Fast prediction of anharmonic vibrational spectra for complex organic molecules
Source abstract
Abstract
Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Computational Drug Discovery Methods · Spectroscopy and Quantum Chemical Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. Rachwalski, M. Special issue: asymmetry and symmetry in organic chemistry. Symmetry 15, 1363 (2023).10.3390/sym15071363 ↗
2. Carpenella, V. et al. High-pressure behavior of δ-phase of formamidinium lead iodide by optical spectroscopies. J. Phys. Chem. C. 127, 2440–2447 (2023).10.1021/acs.jpcc.2c08253 ↗
3. Cherubini, M., Monacelli, L. & Mauri, F. The microscopic origin of the anomalous isotopic properties of ice relies on the strong quantum anharmonic regime of atomic vibration. J. Chem. Phys. 155, 184502 (2021).10.1063/5.0062689 ↗
4. Monacelli, L., Errea, I., Calandra, M. & Mauri, F. Pressure and stress tensor of complex anharmonic crystals within the stochastic self-consistent harmonic approximation. Phys. Rev. B 98, 024106 (2018).10.1103/PhysRevB.98.024106 ↗
5. Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).10.1103/PhysRevLett.98.146401 ↗
6. Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).10.1103/PhysRevLett.104.136403 ↗
7. Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).10.1038/s41467-022-29939-5 ↗
8. Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).10.1039/C6SC05720A ↗
9. Smith, J. S. et al. The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules. Sci. Data 7, 134 (2020).10.1038/s41597-020-0473-z ↗
10. Fink, T. & Reymond, J.-L. Virtual exploration of the chemical universe up to 11 atoms of c, n, o, f: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 47, 342–353 (2007).10.1021/ci600423u ↗
11. Devereux, C. et al. Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens. J. Chem. Theory Comput. 16, 4192–4202 (2020).10.1021/acs.jctc.0c00121 ↗
12. Li, C. & Voth, G. A. Using machine learning to greatly accelerate path integral ab initio molecular dynamics. J. Chem. Theory Comput. 18, 599–604 (2022).10.1021/acs.jctc.1c01085 ↗
13. Bocus, M. et al. Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics. Nat. Commun. 14, 1008 (2023).10.1038/s41467-023-36666-y ↗
14. Chmiela, S. et al. Accurate global machine learning force fields for molecules with hundreds of atoms. Sci. Adv. 9, eadf0873 (2023).10.1126/sciadv.adf0873 ↗
15. Monacelli, L. & Mauri, F. Time-dependent self-consistent harmonic approximation: anharmonic nuclear quantum dynamics and time correlation functions. Phys. Rev. B 103, 104305 (2021).10.1103/PhysRevB.103.104305 ↗
16. Monacelli, L. et al. The stochastic self-consistent harmonic approximation: calculating vibrational properties of materials with full quantum and anharmonic effects. J. Phys. Condens. Matter 33, 363001 (2021).10.1088/1361-648X/ac066b ↗
17. Siciliano, A., Monacelli, L., Caldarelli, G. & Mauri, F. Wigner gaussian dynamics: Simulating the anharmonic and quantum ionic motion. Phys. Rev. B 107, 174307 (2023).10.1103/PhysRevB.107.174307 ↗
18. Long, D. A. The Raman Effect. John Wiley & Sons Ltd, 1–340 (2002). https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470845767.fmatter_indsub.
19. Popov, M. N. et al. Raman spectra of fine-grained materials from first principles. npj Comput. Mater. 6, 121 (2020).10.1038/s41524-020-00395-3 ↗
20. Giannozzi, P. et al. Advanced capabilities for materials modelling with quantum ESPRESSO. J. Phys. Condens. Matter 29, 465901 (2017).10.1088/1361-648X/aa8f79 ↗
21. Mudalip, S. K. A., Bakar, M. R. A., Adam, F. & Jamal, P. Structures and hydrogen bonding recognition of mefenamic acid form i crystals in mefenamic acid ethanol solution. Int. J. Chem. Eng. Appl. 4, 124–128 (2013).
22. Miranda, J. et al. Phase transformation in the c form of myristic-acid crystals and DFT calculations. Spectrochim. Acta A: Mol. Biomol. Spectrosc. 208, 97–108 (2019).10.1016/j.saa.2018.09.065 ↗
23. Saggu, M., Liu, J. & Patel, A. Identification of subvisible particles in biopharmaceutical formulations using raman spectroscopy provides insight into polysorbate 20 degradation pathway. Pharm. Res. 32, 2877–2888 (2015).10.1007/s11095-015-1670-x ↗
24. Errea, I. et al. Quantum crystal structure in the 250-kelvin superconducting lanthanum hydride. Nature 578, 66–69 (2020).10.1038/s41586-020-1955-z ↗
25. Monacelli, L., Errea, I., Calandra, M. & Mauri, F. Black metal hydrogen above 360GPa driven by proton quantum fluctuations. Nat. Phys. 17, 63–67 (2020).10.1038/s41567-020-1009-3 ↗
26. Monacelli, L., Casula, M., Nakano, K., Sorella, S. & Mauri, F. Quantum phase diagram of high-pressure hydrogen. Nat. Phys. 19, 845–850 (2023).10.1038/s41567-023-01960-5 ↗
27. Kapil, V., Engel, E., Rossi, M. & Ceriotti, M. Assessment of approximate methods for anharmonic free energies. J. Chem. Theory Comput. 15, 5845–5857 (2019).10.1021/acs.jctc.9b00596 ↗
28. Errea, I., Calandra, M. & Mauri, F. Anharmonic free energies and phonon dispersions from the stochastic self-consistent harmonic approximation: application to platinum and palladium hydrides. Phys. Rev. B 89, 064302 (2014).10.1103/PhysRevB.89.064302 ↗
29. Bianco, R., Errea, I., Paulatto, L., Calandra, M. & Mauri, F. Second-order structural phase transitions, free energy curvature, and temperature-dependent anharmonic phonons in the self-consistent harmonic approximation: Theory and stochastic implementation. Phys. Rev. B 96, 014111 (2017).10.1103/PhysRevB.96.014111 ↗
30. Miotto, M. & Monacelli, L. Entropy evaluation sheds light on ecosystem complexity. Phys. Rev. E 98, 042402 (2018).10.1103/PhysRevE.98.042402 ↗
31. Miotto, M. & Monacelli, L. TOLOMEO, a novel machine learning algorithm to measure information and order in correlated networks and predict their state. Entropy 23, 1138 (2021).10.3390/e23091138 ↗
32. Lazzeri, M. & Mauri, F. First-principles calculation of vibrational raman spectra in large systems: signature of small rings in crystalline sio2. Phys. Rev. Lett. 90, 036401 (2003).10.1103/PhysRevLett.90.036401 ↗
33. Bastonero, L. & Marzari, N. Automated all-functionals infrared and Raman spectra. npj Comput. Mater. 10, 55 (2024).10.1038/s41524-024-01236-3 ↗
34. Togo, A., Chaput, L., Tadano, T. & Tanaka, I. Implementation strategies in phonopy and phono3py. J. Phys. Condens. Matter 35, 353001 (2023).10.1088/1361-648X/acd831 ↗
35. Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).10.1021/acs.jcim.0c00675 ↗
36. van Uden, N. W. A. et al. Solvation pressure as real pressure: I. ethanol and starch under negative pressure. J. Phys. Condens. Matter 15, 1577–1584 (2003).10.1088/0953-8984/15/10/306 ↗
37. De Gelder, J. Raman spectroscopy as a tool for studying bacterial cell compounds. Ghent University (2008).
38. Beć, K. B. et al. IR spectra of crystalline nucleobases: combination of periodic harmonic calculations with anharmonic corrections based on finite models. J. Phys. Chem. B 123, 10001–10013 (2019).10.1021/acs.jpcb.9b06285 ↗
39. Burikov, S., Dolenko, T., Patsaeva, S., Starokurov, Y. & Yuzhakov, V. Raman and IR spectroscopy research on hydrogen bonding in water-ethanol systems. Mol. Phys. 108, 2427–2436 (2010).10.1080/00268976.2010.516277 ↗