In organic molecules, the atoms that make up chemical bonds or functional groups are constantly vibrating, and their vibration frequency is equivalent to that of infrared light. Therefore, the molecule selectively absorbs certain wavelengths of infrared rays, and the chemical bonds or functional groups in the molecule can undergo vibrational absorption. Different chemical bonds or functional groups have different absorption frequencies and will be present in different positions of the infrared spectrum. Thus, it is possible to obtain important information on chemical bond or functional group form IR spectrum. The absorption of infrared rays by molecules will cause the vibrational and rotational energy levels in the molecules to undergo transitions. The infrared spectrum of the substance can be obtained by detecting the absorption of infrared rays, so IR spectrum is also known as molecular vibrational spectrum or vibrational rotation spectrum.
Figure 1. Calculated vs. experimental IR spectrum of pyrimidine in CS2 solution. (Barone, V.; et al. 2012)
Our IR Spectrum Prediction Workflow
1. Model building
We select the density-functional theory (DFT) method and B3LYP/6-311G(d,p) basis set in Gaussian software.
2. Infrared spectrum calculation
After setting the calculation parameters, we use the correction factor in the vibration spectrum panel to make the value of vibration frequency closer to the experimental value, thereby obtaining the corrected frequency.
3. Vibration mode analysis
Our groups perform analysis of the vibration modes corresponding to all vibration frequencies.
4. Spectral peak identification
5. Infrared spectrum generation
At Alfa Chemistry, we mainly apply ab initio molecular dynamics, machine learning and DFT method for the IR spectrum prediction, assisting in the follow-up chemical characterization and identification. Our fast and high-quality services include the following:
- Ab initio molecular dynamics
Our scientists have developed an ab initio molecular dynamic to predict the near-infrared spectra. All the dynamics are carried out using the first principles method, and we apply DFT for solving the quantum problem for the electrons. DFT forces are also integrated following the Born-Oppenheimer dynamics. Near-IR spectra is able to be predicted by the Fourier transform of the macroscopic polarization autocorrelation function after the dynamics.
- Machine learning
Alfa Chemistry designs a machine-learning protocol to correlate spectral fingerprints with local molecular structures. Our experts can conduct rapid and accurate prediction of infrared absorption spectra based on molecular structures. Moreover, we can complete the structure recognition of chemical groups from vibrational spectral features using it. IR spectral features arising from different selection rules can be recurrently fed to the model to achieve a nearly zero error rate in structure recognition.
- Experimental database assisted-DFT method
In order to improve the computation of infrared spectra of gas-phase cations using DFT calculation, Alfa Chemistry offers experimental IRMPD spectra for various organometallic complexes to provide reference data for different vibrational modes range. Furthermore, we apply multiple DFT functionals and basis sets to assess and improve the accuracy of the IR vibrational frequencies predicted for these bands.
IR spectrum prediction provides accurate fluorescence spectra for the subsequent chemical characterization and identification. Our IR spectrum prediction services remarkably reduce the cost, promote further experiments, and enhance the understanding of chemical process for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
- Barone, V.; et al. Implementation and validation of a multi-purpose virtual spectrometer for large systems in complex environments. Physical Chemistry Chemical Physics. 2012, 14(36): 12404-12422.