add two papers

_publications/28-Ying_JPhysCondensMatter_2024.md

@@ -12,6 +12,7 @@ Machine-learned potentials (MLPs) have become a popular approach of modeling int
 
 [Download paper here](http://hityingph.github.io/files/28-Ying_JPhysCondensMatter_2024.pdf)
 
+[Download erratum here](https://iopscience.iop.org/article/10.1088/1361-648X/ad2c75/pdf)
 
 [Training datasets for the NEP model](https://gitlab.com/brucefan1983/nep-data)
 

_publications/31-Fan_JPhysCondensMatter_2024.md

@@ -0,0 +1,13 @@
+---
+title: "Fan, Z.; Xiao, Y.; Wang, Y.; Ying, P.; Chen, S.; Dong, H. Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials. J Phys Condens Matter 2024. DOI: 10.1088/1361-648X/ad31c2"
+collection: publications
+permalink: /publication/31-Fan_JPhysCondensMatter_2024
+excerpt: 'We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials.'
+date: 2024-03-21
+venue: 'Journal of Physics: Condensed Matter'
+---
+
+We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset.
+
+[Download paper here](http://hityingph.github.io/files/31-Fan_JPhysCondensMatter_2024.pdf)
+

_publications/32-Ying_ACSNano_2024.md

@@ -0,0 +1,17 @@
+---
+title: "Ying, P.; Natan, A.; Hod, O.; Urbakh, M. Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study. ACS Nano 2024, 18 (14), 10133-10141. DOI: 10.1021/acsnano.3c1309"
+collection: publications
+permalink: /publication/32-Ying_ACSNano_2024
+excerpt: 'We present a machine-learning potential (MLP) for bilayer defected graphene, utilizing state-of-the-art graph neural networks trained against many-body dispersion corrected density functional theory calculations under iterative configuration space exploration. '
+date: 2024-03-28
+venue: 'ACS Nano'
+---
+
+Surface defects and their mutual interactions are anticipated to affect the superlubric sliding of incommensurate layered material interfaces. Atomistic understanding of this phenomenon is limited due to the high computational cost of ab initio simulations and the absence of reliable classical force-fields for molecular dynamics simulations of defected systems. To address this, we present a machine-learning potential (MLP) for bilayer defected graphene, utilizing state-of-the-art graph neural networks trained against many-body dispersion corrected density functional theory calculations under iterative configuration space exploration. The developed MLP is utilized to study the impact of interlayer bonding on the friction of bilayer defected graphene interfaces. While a mild effect on the sliding dynamics of aligned graphene interfaces is observed, the friction coefficients of incommensurate graphene interfaces are found to significantly increase due to interlayer bonding, nearly pushing the system out of the superlubric regime. The methodology utilized herein is of general nature and can be adapted to describe other homogeneous and heterogeneous defected layered material interfaces.
+
+[Download paper here](http://hityingph.github.io/files/32-Ying_ACSNano_2024.pdf)
+
+[Download SI here](https://pubs.acs.org/doi/10.1021/acsnano.3c13099)
+
+[Download reeference datasets and MLPs here](https://zenodo.org/records/10374206)
+