Papers
Topics
Authors
Recent
Search
2000 character limit reached

Quantum-informed simulations for mechanics of materials: DFTB+MBD framework

Published 5 Apr 2024 in cs.CE, physics.comp-ph, and quant-ph | (2404.04216v1)

Abstract: The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors require models that rely on insights from electronic and atomic scales. In such cases, classical simplified approximations at those scales are insufficient, and quantum-based modeling is required. In this paper, we study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We base our study on a high-fidelity modeling framework that combines two computationally efficient models rooted in quantum first principles: Density Functional Tight Binding (DFTB) and many-body dispersion (MBD). The MBD model is applied to accurately describe non-covalent van der Waals interactions. Through various benchmark applications, we demonstrate the capabilities of this framework and the limitations of simplified modeling. We provide an open-source repository containing all codes, datasets, and examples presented in this work. This repository serves as a practical toolkit that we hope will support the development of future research in effective large-scale and multiscale modeling with quantum-mechanical fidelity.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. doi:https://doi.org/10.1016/j.actamat.2012.03.011.
  2. doi:https://doi.org/10.1016/j.jmps.2013.09.012.
  3. doi:https://doi.org/10.1016/j.triboint.2018.02.005.
  4. doi:https://doi.org/10.1038/nature07128.
  5. doi:https://doi.org/10.1038/nphys3017.
  6. doi:https://doi.org/10.1038/nature08812.
  7. doi:https://doi.org/10.1103/PhysRev.136.B864.
  8. doi:https://doi.org/10.1103/PhysRev.140.A1133.
  9. doi:https://doi.org/10.1103/RevModPhys.87.897.
  10. doi:https://doi.org/10.1146/annurev-matsci-070218-010143.
  11. G. Csányi, T. Albaret, M. C. Payne, A. De Vita, “learn on the fly”: A hybrid classical and quantum-mechanical molecular dynamics simulation, Physical Review Letters 93 (2004) 175503. doi:https://doi.org/10.1103/PhysRevLett.93.175503.
  12. doi:https://doi.org/10.1088/0965-0393/11/3/201.
  13. doi:https://doi.org/10.1088/0953-8984/14/4/312.
  14. doi:https://doi.org/10.1038/ncomms14621.
  15. doi:https://doi.org/10.1038/s41524-019-0189-9.
  16. doi:https://doi.org/10.1016/j.ijengsci.2020.103342.
  17. doi:https://doi.org/10.1088/0034-4885/60/12/001.
  18. doi:https://doi.org/10.1103/PhysRevB.51.12947.
  19. doi:https://doi.org/10.1103/PhysRevB.58.7260.
  20. doi:https://doi.org/10.1080/23746149.2019.1710252.
  21. doi:https://doi.org/10.1038/s41586-019-1013-x.
  22. doi:https://doi.org/10.1021/bi00483a001.
  23. doi:https://doi.org/10.1073/pnas.192252799.
  24. doi:https://doi.org/10.1103/PhysRevLett.102.073005.
  25. doi:https://doi.org/10.1021/jz402663k.
  26. doi:https://doi.org/10.1021/jz400226x.
  27. doi:https://doi.org/10.1002/anie.201301938.
  28. doi:https://doi.org/10.1103/PhysRevLett.108.236402.
  29. doi:https://doi.org/10.1038/s41467-020-15480-w.
  30. doi:https://doi.org/10.1126/science.aae0509.
  31. doi:https://doi.org/10.1021/acs.jpclett.7b03234.
  32. doi:https://doi.org/10.1126/sciadv.aax0024.
  33. doi:https://doi.org/10.1093/oso/9780195092769.001.0001.
  34. doi:https://doi.org/10.1103/RevModPhys.64.1045.
  35. doi:https://doi.org/10.1021/jp074167r.
  36. doi:https://doi.org/10.1002/piuz.19780090109.
  37. doi:https://doi.org/10.1063/1.4789814.
  38. doi:https://doi.org/10.1039/B600027D.
  39. doi:https://doi.org/10.1063/1.4865104.
  40. doi:https://doi.org/10.1007/BF00549096.
  41. doi:https://doi.org/10.1021/jp952841b.
  42. doi:https://doi.org/10.1103/PhysRevB.75.045407.
  43. doi:https://doi.org/10.1063/1.5143190.
  44. https://github.com/libmbd/libmbd, libmbd source code.
  45. doi:https://doi.org/10.1103/PhysRevLett.128.106101.
  46. doi:https://doi.org/10.1103/PhysRevLett.113.055701.
  47. doi:https://doi.org/10.1103/PhysRevResearch.5.L012028.
  48. doi:https://doi.org/10.1103/PhysRevB.58.14013.
  49. doi:https://doi.org/10.3390/polym13071047.
  50. doi:https://doi.org/10.1126/science.aat7439.
  51. doi:https://doi.org/10.1088/1361-648X/aaa3cc.
  52. doi:https://doi.org/10.1016/j.ijengsci.2019.103137.
  53. doi:https://doi.org/10.1007/978-3-319-01201-8_2.
  54. doi:https://doi.org/10.1021/ja00124a002.
  55. doi:https://doi.org/10.1002/jcc.20303.
  56. doi:https://doi.org/10.1021/ja00214a001.
  57. doi:https://doi.org/10.1103/PhysRevB.31.5262.
  58. doi:https://doi.org/10.1016/j.commatsci.2009.12.007.
  59. doi:https://doi.org/10.1016/B978-0-08-100406-7.00001-5.
  60. doi:https://doi.org/10.1016/B978-0-12-429851-4.X5000-1.
Citations (2)

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We found no open problems mentioned in this paper.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.

Tweets

Sign up for free to view the 1 tweet with 15 likes about this paper.