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Section: Discussionmentioning

confidence: 99%

Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas

Stanek,

Clay,

Dharma-wardana

et al. 2020

Preprint

Self Cite

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“…Moreover, as model complexity increases, additional parameters will be introduced inherently, and more sophisticated exchange-correlation functionals must be developed. Therefore, parameterizing solutions must be considered in tandem, such as developing and implementing machine learning (ML) techniques to quickly parameterize models [81,82] and evaluate their efficacy [83]. ML techniques may also prove useful in developing novel exchange-correlation functionals [84].…”

Section: Perspectivementioning

confidence: 99%

A Perspective on Li/S Battery Design: Modeling and Development Approaches

McCreary

Kim

et al. 2021

Batteries

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Lithium/sulfur (Li/S) cells that offer an ultrahigh theoretical specific energy of 2600 Wh/kg are considered one of the most promising next-generation rechargeable battery systems for the electrification of transportation. However, the commercialization of Li/S cells remains challenging, despite the recent advancements in materials development for sulfur electrodes and electrolytes, due to several critical issues such as the insufficient obtainable specific energy and relatively poor cyclability. This review aims to introduce electrode manufacturing and modeling methodologies and the current issues to be overcome. The obtainable specific energy values of Li/S pouch cells are calculated with respect to various parameters (e.g., sulfur mass loading, sulfur content, sulfur utilization, electrolyte-volume-to-sulfur-weight ratio, and electrode porosity) to demonstrate the design requirements for achieving a high specific energy of >300 Wh/kg. Finally, the prospects for rational modeling and manufacturing strategies are discussed, to establish a new design standard for Li/S batteries.

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“…Rapid advance in machine learning (ML)-based methods − offer us a new tool to address this challenge. ML is superior at finding hidden correlations − and has recently been increasingly exploited in many research studies for chemistry − and materials property prediction. − In our previous studies, we have found that dipoles are often good ML descriptors for molecular properties, because they carry microscopic information of the electronic levels and distributions. , However, molecular dipoles are very difficult to measure in practice. The fact that vibrational spectra, such as infrared (IR) and Raman, are strongly associated with the dipole of the system under investigation seems to suggest that vibrational spectral signals could be directly used as ML descriptors.…”

Section: Introductionmentioning

confidence: 99%

Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning

Wang

Jiang

et al. 2022

J. Am. Chem. Soc.

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Learning microscopic properties of a material from its macroscopic measurables is a grand and challenging goal in physical science. Conventional wisdom is to first identify material structures exploiting characterization tools, such as spectroscopy, and then to infer properties of interest, often with assistance of theory and simulations. This indirect approach has limitations due to the accumulation of errors from retrieving structures from spectral signals and the lack of quantitative structure−property relationship. A new pathway directly from spectral signals to microscopic properties is highly desirable, as it would offer valuable guidance toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational spectroscopy to establish quantitative spectrum− property relationships. Key interaction properties of substrate−adsorbate systems, including adsorption energy and charge transfer, are quantitatively determined directly from Infrared and Raman spectroscopic signals of the adsorbates. The machine-learned spectrum−property relationships are presented as mathematical formulas, which are physically interpretable and therefore transferrable to a series of metal/alloy surfaces. The demonstrated ability of quantitative determination of hard-to-measure microscopic properties using machine-learned spectroscopy will significantly broaden the applicability of conventional spectroscopic techniques for materials design and high throughput screening under operando conditions.

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Machine Learning Discovery of Computational Model Efficacy Boundaries

Cited by 6 publications

References 39 publications

Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas

Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas

A Perspective on Li/S Battery Design: Modeling and Development Approaches

Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning

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