Optical Proximity Correction Using Machine Learning Assisted Human Decision

Optical proximity correction (OPC) is a critical step in semiconductor manufacturing due to its high complexity and significant influence on the subsequent process steps. Conventional OPC using the Maxwell equation can become more and more challenging as a fully vectorized three-dimensional simulation is required for advanced technology nodes. Machine learning (ML) has been a promising alternative recently. This work proposes machine-learning-assisted human decision, which can be more in line with the clean room engineer's practice and can potentially surpass pure human decision and the pure machine learning approach. Using 10-step optimization in the photolithographic mask, the averaged mean square error (MSE) at the optimized cases are 6360 and 2101 for two-bar patterns and 7132 and 5931 for tri-line attackers when comparing pure human decision and ML-assisted human. The average MSEs at the first 3 steps are 26019 and 6023 for the two-bar pattern and 79979 and 7738 for the tri-line attacker when comparing pure ML and ML-assisted human. It is suggested that the strength of the ML-assisted human decision lies in the early-stage superiority over pure ML, flexibility, incorporating past experience, and a human sense that cannot be formulated concretely by statistical models

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Acknowledgements: This work was funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC0205CH11231 (Materials Project programme KC23MP). The work was also supported by the computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant number ACI1053575; the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory; and the Lawrencium Computational Cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory. We thank J. Munro and L. Barroso-Luque for helpful discussions.Funder: U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC0205CH11231 (Materials Project program KC23MP).Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in LixMnO2, the finite temperature phase diagram for LixFePO4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Acknowledgements: This work was funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC0205CH11231 (Materials Project programme KC23MP). The work was also supported by the computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant number ACI1053575; the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory; and the Lawrencium Computational Cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory. We thank J. Munro and L. Barroso-Luque for helpful discussions.Funder: U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC0205CH11231 (Materials Project program KC23MP).AbstractLarge-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in LixMnO2, the finite temperature phase diagram for LixFePO4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.</jats:p

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in LixMnO2, the finite temperature phase diagram for LixFePO4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs

CederGroupHub/chgnet: v0.3.1

&lt;p&gt;Hot fix release for v0.3.0 which is deprecated due to missing pretrained checkpoints (fixed in https://github.com/CederGroupHub/chgnet/pull/86/commits/0f0278649ed1d9d07ccfcef2aa11caacda5848fe). See https://github.com/CederGroupHub/chgnet/pull/86 for details.&lt;/p&gt; &lt;h3&gt; Enhancements&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Add &lt;code&gt;CHGNet.version&lt;/code&gt; property by @janosh in https://github.com/CederGroupHub/chgnet/pull/86&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/CederGroupHub/chgnet/compare/V0.3.0...v0.3.1&lt;/p&gt

CederGroupHub/chgnet: v0.3.1

&lt;p&gt;Hot fix release for v0.3.0 which is missing pretrained checkpoints (fixed in https://github.com/CederGroupHub/chgnet/pull/86/commits/0f0278649ed1d9d07ccfcef2aa11caacda5848fe). See https://github.com/CederGroupHub/chgnet/pull/86 for details.&lt;/p&gt; &lt;h3&gt; Enhancements&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Add &lt;code&gt;CHGNet.version&lt;/code&gt; property by @janosh in https://github.com/CederGroupHub/chgnet/pull/86&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/CederGroupHub/chgnet/compare/V0.3.0...v0.3.1&lt;/p&gt

CederGroupHub/chgnet: v0.3.3

&lt;p&gt;&lt;!-- Release notes generated using configuration in .github/release.yml at v0.3.3 --&gt;&lt;/p&gt; &lt;h2&gt;What's Changed&lt;/h2&gt; &lt;h3&gt; Bug Fixes&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Replace ase &lt;code&gt;ExpCellFilter&lt;/code&gt; with &lt;code&gt;FrechetCellFilter&lt;/code&gt; in &lt;code&gt;StructOptimizer&lt;/code&gt; by @janosh in https://github.com/CederGroupHub/chgnet/pull/101&lt;/li&gt; &lt;/ul&gt; &lt;h3&gt; Enhancements&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Add &lt;code&gt;ase_filter&lt;/code&gt; keyword to &lt;code&gt;StructOptimizer.relax()&lt;/code&gt; by @janosh in https://github.com/CederGroupHub/chgnet/pull/102&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/CederGroupHub/chgnet/compare/v0.3.2...v0.3.3&lt;/p&gt

CederGroupHub/chgnet: v0.3.2

&lt;h2&gt;Changes&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;Link to &lt;a href="https://www.youtube.com/watch?v=Lm148F_1Dn4&amp;feature=youtu.be"&gt;Video tutoria&lt;/a&gt;l @BowenD-UCB&lt;/li&gt; &lt;li&gt;Allow setting MD start temperature @BowenD-UCB&lt;/li&gt; &lt;li&gt;Fixed bug triggerred by dtype @janosh in https://github.com/CederGroupHub/chgnet/pull/95&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/CederGroupHub/chgnet/compare/V0.3.1...v0.3.2&lt;/p&gt

CederGroupHub/chgnet: V0.3.0

&lt;h2&gt;0.3.0 version: Improved pretrained weights released&lt;/h2&gt; &lt;p&gt;We release the most recent pretrained model: CHGNet 0.3.0 :tada: (&lt;a href="https://github.com/CederGroupHub/chgnet/tree/main/chgnet/pretrained/0.3.0"&gt;see details&lt;/a&gt;) &lt;code&gt;CHGNet.load()&lt;/code&gt; now defaults to '0.3.0' version.&lt;/p&gt; &lt;h2&gt;Major changes:&lt;/h2&gt; &lt;ol&gt; &lt;li&gt;Increased AtomGraph cutoff to 6A #55&lt;/li&gt; &lt;li&gt;Resolved discontinuity issue when no BondGraph presents #79&lt;/li&gt; &lt;li&gt;Added some normalization layers&lt;/li&gt; &lt;li&gt;Slight improvements on energy, force, stress accuracies&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/CederGroupHub/chgnet/compare/v0.2.2..v0.3.0&lt;/p&gt

Genome-Wide Identification of Sweet Orange WRKY Transcription Factors and Analysis of Their Expression in Response to Infection by Penicillium digitatum

WRKY transcription factors (TFs) play a vital role in plant stress signal transduction and regulate the expression of various stress resistance genes. Sweet orange (Citrus sinensis) accounts for a large proportion of the world&rsquo;s citrus industry, which has high economic value, while Penicillium digitatum is a prime pathogenic causing postharvest rot of oranges. There are few reports on how CsWRKY TFs play their regulatory roles after P. digitatum infects the fruit. In this study, we performed genome-wide identification, classification, phylogenetic and conserved domain analysis of CsWRKY TFs, visualized the structure and chromosomal localization of the encoded genes, explored the expression pattern of each CsWRKY gene under P. digitatum stress by transcriptome data, and made the functional prediction of the related genes. This study provided insight into the characteristics of 47 CsWRKY TFs, which were divided into three subfamilies and eight subgroups. TFs coding genes were unevenly distributed on nine chromosomes. The visualized results of the intron-exon structure and domain are closely related to phylogeny, and widely distributed cis-regulatory elements on each gene played a global regulatory role in gene expression. The expansion of the CSWRKY TFs family was probably facilitated by twenty-one pairs of duplicated genes, and the results of Ka/Ks calculations indicated that this gene family was primarily subjected to purifying selection during evolution. Our transcriptome data showed that 95.7% of WRKY genes were involved in the transcriptional regulation of sweet orange in response to P. digitatum infection. We obtained 15 differentially expressed genes and used the reported function of AtWRKY genes as references. They may be involved in defense against P. digitatum and other pathogens, closely related to the stress responses during plant growth and development. Two interesting genes, CsWRKY2 and CsWRKY14, were expressed more than 60 times and could be used as excellent candidate genes in sweet orange genetic improvement. This study offers a theoretical basis for the response of CSWRKY TFs to P. digitatum infection and provides a vital reference for molecular breeding