Search for: All records

Obtaining sub-chemical accuracy (1 kJ mol−1) for reaction energies of medium-sized gas-phase molecules is a longstanding challenge in the field of thermochemical modeling. The perturbative triples correction to coupled-cluster single double triple [CCSD(T)] constitutes an important component of all high-accuracy composite model chemistries that obtain this accuracy but can be a roadblock in the calculation of medium to large systems due to its O(N7) scaling, particularly in HEAT-like model chemistries that eschew separation of core and valence correlation. This study extends the work of Lesiuk [J. Chem. Phys. 156, 064103 (2022)] with new approximate methods and assesses the accuracy of five different approximations of (T) in the context of a subset of molecules selected from the W4-17 dataset. It is demonstrated that all of these approximate methods can achieve sub-0.1 kJ mol−1 accuracy with respect to canonical, density-fitted (T) contributions with a modest number of projectors. The approximation labeled Z̃T appears to offer the best trade-off between cost and accuracy and shows significant promise in an order-of-magnitude reduction in the computational cost of the CCSD(T) component of high-accuracy model chemistries. 

Individual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people’s perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate. 

Understanding the process of molecular photoexcitation is crucial in various fields, including drug development, materials science, photovoltaics, and more. The electronic vertical excitation energy is a critical property, for example in determining the singlet–triplet gap of chromophores. However, a full understanding of excited-state processes requires additional explorations of the excited-state potential energy surface and electronic properties, which is greatly aided by the availability of analytic energy gradients. Owing to its robust high accuracy over a wide range of chemical problems, equation-of-motion coupled cluster with single and double excitations (EOM-CCSD) is a powerful method for predicting excited-state properties, and the implementation of analytic gradients of many EOM-CCSD variants (excitation energies, ionization potentials, electron attachment energies, etc.) along with numerous successful applications highlights the flexibility of the method. In specific cases where a higher level of accuracy is needed or in more complex electronic structures, the inclusion of triple excitations becomes essential, for example, in the EOM-CCSD* approach of Saeh and Stanton. In this work, we derive and implement for the first time the analytic gradients of EOMEE-CCSD*, which also provides a template for analytic gradients of related excited-state methods with perturbative triple excitations. The capabilities of analytic EOMEE-CCSD* gradients are illustrated by several representative examples. 

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized α-investing framework which enables control of the marginal false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of α-wealth which motivates a consideration of sample size in the α-investing decision rule. Posing the testing process as a game with nature, we construct a decision rule that optimizes the expected α-wealth reward (ERO) and provides an optimal sample size for each test. Empirical results show that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods for $n=1$ where n is the sample size. When the sample size is not fixed cost-aware ERO uses a prior on the null hypothesis to adaptively allocate of the sample budget to each test. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner. Finally, empirical tests on real data sets from biological experiments show that cost-aware ERO balances the allocation of samples to an individual test against the allocation of samples across multiple tests.