Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy

Mo, Pinghui; Shi, Mengchao; Yao, Wenze; Liu, Jie

doi:10.1109/led.2020.2972066

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…One is transfer learning, which has been developed extensively in the context of articial neural networks, 1 and much of the work in that eld has been brought into chemistry, especially in the development of PESs. [2][3][4] For example, Meuwly and co-workers applied transfer learning using thousands of local CCSD(T) energies to improve their MP2-based neural network PESs for malonaldehyde, acetoacetaldehyde and acetylacetone. 3 The basic idea of transfer learning is that a t obtained from one source of data (perhaps a large one) can be ne-tuned for a related problem by using limited data.…”

Section: Introductionmentioning

confidence: 99%

A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential

Qu¹,

Conte

et al. 2022

Digital Discovery

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Section: Introductionmentioning

confidence: 99%

A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential

Qu¹,

Conte

et al. 2022

Digital Discovery

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“…This has been demonstrated in a recent study, in which the neurons act as the tight-binding (TB) matrix elements in the Hamiltonian parameterization of the TB model for energy band calculation [124]. From calculation methodology to case studies, several studies have been reported in which machine-learningaugmented DFT works efficiently with high accuracy in atomistic modeling for devices, including the prediction of atomic force in phase change memory [125], the calculation of potential energy surface in SiGe alloys [126], and the simulation of surface reconstruction of the Si ( 111)-(7 × 7) surface [127].…”

Section: Atomistic Calculation With Aimentioning

confidence: 99%

CMOS Scaling for the 5 nm Node and Beyond: Device, Process and Technology

Radamson,

Miao,

Zhou

et al. 2024

Nanomaterials

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After more than five decades, Moore’s Law for transistors is approaching the end of the international technology roadmap of semiconductors (ITRS). The fate of complementary metal oxide semiconductor (CMOS) architecture has become increasingly unknown. In this era, 3D transistors in the form of gate-all-around (GAA) transistors are being considered as an excellent solution to scaling down beyond the 5 nm technology node, which solves the difficulties of carrier transport in the channel region which are mainly rooted in short channel effects (SCEs). In parallel to Moore, during the last two decades, transistors with a fully depleted SOI (FDSOI) design have also been processed for low-power electronics. Among all the possible designs, there are also tunneling field-effect transistors (TFETs), which offer very low power consumption and decent electrical characteristics. This review article presents new transistor designs, along with the integration of electronics and photonics, simulation methods, and continuation of CMOS process technology to the 5 nm technology node and beyond. The content highlights the innovative methods, challenges, and difficulties in device processing and design, as well as how to apply suitable metrology techniques as a tool to find out the imperfections and lattice distortions, strain status, and composition in the device structures.

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“…This “gold standard” – CCSD(T) – scales as N 7 (with N being the number of basis functions), 176 which makes calculating energies and forces for large data sets and larger molecules impractical. Thus, TL 50,177–180 and related Δ-learning approaches 170,181–183 gained a lot of attention in recent years and were shown to be data and cost effective alternatives to the “brute force” approach in quantum chemistry: a low level PES based on a large data set of cheap reference data ( e.g. DFT) is generated first, which then is used to obtain a high level PES based on few, well chosen high level of theory ( e.g.…”

Section: Knowledge Transfermentioning

confidence: 99%