185 research outputs found
Field-Effect Transistors based on 2-D Materials: a Modeling Perspective
Two-dimensional (2D) materials are particularly attractive to build the
channel of next-generation field-effect transistors (FETs) with gate lengths
below 10-15 nm. Because the 2D technology has not yet reached the same level of
maturity as its Silicon counterpart, device simulation can be of great help to
predict the ultimate performance of 2D FETs and provide experimentalists with
reliable design guidelines. In this paper, an ab initio modelling approach
dedicated to well-known and exotic 2D materials is presented and applied to the
simulation of various components, from thermionic to tunnelling transistors
based on mono- and multi-layer channels. Moreover, the physics of metal - 2D
semiconductor contacts is revealed and the importance of different scattering
sources on the mobility of selected 2D materials is discussed. It is expected
that modeling frameworks similar to the one described here will not only
accompany future developments of 2D devices, but will also enable them
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many
important insights into a range of physical and chemical properties of various
molecular and solid-state systems. Their importance to various fields,
including materials science, chemical sciences, computational chemistry and
device physics, is underscored by the large fraction of available public
supercomputing resources devoted to these calculations. As we enter the
exascale era, exciting new opportunities to increase simulation numbers, sizes,
and accuracies present themselves. In order to realize these promises, the
community of electronic structure software developers will however first have
to tackle a number of challenges pertaining to the efficient use of new
architectures that will rely heavily on massive parallelism and hardware
accelerators. This roadmap provides a broad overview of the state-of-the-art in
electronic structure calculations and of the various new directions being
pursued by the community. It covers 14 electronic structure codes, presenting
their current status, their development priorities over the next five years,
and their plans towards tackling the challenges and leveraging the
opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in
Materials Science and Engineering; Address any correspondence to Vikram
Gavini ([email protected]) and Danny Perez ([email protected]
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in
computational science, underlying calculations in domains from linguistics to
biology and physics. Generative machine-learning (ML) models have emerged as a
promising tool in this space, building on the success of this approach in
applications such as image, text, and audio generation. Often, however,
generative tasks in scientific domains have unique structures and features --
such as complex symmetries and the requirement of exactness guarantees -- that
present both challenges and opportunities for ML. This Perspective outlines the
advances in ML-based sampling motivated by lattice quantum field theory, in
particular for the theory of quantum chromodynamics. Enabling calculations of
the structure and interactions of matter from our most fundamental
understanding of particle physics, lattice quantum chromodynamics is one of the
main consumers of open-science supercomputing worldwide. The design of ML
algorithms for this application faces profound challenges, including the
necessity of scaling custom ML architectures to the largest supercomputers, but
also promises immense benefits, and is spurring a wave of development in
ML-based sampling more broadly. In lattice field theory, if this approach can
realize its early promise it will be a transformative step towards
first-principles physics calculations in particle, nuclear and condensed matter
physics that are intractable with traditional approaches.Comment: 11 pages, 5 figure
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