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.
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).
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).
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).
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).
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]
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)
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)
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)
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)
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)
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)
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)