How Computational Chemistry is Predicting NMR and Acidity in Solid Catalysts
Imagine trying to understand a complex lock mechanism by only examining the keys that open it. This is the challenge chemists face daily when studying solid catalysts—materials that accelerate chemical reactions without being consumed themselves. These invisible workhorses are responsible for 90% of all chemical industrial processes, from producing life-saving pharmaceuticals to refining fuels and creating new materials. Yet, their atomic-scale workings remain largely hidden from direct observation.
Solid catalysts enable 90% of industrial chemical processes, driving pharmaceutical, fuel, and material production.
Nuclear Magnetic Resonance provides atomic-level insights into molecular structures and environments.
The scientific community has long relied on Nuclear Magnetic Resonance (NMR) spectroscopy as their "eyes" into the molecular world. NMR allows researchers to probe the local environment of atoms within molecules, much like how MRI reveals internal structures of the human body. However, interpreting NMR data requires matching experimental results to predictions from possible molecular structures—a process that has traditionally been slow, expensive, and often imprecise. The advent of quantum chemical calculations has revolutionized this field, enabling researchers to peer deeper into the quantum realm of catalysts with unprecedented clarity and speed, bridging the gap between theoretical models and experimental reality 5 .
At its heart, NMR spectroscopy is fundamentally a quantum phenomenon that reveals the intricate dance of atomic nuclei in a magnetic field. The "spin" property of nuclei—an intrinsic quantum characteristic—makes NMR possible. As physicist Hongjun Zhou from UCSB poetically notes, "spin is an intrinsic property of an object, like mass, charge... and it possesses a unique property that is fully defined by its rotational transformation" 6 . This quantum spin property emerges naturally from Dirac's relativistic quantum equation, proving that the quantum world isn't just a theoretical curiosity but has tangible effects we can measure and utilize 6 .
Quantum spin alignment in magnetic fields creates measurable NMR signals.
For decades, Density Functional Theory (DFT) has been the workhorse for predicting NMR parameters from first principles. DFT solves the complex quantum mechanical equations that describe how electrons are arranged around molecules, allowing researchers to calculate chemical shifts and other NMR parameters 5 . The accuracy is remarkable—the best DFT methods can reproduce experimental NMR chemical shifts to within 1-2% of the appropriate parameter ranges (0.2-0.3 ppm for ¹H and 2-4 ppm for ¹³C shifts across ranges of ~10 and ~200 ppm, respectively) .
However, this precision comes at a tremendous computational cost. Traditional DFT calculations require hours to days of supercomputer time for a single molecule of moderate size (30-40 non-hydrogen atoms). When multiple conformations or isomers must be considered—as is almost always the case—the computation time can stretch to "months of computation for a single study" , creating a significant bottleneck in chemical research and catalyst development.
In 2025, a team of researchers addressed the computational bottleneck head-on with the development of IMPRESSION-Generation 2 (G2), a transformer-based neural network that serves as a much faster alternative to high-level DFT calculations 1 . This system represents a paradigm shift in computational NMR, being the first to simultaneously predict all NMR chemical shifts and scalar couplings for ¹H, ¹³C, ¹âµN, and ¹â¹F nuclei up to four bonds apart in a single prediction event starting from a 3D molecular structure 1 .
Molecules in Training Set
Complementary Data Sources
Faster Than DFT
They compiled 18,182 molecules from three complementary sources: the Cambridge Structural Database (4,799 molecules with X-ray crystal structures), ChEMBL (4,055 bioactive, drug-like molecules), and the OTAVA chemicals diversity library (9,328 drug-like molecules) .
Each molecule underwent DFT calculations at the ωB97XD/6-311g(d,p) level of theory—a sophisticated quantum chemical method known for its accuracy in predicting NMR parameters .
The team implemented a graph transformer network with attention mechanisms, allowing the model to optimize information transfer between all NMR parameters during training and inference .
The performance of IMPRESSION-G2 exceeded even the researchers' expectations. The system demonstrated remarkable accuracy when compared to both DFT benchmarks and experimental data:
| Parameter Type | Mean Absolute Deviation | Comparison to DFT |
|---|---|---|
| ¹H Chemical Shifts | ~0.07 ppm | More accurate than existing ML systems |
| ¹³C Chemical Shifts | ~0.8 ppm | More accurate than existing ML systems |
| ³JHH Scalar Couplings | <0.15 Hz | Comparable to high-level DFT |
| All Scalar Couplings | Similar DFT-level accuracy | Across all nuclei types |
Perhaps most impressively, the speed improvement was nothing short of revolutionary. IMPRESSION-G2 can predict approximately 5,000 chemical shifts and scalar couplings per molecule in under 50 milliseconds—approximately 10â¶-times faster than DFT-based NMR predictions starting from a 3D structure 1 . When combined with fast GFN2-xTB geometry optimizations to generate the 3D input structures, a complete workflow for NMR predictions on a new molecule becomes 10³–10â´ times faster than a wholly DFT-based workflow 1 .
| Method | Computation Time | Relative Speed | Hardware Requirements |
|---|---|---|---|
| Traditional DFT | Hours to days | 1× | High-performance computing cluster |
| IMPRESSION-G2 | <50 ms | 10â¶Ã— faster | Standard laptop |
| Complete Workflow (with GFN2-xTB) | Minutes | 10³-10â´Ã— faster | Standard laptop |
The implications are profound: calculations that previously required "days on a large scale High Performance Computing system" can now be completed in minutes on a standard laptop with nearly indistinguishable results 1 . This breakthrough democratizes computational NMR, making it accessible to researchers without specialized supercomputing resources.
While NMR parameters provide structural insights, for solid acid catalysts, the acidic properties determine their chemical effectiveness. These catalysts include zeolites, acid clays, metal salts, and cation exchange resins that drive essential reactions in the petrochemical and chemical industries 2 . The global solid acid catalyst market, projected to reach $65.8 million in 2025 with a 3.9% compound annual growth rate through 2033, reflects their industrial importance 2 .
Projected growth of solid acid catalyst market through 2033.
Quantum catalysts—materials whose properties cannot be explained by classical interactions alone—often exhibit distinctive catalytic behaviors frequently associated with open-shell orbital configurations and strong electronic correlations 4 . As researcher Jose Gracia notes, "Many catalysts are effectively quantum materials, and we need to achieve a complete perspective from appropriate wavefunctions" 4 . The unique properties of these quantum catalysts include strong electronic correlations, various electronic orders (superconducting, spin-orbital), and multiple coexisting interdependent phases associated with quantum phenomena like superposition and entanglement 4 .
Quantum chemical calculations excel at predicting the acidity strength and distribution in solid catalysts by modeling the electron distribution around potential acid sites. The NMR chemical shifts of specific atoms, particularly hydrogen, oxygen, and aluminum, serve as experimental proxies for acidity—lower electron density around acidic protons results in characteristic chemical shifts that quantum calculations can accurately predict.
This quantum-based approach is driving innovation in catalyst design, with major companies like Clariant, BASF, and Haldor Topsoe continuously investing in research to enhance catalyst performance and expand their product portfolios 2 .
| Tool/Resource | Function | Application in Catalyst Research |
|---|---|---|
| DFT Methods (ωB97XD, mPW1PW91) | High-accuracy quantum chemical calculations | Predicting NMR parameters, acid site electron density |
| IMPRESSION-G2 | Transformer-based neural network for NMR | Rapid prediction of chemical shifts and J-couplings |
| GFN2-xTB | Fast geometry optimization | Generating accurate 3D molecular structures in seconds |
| Solid Acid Catalysts | Reaction acceleration in petrochemical/chemical industries | Zeolites, acid clays, metal salts for industrial processes |
| Quantum Materials Theory | Modeling strong electronic correlations | Understanding quantum catalysts with open-shell configurations |
The combination of IMPRESSION-G2 with fast geometry optimization methods creates a workflow that is thousands of times faster than traditional DFT approaches while maintaining comparable accuracy.
This computational democratization enables researchers without access to supercomputing facilities to perform sophisticated NMR predictions and catalyst design on standard laptops.
The marriage of quantum chemical calculations with NMR spectroscopy has transformed our ability to understand and design solid acid catalysts at the atomic level. What was once an insurmountable computational challenge has become increasingly accessible through innovations like the IMPRESSION-G2 system. As these tools continue to evolve, they promise to accelerate the development of next-generation catalysts for a sustainable future—from more efficient fuel production to greener chemical processes.
The words of Paul Dirac seem prescient in this context: "Pick a flower on Earth and you move the farthest star" 6 . In the intricate quantum dance of atoms within solid catalysts, we're learning that subtle changes in electron distribution indeed have far-reaching effects—connecting fundamental quantum physics to industrial-scale chemical transformations that shape our world. As research continues to bridge the gap between quantum theory and catalytic practice, we stand at the threshold of a new era in materials design, powered by the invisible quantum world made visible through computational brilliance.
Atomic-level understanding of catalyst properties
Millions of times faster predictions
Greener chemical processes through better catalysts