- Access by Xinjiang University
Identifying polymer states by machine learning
Phys. Rev. E 95, 032504 – Published 30 March, 2017
DOI: https://doi.org/10.1103/PhysRevE.95.032504
Abstract
The ability of a feed-forward neural network to learn and classify different states of polymer configurations is systematically explored. Performing numerical experiments, we find that a simple network model can, after adequate training, recognize multiple structures, including gaslike coil, liquidlike globular, and crystalline anti-Mackay and Mackay structures. The network can be trained to identify the transition points between various states, which compare well with those identified by independent specific-heat calculations. Our study demonstrates that neural networks provide an unconventional tool to study the phase transitions in polymeric systems.
Physics Subject Headings (PhySH)
Article Text
References (35)
- W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, AI Mag. 13, 57 (1992).
- T. L. H. Watkin, A. Rau, and M. Biehl, Rev. Mod. Phys. 65, 499 (1993).
- I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, San Francisco, CA, 2005).
- R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine Learning: An Artificial Intelligence Approach (Springer Science & Business Media, Berlin, 2013).
- K. P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA, 2012).
- M. Jordan and T. Mitchell, Science 349, 255 (2015).
- K. Fukushima, Neural Netw. 1, 119 (1988).
- H. S. Seung, H. Sompolinsky, and N. Tishby, Phys. Rev. A 45, 6056 (1992).
- R. Parekh, J. Yang, and V. Honavar, IEEE Trans. Neural Netw. 11, 436 (2000).
- A. K. Jain, R. P. W. Duin, and J. Mao, IEEE Trans. Pattern Anal. Mach. Intell. 22, 4 (2000).
- C. Davatzikos, K. Ruparel, Y. Fan, D. Shen, M. Acharyya, J. Loughead, R. Gur, and D. D. Langleben, Neuroimage 28, 663 (2005).
- C. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2007).
- J. Schmidhuber, Neural Netw. 61, 85 (2015).
- Y. LeCun, L. Jackel, L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. Muller, E. Sackinger, P. Simard et al., Neural Netw. 261, 276 (1995).
- M. A. Nielsen, http://neuralnetworksanddeeplearning.com/.
- R. P. Lippmann, Neural Comput. 1, 1 (1989).
- G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al., IEEE Signal Process. Mag. 29, 82 (2012).
- J. C. Snyder, M. Rupp, K. Hansen, K.-R. Müller, and K. Burke, Phys. Rev. Lett. 108, 253002 (2012).
- J. Behler and M. Parrinello, Phys. Rev. Lett. 98, 146401 (2007).
- P. Geiger and C. Dellago, J. Chem. Phys. 139, 164105 (2013).
- L.-F. Arsenault, A. Lopez-Bezanilla, O. A. von Lilienfeld, and A. J. Millis, Phys. Rev. B 90, 155136 (2014).
- J. Carrasquilla and R. G. Melko, Nat. Phys., doi:10.1038/nphys4035.
- G. Torlai and R. G. Melko, Phys. Rev. B 94, 165134 (2016).
- L. Huang and L. Wang, Phys. Rev. B 95, 035105 (2017).
- J. Liu, Y. Qi, Z. Y. Meng, and L. Fu, Phys. Rev. B 95, 041101 (2017).
- N. Portman and I. Tamblyn, arXiv:1611.05891 (2016).
- P. Mehta and D. J. Schwab, arXiv:1410.3831 (2014).
- H. W. Lin and M. Tegmark, arXiv:1608.08225 (2016).
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, J. Mach. Learn. Res. 15, 1929 (2014).
- S. Schnabel, T. Vogel, M. Bachmann, and W. Janke, Chem. Phys. Lett. 476, 201 (2009).
- D. T. Seaton, T. Wüst, and D. P. Landau, Phys. Rev. E 81, 011802 (2010).
- S. Schnabel, D. T. Seaton, D. P. Landau, and M. Bachmann, Phys. Rev. E 84, 011127 (2011).
- M. Doi and S. F. Edwards, The Theory of Polymer Dynamics (Oxford University Press, New York, 1986).
- F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001).
- F. Wang and D. P. Landau, Phys. Rev. E 64, 056101 (2001).