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Ying Wai Li
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Ying Wai Li
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Publications

(link to Google Scholar)

57. Best of both worlds: enforcing detailed balance in machine learning models of transition rates

A. Talapatra, A. Pandey, M. Wilson, Y. W. Li, B. P. Uberuaga, D. Perez, and G. Pilania

npj Computational Materials, submitted.

56. High-throughput search and prediction of layered 4f-materials

L. Hou, Y. W. Li, and C. Lane

npj Computational Materials, submitted.

55. Challenging and improving machine learning potentials for transition path sampling: alanine dipeptide and azobenzene studies

N. Fedik, W. Li, N. Lubbers, B. Nebgen, S. Tretiak, and Y. W. Li

Digital Discovery, submitted.

54. Data generation for machine learning interatomic potentials and beyond

M. Kulichenko, B. Nebgen, N. Lubbers, J. Smith, K. Barros, A. Allen, A. Habib, E. Shinkle, N. Fedik, Y. W. Li, R. Messerly, and S. Tretiak

Chemical Reviews, submitted.

53. Quantification of heterogeneity in human CD8+ T cell responses to vaccine antigens: an HLA-guided perspective

D. C. Harris, A. Shanker, M. M. Montoya, T. R. Llewellyn, A. R. Matuszak, A. Lohar, J. Z. Kubicek-Sutherland, Y. W. Li, K. Wilding, B. Mcmahon, S. Gnanakaran, R. M. Ribeiro, A. S. Perelson, and C. Molina-París

Front. Immunol. 15, 1420284 (2024).

52. Machine learning potentials with the iterative Boltzmann inversion: training to experiment

S. Matin, A. E. A. Allen, J. Smith, N. Lubbers, R. B. Jadrich, R. Messerly, B. Nebgen, Y. W. Li, S. Tretiak, and K. Barros

J. Chem. Theory Comput., 20, 3, 1274-1281 (2024).

51. Quantum-to-classical crossover in generalized spin systems: temperature-dependent spin dynamics of FeI2

D. Dahlbom, F. T. Brooks, M. S. Wilson, S. Chi, A. I. Kolesnikov, M. B. Stone, H. Cao, Y.-W. Li, K. Barros, M. Mourigal, C. D. Batista, and X. Bai

Phys. Rev. B 109, 014427 (2024).

50. OMNIINPUT: A model-centric evaluation framework through output distribution

W. Liu, Y. W. Li, T Wang, Y.-Z. You, and J. Zhang

arXiv:2312.03291 (2023).

49. Synergy of semiempirical models and machine learning in computational chemistry [Editor's pick]

N. Fedik, B. Nebgen, N. Lubbers, K. Barros, M. Kulichenko, Y. W. Li, R. Zubatyuk, R. Messerly, O. Isayev, and S. Tretiak

J. Chem. Phys. 159, 110901 (2023).

48. Gradient-based Wang–Landau algorithm: a novel sampler for output distribution of neural networks over the input space

W. Liu, Y.-Z. You, Y. W. Li, and J. Zhang

Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202:22338-22351 (2023).

47. HPC application performance prediction with machine learning on new architectures

D. Yokelson, M. R. J. Charest, and Y. W. Li

In Proceedings of the 3rd Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn Strategy (PERMAVOST '23). Association for Computing Machinery, New York, NY, USA, 1–8 (2023).

46. Uncertainty driven dynamics for active learning of interatomic potentials

M. Kulichenko, K. Barros, N. Lubbers, Y. W. Li, R. Messerly, S. Tretiak, J. S. Smith, and B. Nebgen

Nat. Comput. Sci. 3, 230-239 (2023).

45. Machine learning for molecular properties: going beyond interatomic potentials

N. Fedik, R. Zubatyuk, N. Lubbers, J. S. Smith, B. Nebgen, R. Messerly, Y. W. Li, M. Kulichenko, A. I. Boldyrev, K. Barros, O. Isayev, and S. Tretiak

Nat. Rev. Chem. 6, 653–672 (2022).

44. Performance analysis of CP2K code for ab initio molecular dynamics on CPUs and GPUs

D. Yokelson, N. Tkachenko, R. Robey, Y. W. Li, and P. Dub

J. Chem. Inf. Model 62, 10, 2378–2386 (2022).

43.  Supervised and unsupervised machine learning of structural phases of polymers adsorbed to nanowires

Q. Parker, D. Perera, Y. W. Li, and T. Vogel

Phys. Rev. E 105, 035304 (2022).

42. Unintended consequence of topochemical reduction of SrFeO3 to SrFeO2: design of infinite layered-oxides

T. Ferreira, S. R. Acharya, Y. W. Li, D. Parker, A. S. Sefat, and V. R. Cooper

Phys. Rev. Mater. 5, 123401 (2021).

41. Machine-learning accelerated studies of materials with high-performance and edge computing

Y. W. Li, P. W. Doak, G. Balduzzi, W. Elwasif, E. F. D’Azevedo, and T. A. Maier 

In: Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation. Smoky Mountains Computational Sciences and Engineering Conference (SMC 2021). Communications in Computer and Information Science, vol 1512. Springer, Cham. 

40. Hands-on with OWL: the Oak–Ridge Wang–Landau Monte Carlo software suite

Y. W. Li, K. C. Pitike, M. Eisenbach, and V. R. Cooper

J. Phys.: Conf. Ser.  2122, 012001 (2021).

39. The rise of neural networks for materials and chemical dynamics

M. Kulichenko, J. S. Smith, B. Nebgen, Y. W. Li, N. Fedik, A. I. Boldyrev, N. Lubbers, K. Barros, and S. Tretiak

J. Phys. Chem. Lett. 12, 6227–6243 (2021).

38.  A scalable constructive algorithm for the optimization of neural network architectures

M. Lupo Pasini, J. Yin, Y. W. Li, and M. Eisenbach

Parallel Comput. 104-105, 102788 (2021).

37.  Fast and stable deep-learning predictions of material properties for solid solution alloys

M. Lupo Pasini, Y. W. Li, J. Yin, J. Zhang, K. M. Barros, and M. Eisenbach

J. Phys. Condens. Matter 33, 084005 (2021).

36.  Multiscale simulation of plasma flows using active learning

A. Diaw, K. Barros, J. Haack, C. Junghans, B. Keenan, Y. W. Li, D. Livescu, N. Lubbers, M. McKerns, R. S. Pavel, D. Rosenberger, I. Sagert, and T. C. Germann

Phys. Rev. E 102, 023310 (2020). 

35.  Machine learning assisted insight to spin ice Dy2Ti2O7

A. Samarakoon, K. M. Barros, Y. W. Li, M. Eisenbach, Q. Zhang, F. Ye, Z. Dun, H. Zhou, S. A. Grigera, C. D. Batista, and D. A. Tennant

Nature Communications 11, 892 (2020).

34.  Pre-exascale accelerated application development: the ORNL Summit experience

L. Luo et al.

IBM Journal of Research and Development 64, 11:1 - 11:21 (2020).

33.  Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system

S. Das, P. Motamarri, V. Gavini, B. Turcksin, Y. W. Li, and B. Leback

In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '19). ACM, New York, NY, USA, Article 2, 11 pages (2019). [Gordon Bell Finalist]

32.  Accelerating DCA++ (Dynamical Cluster Approximation) scientific application on the Summit supercomputer

G. Balduzzi, A. Chatterjee, Y. W. Li, P. W. Doak, U. Haehner, E.F. D'Azevedo, T. A. Maier, and T. Schulthess

2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT), Seattle, WA, USA, pp. 433-444 (2019).

31.  Histogram-free multicanonical Monte Carlo sampling to calculate the density of states

A. C. K. Farris, Y. W. Li, and M. Eisenbach

Comput. Phys. Commun. 235, 297-304 (2019)

30.  A practical guide to replica-exchange Wang-Landau simulations

T. Vogel, Y. W. Li, and D. P. Landau

J. Phys.: Conf. Ser. 1012 (1), 012003 (2018)

29.  Influence of substrate pattern on the adsorption of HP lattice proteins

M. S. Wilson, G. Shi, T. Wüst, Y. W. Li, and D. P. Landau

Mol. Simulat. 44, 1025 -1030 (2018)

28.  QMCPACK: An open source ab initio Quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids

J. Kim et al.

J. Phys. Condens. Matter. 30 (19), 195901 (2018)

27.  Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

T. McDaniel, E. D'Avezedo, Y. W. Li, P. R. C. Kent, and K. Wong

J. Chem. Phys. 147, 174107 (2017)

26.  A histogram-free multicanonical Monte Carlo algorithm for the basis expansion of density of states

Y. W. Li, and M. Eisenbach

in Proceedings of the Platform for Advanced Scientific Computing Conference (PASC '17). Association for Computing Machinery (ACM), New York, NY, USA, Article 10, 7 pages (2017)

25.  Towards an accurate description of perovskite ferroelectrics: exchange and correlation effects

S. Yuk, K. Pitike, S. Nakhmanson, M. Eisenbach, Y. W. Li, and V. R. Cooper

Scientific Reports 7, 43482 (2017)

24.  Delayed update algorithms for quantum Monte Carlo simulation on GPU

T. McDaniel, E. D'Avezedo, Y. W. Li, P. Kent, M. Wong, and K. Wong

Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale, Association for Computing Machinery (ACM), New York, NY, USA, 13:1-13:4 (2016)

23.  Using C++ AMP to accelerate HPC applications on Multiple Platforms

M. G. Lopez, C. Bergstrom, Y. W. Li, W. Elwasif, and O. Hernandez

in "High Performance Computing", M. Taufer, B. Mohr, J. Kunkel (Eds.), ISC High Performance 2016. Lecture Notes in Computer Science, vol 9945, Springer, pp. 563-576 (2016)

22.  Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space

A. Valentim, J. C. S. Rocha, S.-H. Tsai, Y. W. Li, M. Eisenbach, C. E. Fiore, and D. P. Landau

J. Phys.: Conf. Ser. 640, 012006 (2015)

21.  Effect of surface attractive strength on structural transitions of a confined HP lattice protein

B. Pattanasiri, Y. W. Li, T. Wüst, and D. P. Landau

J. Phys.: Conf. Ser. 640, 012015 (2015)

20.  Protein folding of the H0P model: a parallel Wang-Landau study

G. Shi, T. Wüst, Y. W. Li, and D. P. Landau

J. Phys.: Conf. Ser. 640, 012017 (2015)

19.  Replica-exchange Wang-Landau sampling: pushing the limits of Monte Carlo simulations in materials sciences

D. Perera, Y. W. Li, M. Eisenbach, T. Vogel, and D. P. Landau

TMS2015 Supplemental Proceedings, John Wiley & Sons, Inc., Hoboken, NJ, USA, P. 811-818 (2015)

18.  Effect of single-site mutations on HP lattice proteins             [selected as a PRE Editors' Suggestion]

G. Shi, T. Vogel, T. Wüst, Y. W. Li, and D. P. Landau

Phys. Rev. E. 90, 033307 (2014)

17.  A fast, replica-exchange framework for Wang-Landau sampling    [displayed in PRE's Kaleidoscope]

T. Vogel, Y. W. Li, T. Wüst, and D. P. Landau

Phys. Rev. E. 90, 023302 (2014)

16.  Performance of replica-exchange Wang-Landau sampling for the 2D Ising model: A brief survey

Y. Zhao, S. W. Cheung, Y. W. Li, and M. Eisenbach

Physics Procedia 57C, 43-47 (2014)

15.  A new paradigm for petascale Monte Carlo simulation: Replica exchange Wang-Landau sampling

Y. W. Li, T. Vogel, T. Wüst, and D. P. Landau

J. Phys.: Conf. Ser. 510, 012012 (2014)

14.  Exploring new frontiers in statistical physics with new, parallel Wang-Landau framework

T. Vogel, Y. W. Li, T. Wüst, and D. P. Landau

J. Phys.: Conf. Ser. 487, 012001 (2014)

13.  Wang-Landau sampling of the interplay between surface adsorption and folding of HP lattice proteins

Y. W. Li, T. Wüst, and D. P. Landau

Mol. Simulat. 40, 640-655 (2014)

12.  Thermodynamics and structural properties of a confined HP protein determined by Wang-Landau simulation

B. Pattanasiri, Y. W. Li, D. P. Landau, T. Wüst, and W. Triampo

J. Phys.: Conf. Ser. 454, 012071 (2013)

11.  Generic, hierarchical framework for massively parallel Wang-Landau sampling

T. Vogel, Y. W. Li, T. Wüst, and D. P. Landau

Phys. Rev. Lett. 110, 210603 (2013)

10.  Generic folding and transition hierarchies for surface adsorption of HP lattice model proteins

Y. W. Li, T. Wüst, and D. P. Landau

Phys. Rev. E 87, 012706 (2013)

9.   Conformational transitions of a confined lattice protein: A Wang-Landau study

B. Pattanasiri, Y. W. Li, D. P. Landau, T. Wüst, and W. Triampo

J. Phys.: Conf. Ser. 402, 012048 (2012)

8.   Surface adsorption of lattice HP proteins: thermodynamics and structural transitions using Wang-Landau sampling

Y. W. Li, T. Wüst, and D. P. Landau

J. Phys.: Conf. Ser. 402, 012046 (2012)

7.   Wang-Landau simulations of adsorbed and confined lattice proteins

B. Pattanasiri, Y. W. Li, D. P. Landau, and T. Wüst

Int. J. Mod. Phys. C 23, 1240008 (2012)

6.   Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds

Y. Wang, A. Manzour, P. Shareghi, T. I. Shaw, Y. W. Li, R. L. Malmberg, and L. Cai

BMC Bioinformatics 13 (Suppl 5), S1 (2012)

5.   Unraveling the beautiful complexity of simple lattice model polymers and proteins using Wang-Landau sampling

T. Wüst, Y. W. Li, and D. P. Landau

J. Stat. Phys. 144, 638-651 (2011)

4.   Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds

Y. Wang, A. Manzour, P. Shareghi, T. I. Shaw, Y. W. Li, R. L. Malmberg, and L. Cai

The Proceedings of 1st IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 184-189 (2011)

3.   Monte Carlo simulations of the HP model (the "Ising model" of protein folding)

Y. W. Li, T. Wüst, and D. P. Landau

Comput. Phys. Commun. 182, 1896-1899 (2011)

2.   Numerical integration using Wang-Landau sampling

Y. W. Li, T. Wüst, D. P. Landau, and H. Q. Lin

Comput. Phys. Commun. 177, 524 (2007)

1.   Fidelity, dynamic structure factor, and susceptibility in critical phenomena

W. L. You, Y. W. Li, and S. J. Gu

Phys. Rev. E 76, 022101 (2007)

Book Chapters

2.   Development of QMCPACK for Exascale Scientific Computing

A. Benali, D. M. Ceperley, E. D’Azevedo, M. Dewing, P. R. C. Kent, J. Kim, J. T. Krogel, Y. W. Li, Y. Luo, T. McDaniel, M. A. Morales, A. Mathuria, L. Shulenburger, and N. M. Tubman

in "Exascale Scientific Applications: Programming Approaches for Scalability, Performance, and Prortability", CRC / Taylor and Francis, Chapter 21, pp. 461-480 (2017)

1.   Biologically inspired surface physics: The HP protein model

Y. W. Li, T. Wüst, and D. P. Landau

in "Nanophenomena at Surfaces: Fundamentals of Exotic Condensed Matter Properties", M. Michailov (Ed.), Springer Series in Surface Sciences, vol. 47, Springer-Verlag, Berlin, Heidelberg, Chapter 7, pp.169-183 (2011)

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