In 1800, a chemist discovered an explosive substance that would eventually help crack open one of chemistry's most frustrating puzzles. More than two centuries later, that same molecule is exposing fundamental problems in how we predict chemical behavior using computers.

The molecule is fulminic acid, containing just four atoms: hydrogen, carbon, nitrogen, and oxygen. Despite its simplicity, this tiny chemical has been driving computational chemists to distraction for decades. Now, researchers at the University of Georgia have conducted the most comprehensive test yet of modern computational chemistry methods, and the results reveal an uncomfortable truth about the tools scientists rely on every day.

A 200 Year Old Mystery

The story begins with Edward Howard, who in 1800 discovered certain salts that exploded violently when struck. He called them "fulminating" after the Latin word for lightning. These compounds caught the attention of prominent chemists like Justus von Liebig, who discovered something peculiar: fulminic acid had exactly the same chemical formula as cyanic acid, yet the two substances behaved completely differently.

This observation helped establish the concept of isomers, molecules with identical atomic compositions but different arrangements. Yet pinning down the actual structure of fulminic acid proved maddeningly difficult. For over a century, chemists proposed incorrect structures, many insisting it must contain two carbon atoms. Only in 1966 did infrared spectroscopy finally prove fulminic acid was a chain of four atoms: H-C-N-O.

But solving the connectivity was just the beginning. The real puzzle lay in the molecule's shape.

The Bent or Straight Dilemma

Here's where things get strange. Is fulminic acid straight like a stick, or does it bend at the hydrogen-carbon-nitrogen angle? This might sound like an arcane detail, but it matters enormously for understanding the molecule's properties and behavior.

Early quantum mechanical calculations gave contradictory answers. Some methods predicted the molecule was perfectly straight. Others insisted it bent at an angle. Even more confusing, the answer seemed to change depending on subtle details of how the calculation was performed.

This wasn't just a computational quirk. Fulminic acid belongs to a rare class called "quasibent" molecules. These species have vibrationless structures that are perfectly linear, but their lowest energy bending vibration is so weak that quantum mechanical effects make them appear bent in certain experiments. The molecule sits on an extraordinarily flat potential energy surface, meaning tiny computational errors can push predictions in either direction.

In 1994, researchers at Cambridge University applied newly developed density functional theory methods to the problem. They expressed optimism that these modern computational techniques would finally settle the debate, writing that "modern DFT has a role to play even in the study of the most delicate properties of small molecules."

Thirty years later, scientists decided to test whether that optimism was justified.

Testing the Computational Zoo

The research team assembled a staggering collection of 473 different computational chemistry methods, representing virtually every flavor of density functional theory currently in use. These methods are often arranged in a conceptual "ladder" where higher rungs supposedly give more accurate results.

They tested each method on fulminic acid, examining bond lengths, vibration frequencies, the critical bending angle, and the energy required to break the molecule apart. They used an extremely large basis set to eliminate any concerns about mathematical approximations, and employed the finest computational grid settings available.

For comparison, they had benchmark results from the most rigorous quantum mechanical calculations possible, pushed to the absolute limits of what current computers can achieve. These benchmark calculations required methods with intimidating names like "all-electron CCSDTQ(P)" and cost vastly more computing time than any of the 473 density functional methods being tested.

What they found was deeply troubling for anyone who uses these computational tools.

No Consensus, No Convergence

The results were scattered all over the map. Different methods disagreed wildly about whether fulminic acid was bent or straight, and by how much. They couldn't agree on bond lengths, vibration frequencies, or binding energies.

Methods at the bottom of the computational ladder got some things wrong but other things surprisingly right. Higher ranked methods sometimes performed worse. The very top of the ladder circled back to producing mostly wrong answers for the molecular structure.

For the critical bending frequency, which should be around 19 inverse centimeters based on rigorous calculations, not a single group of methods got close to the right answer. Most predicted values between 250 and 350 inverse centimeters, more than ten times too large. This catastrophic failure means the methods completely misrepresent the flat bending potential that makes fulminic acid so interesting.

The situation was like asking 473 witnesses to describe the same car accident and getting 473 completely different stories, with no way to know which accounts to trust.

When the Best Methods Disagree

Perhaps most disturbing was the lack of internal consistency. The research team identified the single best performing method for each property they examined: bond distances, vibration frequencies, reaction energies. Then they checked how well each "winner" performed on all the other properties.

Not one method excelled across the board. The method that best predicted the nitrogen-oxygen bond distance gave terrible results for other bond lengths. The method that got the carbon-nitrogen stretching frequency right badly underestimated the carbon-hydrogen stretch. Methods that correctly predicted a linear structure often got the energetics wrong.

A computational chemist trying to study fulminic acid faces an impossible choice: which method to trust? The answer depends entirely on which property you care about most, and there's no way to know in advance which method will work best for a new molecule you've never studied before.

The Dispersion Correction Problem

Modern computational chemistry often adds "dispersion corrections" to account for weak attractive forces between molecules. These corrections are crucial for studying systems where molecules interact at long range, like proteins folding or drugs binding to targets.

But fulminic acid is a single small molecule with strong chemical bonds. Dispersion forces shouldn't matter much. The researchers found that adding these corrections often changed their predictions significantly anyway, sometimes in ways that worsened accuracy.

Bond lengths shifted by amounts that seem small in absolute terms but are large enough to matter for precision work. Bond angles changed by more than a degree. Most dramatically, the computed energy to break the molecule apart shifted by several kilocalories per mole in many cases, with one method showing a shift of more than six kilocalories.

These corrections are supposed to be carefully designed to affect only long range interactions without disturbing the covalent bonds that hold molecules together. The fulminic acid results suggest this ideal isn't being achieved, at least not for this challenging case.

What This Means for Chemistry

Fulminic acid might seem like an obscure edge case. Who cares about getting a tiny molecule exactly right when computational chemistry successfully guides drug discovery, materials design, and catalysis research?

But that's precisely the point. If 473 methods can't converge on consistent answers for four atoms, what confidence can we have in predictions for systems with hundreds or thousands of atoms?

The fulminic acid challenge reveals that the computational ladder isn't really a ladder at all, at least not one that reliably leads upward. Methods don't systematically improve as you climb the rungs. Higher level theories sometimes perform worse than simpler approaches. There's no convergence toward a single correct answer.

This doesn't mean computational chemistry is useless. Far from it. These methods provide tremendous value in countless applications. A method that gets fulminic acid's bending frequency wrong by 200 wavenumbers might still correctly predict which of two drug candidates binds more tightly to its target, and that relative prediction is often all chemists need.

But it does mean we need humility about the limitations. Computational chemistry is not like going to increasingly powerful telescopes to see deeper into space, where better instruments reliably reveal more detail. It's more like trying to predict weather, where multiple models give different forecasts and uncertainty is irreducible.

The Path Forward

The University of Georgia researchers didn't just catalog failures. They also provided a valuable resource: detailed performance data for 473 methods on a chemically important test case. This gives method developers concrete targets for improvement and helps practicing chemists make more informed choices about which tools to use.

Interestingly, some older, simpler methods performed remarkably well on certain properties. The local density approximation, one of the most basic approaches, gave the best predictions for some bond distances and frequency shifts. This challenges the assumption that newer is always better.

For the specific case of fulminic acid, the researchers could now definitively answer the bent versus straight question using their benchmark quantum mechanical calculations. The molecule is linear at equilibrium, with an extraordinarily small bending frequency of just 19 inverse centimeters. This makes it truly quasibent, a fascinating case where quantum effects blur the classical distinction between bent and straight.

But reaching that definitive answer required calculations that took orders of magnitude more computer time than any of the 473 density functional methods tested. For larger molecules, such rigorous approaches quickly become impossible.

Lessons from a Tiny Molecule

The fulminic acid saga teaches us something important about scientific modeling. Mathematics and computers are powerful tools, but they don't eliminate the need for judgment, validation, and healthy skepticism.

When computational predictions matter, whether designing a new battery material or screening drug candidates, we can't simply trust whatever comes out of the software. We need experimental validation. We need to test multiple methods and understand when they agree or disagree. We need benchmark systems like fulminic acid where we can rigorously assess performance.

The 1994 optimism that better density functionals would eventually represent all correlation effects correctly has not been borne out. Thirty years of intensive development produced a zoo of 473 methods, each with its own strengths and blind spots, but no clear path toward systematic improvability.

This is humbling but also liberating. It frees us from the false comfort of believing our calculations are more reliable than they actually are. It pushes us to be more creative in how we use and interpret these tools. And it reminds us that even in an age of powerful computers, understanding nature's behavior remains delightfully difficult.

A molecule of just four atoms, discovered more than two centuries ago, still has lessons to teach us. Sometimes the smallest systems pose the biggest challenges, and that's exactly what makes science endlessly fascinating.

Publication Details

Published: 2025 (Online)
Journal: Journal of the American Chemical Society
Publisher: American Chemical Society
DOI: https://doi.org/10.1021/jacs.4c13823

Credit and Disclaimer

This article is based on original research published in the Journal of the American Chemical Society. The content has been adapted for a broader audience while maintaining scientific accuracy. For complete details, comprehensive data, full methodology, and in depth analysis, readers are strongly encouraged to access the original peer reviewed research article through the DOI link provided above. All factual information, data interpretations, and scientific conclusions presented here are derived from the original publication, and full credit goes to the research team and their contributing institutions at the University of Georgia's Center for Computational Quantum Chemistry.