Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

Artificial neural networks (ANNs) for material modeling have received significant interest. We recently reported an adaptation of ANNs based on Boltzmann machine (BM) architectures to an ansatz of the multiconfigurational many-electron wavefunction, designated neural-network quantum state (NQS), for quantum chemistry calculations [Yang et al., J. Chem. Theory Comput., 2020, 16, 3513--3529]. Here, this study presents its extended formalism to a quantum algorithm that enables the preparation of the NQS through quantum gates. The descriptors of the ANN model, which are chosen as the occupancies of electronic configurations, are quantum-mechanically represented by qubits. Our algorithm may thus bring potential advantages over classical sampling-based computation employed in the previous studies. The NQS can be accurately formed using quantum-native procedures. Still, the training of the model in terms of energy minimization is efficiently performed on a classical computer; thus, our approach is a class of variational quantum eigensolver. The BM models are related to the Gibbs distribution, and our preparation procedures exploit techniques of quantum phase estimation but with no Hamiltonian evolution. The proposed algorithm is assessed by implementing it on a quantum computer simulator. Illustrative molecular calculations at the complete-active-space configuration interaction level of theory are displayed, confirming consistency with the accuracy of our previous classical approaches

Extended theoretical modeling of reverse intersystem crossing for thermally activated delayed fluorescence materials

Thermally activated delayed fluorescence (TADF) materials and multi-resonant (MR) variants are promising organic emitters that can achieve an internal electroluminescence quantum efficiency of approximately 100%. The reverse intersystem crossing (RISC) is key for harnessing triplet energies for fluorescence. Theoretical modeling is thus crucial to estimate its rate constant kRISC for material development. Herein, we present a comprehensive assessment of the theory for simulating the RISC of MR-TADF molecules within a perturbative excited-state dynamics framework. Our extended rate formula reveals the importance of the concerted effects of nonadiabatic spin-vibronic coupling and vibrationally induced spin-orbital couplings in reliably determining kRISC of MR-TADF molecules. The excited singlet-triplet energy gap is another factor influencing kRISC. We present a new scheme for gap estimation using experimental Arrhenius plots of kRISC. Erroneous behavior caused by approximations in Marcus theory is elucidated by testing 121 MR-TADF molecules. Our extended modeling offers in-depth descriptions of kRISC

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field