This study investigates the competition between mononuclear and binuclear copper sites in different zeolite topologies, using computational models to predict Cu speciation and nuclearity. The research focuses on CHA, MOR, BEA, AFX, and FER zeolites, employing interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to analyze the vast configurational and compositional space. The results show that both topological features and Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites in these materials compared to other zeolites like CHA and BEA. A machine learning classification model was used to determine the preference for forming mononuclear or binuclear Cu sites in 200 zeolites in the international zeolite database (IZDB). The results highlight several zeolite topologies at the extreme ends of the mononuclear vs binuclear spectrum, suggesting synthetic options for realizing zeolites with strong Cu nuclearity preferences. The study also explores how Cu speciation depends on exposure conditions, Al distribution, and zeolite topology. It shows that the nuclearity of Cu sites is sensitive to temperature and the availability of specific 2Al configurations, with the multiplicity of these configurations being zeolite topology-dependent. The results rationalize experimentally observed differences in PMO performance between different zeolites and experimentally quantify variations in Cu dimer populations as a function of temperature and topology. The study further demonstrates that a machine learning-based classification model using geometry and void space descriptors can discriminate the propensity to form mononuclear or binuclear Cu sites at specific 2Al configurations in zeolite topologies across the IZDB. The research also examines the effects of temperature on Cu speciation, showing that at high temperatures and dry conditions, Z₂Cu₂O accounts for >90% of all dimers across a wide range of compositions in both CHA and MOR zeolites. The study further explores the impact of nonrandom Al distributions on Cu speciation, showing that certain zeolite types, such as CHA synthesized with specific protocols, exhibit a pseudorandom aluminum distribution. The results indicate that the abundance of Al in T3 and T4 sites and the absence of 2NN 2Al configurations in the biased Al distribution contribute to a higher dimer population compared to a random Al distribution. The study also investigates the geometric features of Z₂Cu₂O dimers, showing that Cu–O–Cu angles and Cu–Cu distances are important parameters for PMO activity. The results show that the Cu–O–Cu angles in MOR show a bimodal distribution centered at 120° and 140°, consistent with previous studies. The study further explores the correlation between Cu dimer fractions and methanol yields, showing that the methanol produced in cyclic PMO over differentThis study investigates the competition between mononuclear and binuclear copper sites in different zeolite topologies, using computational models to predict Cu speciation and nuclearity. The research focuses on CHA, MOR, BEA, AFX, and FER zeolites, employing interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to analyze the vast configurational and compositional space. The results show that both topological features and Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites in these materials compared to other zeolites like CHA and BEA. A machine learning classification model was used to determine the preference for forming mononuclear or binuclear Cu sites in 200 zeolites in the international zeolite database (IZDB). The results highlight several zeolite topologies at the extreme ends of the mononuclear vs binuclear spectrum, suggesting synthetic options for realizing zeolites with strong Cu nuclearity preferences. The study also explores how Cu speciation depends on exposure conditions, Al distribution, and zeolite topology. It shows that the nuclearity of Cu sites is sensitive to temperature and the availability of specific 2Al configurations, with the multiplicity of these configurations being zeolite topology-dependent. The results rationalize experimentally observed differences in PMO performance between different zeolites and experimentally quantify variations in Cu dimer populations as a function of temperature and topology. The study further demonstrates that a machine learning-based classification model using geometry and void space descriptors can discriminate the propensity to form mononuclear or binuclear Cu sites at specific 2Al configurations in zeolite topologies across the IZDB. The research also examines the effects of temperature on Cu speciation, showing that at high temperatures and dry conditions, Z₂Cu₂O accounts for >90% of all dimers across a wide range of compositions in both CHA and MOR zeolites. The study further explores the impact of nonrandom Al distributions on Cu speciation, showing that certain zeolite types, such as CHA synthesized with specific protocols, exhibit a pseudorandom aluminum distribution. The results indicate that the abundance of Al in T3 and T4 sites and the absence of 2NN 2Al configurations in the biased Al distribution contribute to a higher dimer population compared to a random Al distribution. The study also investigates the geometric features of Z₂Cu₂O dimers, showing that Cu–O–Cu angles and Cu–Cu distances are important parameters for PMO activity. The results show that the Cu–O–Cu angles in MOR show a bimodal distribution centered at 120° and 140°, consistent with previous studies. The study further explores the correlation between Cu dimer fractions and methanol yields, showing that the methanol produced in cyclic PMO over different