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Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

Diversity increases toward the tropics, but the strength of this pattern diverges with thermoregulatory strategy. Synthesizing over 30,000 species distributions, we quantified patterns of richness in terrestrial vertebrates, and present evidence for a latitudinal gradient of community composition. We observe a two orders of magnitude shift in comparative diversity with temperature, from endothermic mammal and avian dominance near the poles, toward ectothermic reptile and amphibian majority in the tropics. Next, we provide mechanistic support for a corresponding latitudinal gradient of predatory interactions. Using automated video tracking in >4500 trials, we show that differences in thermal sensitivity of locomotion in endothermic predators and ectothermic prey favors endotherms in colder environments and yields theoretically predicted foraging outcomes across thermal conditions, including the number of strikes, the distance traveled, and the time to capture prey. We also present evidence that endotherms use thermal cues to anticipate prey behavior, modulating the impact of temperature. Finally, we integrate theory and data to forecast future patterns of diversity, revealing that as the world get warmer, it will become increasingly reptilian. Overall, our results point toward a broad reorganization of vertebrate diversity with latitude, elevation, and temperature: from endotherm dominance in cold systems toward ectotherm dominance in warm.

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the network's nearness to criticality , an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animal's genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior., Competing Interests: DECLARATION OF INTERESTS D.M.H. is on the scientific advisory board of C2N diagnostics and has equity. D.M.H. is on the scientific advisory board of Denali Therapeutics, Genentech, and Cajal Therapeutics and consults for Asteroid Therapeutics.

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

In this paper we discuss the application of novel parallel implementation of the coupled cluster (CC) and equation-of-motion coupled cluster methods (EOMCC) in calculations of excitation energies of triplet states in β-carotene. Calculated excitation energies are compared with experimental data, where available. We also provide a detailed description of the new parallel algorithms for iterative CC and EOMCC models involving singles and doubles excitations.

Enthalpies of formation for the ThX4 and UX4 (X = F, Cl, OH) species have been investigated with density functional theory and coupled-cluster methods. ThX4 molecules are all confirmed as tetrahedral, while all UX4 molecules are predicted to adopt D2d symmetry using density functional theory. Multireference coupled cluster approaches confirm the D2d symmetry for UF4. The bonding is mostly ionic, and predicted formation energies for the halogen species show good agreement with experiment. Our calculated hydration energy of UF4 (-54.0 kcal/mol) is in very good agreement with the experimental data (-54.8 kcal/mol). We predict CCSD(T) formation energies of ΔfG[U(OH)4(g)] = -286.3 kcal/mol and ΔfG[U(OH)4(aq)] = -318.7 kcal/mol. ΔfG[U(OH)4(aq)] is 21 kcal/mol less stable than the established experimental thermodynamic data.