Toward Slower, Less Accurate, More Expensive, and Worse Density Functional Theory

Abstract: In this work, we present Natural Stupidity (NS) as a pioneering anti-optimization framework for Density Functional Theory (DFT) calculations. While recent trends have seen Artificial Intelligence (AI) enhance DFT accuracy and efficiency, we argue that such progress has come at the expense of interpretability, frustration, and the cherished academic tradition of unnecessary suffering. To address this, we propose Artificial Convergence Dynamics (ACD), a methodology designed to prolong runtime, increase uncertainty, and ensure that convergence—if it happens at all—occurs entirely by accident. Our approach avoids innovation in functionals and basis sets, instead leveraging randomness, ego-driven parameters, and emotional decision-making. Results appear numerically plausible but are fundamentally incorrect upon the slightest scrutiny, aligning well with historical precedent. While NS does not improve performance in any measurable way, it offers profound metaphysical insight and satisfies the rarely discussed “vibe criteria” of modern computational chemistry.