Quantum Chemical

Quantum chemically computed infrared (IR) spectroscopy is a powerful tool in molecular analysis, especially valuable when experimental samples are limited or when spectra are complicated by overlapping bands. This technique provides deep insights into molecular structures, allowing for the identification and characterization of molecules through their IR spectra. It provides insights that might be impossible to achieve through experimental means alone. Its capability to analyze vibrational properties sheds light on the types of chemical bonds present. This methodology is essential in differentiating similar compounds and finds wide application in fields like materials science, pharmaceutical research, and environmental monitoring. The cost-effectiveness and efficiency of this method make it especially useful in scenarios where experimental challenges, such as scarce samples or complex spectral data, are present.

However, conducting quantum chemical computations for IR spectroscopy entails considerable challenges due to its complexity and the resource-intensive nature of the process. These calculations, which involve solving intricate equations, demands significant computational power and sophisticated software. Expertise in quantum chemistry is a prerequisite for accurate calculation and interpretation. The time commitment for these computations can vary significantly, potentially spanning from hours to weeks, and is dependent on the molecular size and the required accuracy level. A key challenge is the trade-off between desired accuracy and computational feasibility, as achieving higher accuracy demands more resources.

In response to the inherent complexities of quantum chemical computation, we've pioneered the QSQN technology, backed by 41 patents, to provide instant access to quantum chemically computed IR spectroscopy and corresponding vibration animations. This advanced approach seamlessly integrates Quantum Chemistry, Statistical Thermodynamics, Quantitative Structure–Property Relationships (QSPR), and Neural Networks.

The QSQN framework ensures the production of high-quality IR spectra. Our quantum chemical calculations are meticulously refined using an optimal starting geometry, calculation method, basis set, and a carefully determined scaling factor for vibrational frequencies. This scaling factor is calibrated by comparing with an extensive database of over 2,500 experimental frequencies. For an in-depth understanding of our methodology, we invite you to explore the “High-Quality Quantum Chemical Computations” section on our technology webpage.

To access the IR spectroscopy and the corresponding vibration animation using our QSQN approach, please input your target chemical compound in the search bar provided below:

The QSQN model has undergone rigorous validation against a dataset comprising over 1.5 million experimental data points. This dataset, encompassing more than 230,000 chemical compounds, was curated over a span of 5 years. In our commitment to utmost accuracy, we have stringently filtered this data, giving precedence to the most trustworthy data points. We encourage a review of our case study that offers an in-depth look at our data refinement methodology.

Our QSQN model consistently delivers high prediction accuracies. For those interested in a detailed evaluation, we have made available extensive comparisons of our QSQN outputs (denoted as ‘MOLINSTINCTS’) with experimental data.

A key strength of our QSQN approach lies in its foundation of high-quality quantum chemical computations, the initial structures of which are sourced from conformer analyses. Setting itself apart from many conventional models that rely on empirical formulations, our approach emphasizes parameters with well-defined physical meaning, rooted in fundamental scientific principles. This not only enhances the precision of our predictions but also bolsters the model's reliability, particularly in scenarios that extend beyond typical experimental conditions.

Harnessing the power of our QSQN technology, we've conducted extensive multicore computations across thousands of CPU cores. This computational effort has enabled us to amass an extensive chemical database, named 'Mol-Instincts'. Within this reservoir, users can access an array of property data, spectra data, quantum data, and molecular descriptors for a wide range of chemical compounds via our website.

Distinctive features of our 'Mol-Instincts' chemical database include:

Our 'Mol-Instincts' database has emerged as a vital resource for the global chemical community, attracting over a million users worldwide. Many of these users have utilized our data in their research endeavors, leading to publications in prestigious journals such as NATURE, ELSEVIER, Springer, American Chemical Society, Royal Society of Chemistry, and Wiley. These publications often cite our database in their references. For your convenience, we have compiled a curated list of such high-impact publications referencing our data.

The extensive datasets provided by 'Mol-Instincts' are increasingly crucial in the evolving field of chemical AI development, which faces a growing need for comprehensive chemical data – a requirement that few other platforms can fulfill. Additionally, our database fills a critical gap in niche R&D sectors that depend on specific chemical data, often uniquely available on our platform. Here’s a glimpse of what our database offers:

The 'Mol-Instincts' database represents a significant stride in chemical research and development, powered by our innovative QSQN technology. It has revolutionized the accessibility and application of chemical data, supporting advancements across various scientific domains. This tool has become indispensable for researchers, educators, and industry experts worldwide, fostering discoveries in diverse fields such as pharmaceuticals and environmental studies.

As we continue to expand and refine 'Mol-Instincts', our goal is to keep it at the forefront of chemical research, adapting to the evolving needs of the scientific community. We are committed to further enriching this dynamic resource and invite the global community of scientists and innovators to join us in this journey of discovery and progress.