Assistant Professor (Tenure track), Department of Mathematics, University of California, Berkeley, 2014--present
Faculty Scientist, Computational Research Division, Lawrence Berkeley National Laboratory, 2014--present
Research Scientist (Career Track), Computational Research Division, Lawrence Berkeley National Laboratory, 2013--2014
Luis W. Alvarez Postdoctoral Fellow, Computational Research Division, Lawrence Berkeley National Laboratory, 2011--2013
Ph.D. Applied Mathematics, Princeton University, 2011
B.S. Mathematics, Peking University, 2007
Lin Lin received his PhD in applied and computational mathematics from Princeton University in 2011. He then joined Lawrence Berkeley National Laboratory as a Luis W. Alvarez Postdoctoral Fellow during 2011-2013, and a research scientist during 2013--2014. He is currently an assistant professor at the Department of Mathematics at University of California, Berkeley, and a faculty scientist at Lawrence Berkeley National Laboratory. His current research interest includes numerical analysis, numerical linear algebra, scientific computing, parallel computing, electronic structure theory and other quantum theories, with a focused effort on developing novel, efficient and reliable numerical algorithms and mathematical software tools for computational chemistry and materials science.
For more information, please visit his personal website:
M. Jacquelin, L. Lin and C. Yang, "A Distributed Memory Parallel Algorithm for Selected Inversion: the non-symmetric case", PMAA, December 30, 2016,
A. S. Banerjee, L. Lin, W. Hu, C. Yang and J. E. Pask, "Chebyshev polynomial filtered subspace iteration in the discontinuous Galerkin method for large-scale electronic structure calculations", Journal of Chemical Physics, October 1, 2016,
Meiyue Shao, Lin Lin, Chao Yang, Fang Liu, Felipe H. da Jornada, Jack Deslippe and Steven G. Louie, "Low rank approximation in G0W0 calculations", Science China Mathematics, June 4, 2016, 59:1593–1612, doi: 10.1007/s11425-016-0296-x
M. Jacquelin, L. Lin, W. Jia, Y. Zhao and C. Yang, "A Left-looking selected inversion algorithm and task parallelism on shared memory systems", April 9, 2016,
Wei Hu, Lin Lin, Chao Yang, Jun Dai and Jinlong Yang, "Edge-Modied Phosphorene Nano ake Heterojunctions as Highly Ecient Solar Cells", Nano Lett, February 5, 2016, 16:1675–1682, doi: 10.1021/acs.nanolett.5b04593
L. Lin, Y. Saad and C. Yang, "Approximating spectral densities of large matrices", SIAM Review, February 1, 2016, 58:34–65, doi: 10.1137/130934283
P. Li, X. Liu, M. Chen, P. Lin, X. Ren, L. Lin, C. Yang, L. He, "Large-scale ab initio simulations based on systematically improvable atomic basis", Computational Materials Science, February 1, 2016, 112:503–517, doi: doi:10.1016/j.commatsci.2015.07.004
M. van Setten; F. Carouso; S. Sharifzadeh; X. Ren; M. Scheffler; F. Liu; J. Lischner; L. Lin; J. Deslippe; S. Louie; C. Yang; F. Weigend; J. Neaton; F. Evers; P. Rinke, "GW 100: Benchmarking G0W0 for molecular systems", Journal of Chemical Theory and Computation, October 22, 2015,
Jiri Brabec, Lin Lin, Meiyue Shao, Niranjan Govind, Chao Yang, Yousef Saad, Esmond G. Ng, "Fast Algorithms for Estimating the Absorption Spectrum within Linear Response Time-dependent Density Functional Theory", Journal of Chemical Theory and Computation, 2015, 11:5197–5208, doi: 10.1021/acs.jctc.5b00887
Mathias Jacquelin, Lin Lin, Chao Yang, "A Distributed Memory Parallel Algorithm for Selected Inversion : the Symmetric Case", To appear in ACM Transactions on Mathematical Software (TOMS), May 28, 2015,
Fang Liu, Lin Lin , Derek Vigil-Fowlerd , Johannes Lischnerd, Alexander F. Kemper, , Sahar Sharifzadehe, Felipe H. da Jornadad, Jack Deslippef, Chao Yangc, Jeffrey B. Neaton, Steven G. Louied,, "Numerical integration for ab initio many-electron self energy calculations within the GW approximation", Journal of Computational Physics, April 1, 2015,
Wei Hu, Lin Lin and Chao Yang, "Edge reconstruction in armchair phosphorene nanoribbons revealed by discontinuous Galerkin density functional theory", Phys. Chem. Chem. Phys., 2015, Advance Article, February 11, 2015, doi: 10.1039/C5CP00333D
With the help of our recently developed massively parallel DGDFT (Discontinuous Galerkin Density Functional Theory) methodology, we perform large-scale Kohn–Sham density functional theory calculations on phosphorene nanoribbons with armchair edges (ACPNRs) containing a few thousands to ten thousand atoms. The use of DGDFT allows us to systematically achieve a conventional plane wave basis set type of accuracy, but with a much smaller number (about 15) of adaptive local basis (ALB) functions per atom for this system. The relatively small number of degrees of freedom required to represent the Kohn–Sham Hamiltonian, together with the use of the pole expansion the selected inversion (PEXSI) technique that circumvents the need to diagonalize the Hamiltonian, results in a highly efficient and scalable computational scheme for analyzing the electronic structures of ACPNRs as well as their dynamics. The total wall clock time for calculating the electronic structures of large-scale ACPNRs containing 1080–10 800 atoms is only 10–25 s per self-consistent field (SCF) iteration, with accuracy fully comparable to that obtained from conventional planewave DFT calculations. For the ACPNR system, we observe that the DGDFT methodology can scale to 5000–50 000 processors. We use DGDFT based ab initio molecular dynamics (AIMD) calculations to study the thermodynamic stability of ACPNRs. Our calculations reveal that a 2 × 1 edge reconstruction appears in ACPNRs at room temperature.
Wei Hu, Lin Lin, Chao Yang and Jinlong Yang, "Electronic structure and aromaticity of large-scale hexagonal graphene nanoflakes", J. Chem. Phys. 141, 214704 (2014), December 2, 2014, 141:214704, doi: 10.1063/1.4902806
- Download File: JCPGNFs.pdf (pdf: 3.7 MB)
With the help of the recently developed SIESTA-PEXSI method [L. Lin, A. García, G. Huhs, and C. Yang, J. Phys.: Condens. Matter26, 305503 (2014)], we perform Kohn-Sham density functional theory calculations to study the stability and electronic structure of hydrogen passivated hexagonal graphene nanoflakes (GNFs) with up to 11 700 atoms. We find the electronic properties of GNFs, including their cohesive energy, edge formation energy, highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap, edge states, and aromaticity, depend sensitively on the type of edges (armchair graphene nanoflakes (ACGNFs) and zigzag graphene nanoflakes (ZZGNFs)), size and the number of electrons. We observe that, due to the edge-induced strain effect in ACGNFs, large-scale ACGNFs’ edge formation energydecreases as their size increases. This trend does not hold for ZZGNFs due to the presence of many edge states in ZZGNFs. We find that the energy gaps E g of GNFs all decay with respect to 1/L, where L is the size of the GNF, in a linear fashion. But as their size increases, ZZGNFs exhibit more localized edge states. We believe the presence of these states makes their gap decrease more rapidly. In particular, when L is larger than 6.40 nm, we find that ZZGNFs exhibit metallic characteristics. Furthermore, we find that the aromatic structures of GNFs appear to depend only on whether the system has 4N or 4N + 2 electrons, where N is an integer.