Microfluidic Chip-Assisted Upconversion Luminescence Biosensing Platform for Point-of-Care Virus Diagnostics.

Epidemics caused by multiple viruses continue to emerge, which have brought a terrible impact on human society. Identification of viral infections with high sensitivity and portability is of significant importance for the screening and management of diseases caused by viruses. Herein, a microfluidic chip (MFC)-assisted upconversion luminescence biosensing platform is designed and fabricated for point-of-care virus detection. Upconversion nanoparticles with excellent stability are successfully synthesized as luminescent agents for optical signal generation in the portable virus diagnostic platform. The relevant investigation results illustrate that the MFC-assisted virus diagnostic platform possesses outstanding performance such as good integration, high sensitivity (1.12 pg mL-1 ), ease of use, and portability. In addition, clinical sample test result verifies its more prominent virus diagnostic properties than commercially available rapid test strips. All of these thrilling capabilities imply that the designed portable virus diagnostic platform has great potential for future virus detection applications.

Kylin 1.0: An ab-initio density matrix renormalization group quantum chemistry program.

The accurate evaluation of electron correlations is highly necessary for the proper descriptions of the electronic structures in strongly correlated molecules, ranging from bond-dissociating molecules, polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, in this paper, a new ab-initio quantum chemistry program Kylin 1.0 for electron correlation calculations at various quantum many-body levels, including configuration interaction (CI), perturbation theory (PT), and density matrix renormalization group (DMRG), is presented. Furthermore, fundamental quantum chemistry methods such as Hartree-Fock self-consistent field (HF-SCF) and the complete active space SCF (CASSCF) are also implemented. The Kylin 1.0 program possesses the following features: (1) a matrix product operator (MPO) formulation-based efficient DMRG implementation for describing static electron correlation within a large active space composed of more than 100 orbitals, supporting both U 1 n × U 1 S z and U 1 n × SU 2 S symmetries; (2) an efficient second-order DMRG-self-consistent field (SCF) implementation; (3) an externally contracted multi-reference CI (MRCI) and Epstein-Nesbet PT with DMRG reference wave functions for including the remaining dynamic electron correlation outside the large active spaces. In this paper, we introduce the capabilities and numerical benchmark examples of the Kylin 1.0 program.

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

Rapid and ultrasensitive detection of SARS-CoV-2 spike protein based on upconversion luminescence biosensor for COVID-19 point-of-care diagnostics.

Here, we firstly introduce a detection system consisting of upconversion nanoparticles (UCNPs) and Au nanorods (AuNRs) for an ultrasensitive, rapid, quantitative and on-site detection of SARS-CoV-2 spike (S) protein based on Förster resonance energy transfer (FRET) effect. Briefly, the UCNPs capture the S protein of lysed SARS-CoV-2 in the swabs and subsequently they are bound with the anti-S antibodies modified AuNRs, resulting in significant nonradiative transitions from UCNPs (donors) to AuNRs (acceptors) at 480 nm and 800 nm, respectively. Notably, the specific recognition and quantitation of S protein can be realized in minutes at 800 nm because of the low autofluorescence and high Yb-Tm energy transfer in upconversion process. Inspiringly, the limit of detection (LOD) of the S protein can reach down to 1.06 fg mL-1, while the recognition of nucleocapsid protein is also comparable with a commercial test kit in a shorter time (only 5 min). The established strategy is technically superior to those reported point-of-care biosensors in terms of detection time, cost, and sensitivity, which paves a new avenue for future on-site rapid viral screening and point-of-care diagnostics.

Advances in upconversion luminescence nanomaterial-based biosensor for virus diagnosis.

Various infectious viruses have been posing a major threat to global public health, especially SARS-CoV-2, which has already claimed more than six million lives up to now. Tremendous efforts have been made to develop effective techniques for rapid and reliable pathogen detection. The unique characteristics of upconversion nanoparticles (UCNPs) pose numerous advantages when employed in biosensors, and they are a promising candidate for virus detection. Herein, this Review will discuss the recent advancement in the UCNP-based biosensors for virus and biomarkers detection. We summarize four basic principles that guide the design of UCNP-based biosensors, which are utilized with luminescent or electric responses as output signals. These strategies under fundamental mechanisms facilitate the enhancement of the sensitivity of UCNP-based biosensors. Moreover, a detailed discussion and benefits of applying UCNP in various virus bioassays will be presented. We will also address some obstacles in these detection techniques and suggest routes for progress in the field. These progressions will undoubtedly pose UCNP-based biosensors in a prominent position for providing a convenient, alternative approach to virus detection.

Optimizing two-electron repulsion integral calculations with McMurchie-Davidson method on graphic processing unit.

In this article, several optimization methods of two-electron repulsion integral calculations on a graphic processing unit (GPU) are presented. These methods are based on the investigations of the method presented by McMurchie and Davidson (MD). A new Boys function evaluation method for the GPU calculation is introduced. The series summation, the error function, and the finite sum formula method are combined; thus, good performance on the GPU can be achieved. By taking some theoretical study of the McMurchie-Davidson recurrence relations, three major optimization approaches are derived from the deduction of the general term formula for the Hermite expansion coefficient. The three approaches contain a new form of the Hermite expansion coefficients with corresponding recurrence relations, which is more efficient for one-electron integrals and [ss|∗∗] or [∗∗|ss] type two-electron integrals. In addition, a simple yet efficient new recurrence formula for the coefficient evaluation is derived, which is more efficient both in float operations and memory operations than its original one. In average, the new recurrence relation can save 26% float operations and 37% memory operations. Finally, a common sub-expression elimination (CSE) method is implemented. This CSE method is directly generated from some equalities we discovered from the general term formula other than by computer algebra system software. This optimized method achieved up to 3.09 speedups compared to the original MD method on the GPU and up to 92.75 speedups compared to the GAMESS calculation on the central processing unit.

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics.

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz. Herein, the cheminformatics is used to recognize the topological structure of molecules, the ML models are used to build the relationships between topology and computational cost, and the MWI framework is used to hold various combinations of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results of molecules from the DrugBank dataset show that (1) it can give quantitative predictions of computational costs, typical mean relative errors can be less than 0.2 for DFT/TDDFT calculations with derivations of ±25% using the exactly pretrained ML models and (2) it can also be employed to various combinations of DFT functional and basis set cases without exactly pretrained ML models, while only slightly enlarge predicting errors.

Portably parallel construction of a configuration-interaction wave function from a matrix-product state using the Charm++ framework.

The construction of configuration-interaction (CI) expansions from a matrix product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations in a large Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS employing the parallel object-oriented Charm++ programming framework, upon which automatic load-balancing and object migrating facilities can be employed. This procedure was employed in the MPS-to-CI utility (Moritz et al., J. Chem. Phys. 2007, 126, 224109), the sampling-reconstructed complete active-space algorithm (SR-CAS, Boguslawski et al., J. Chem. Phys. 2011, 134, 224101), and the entanglement-driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13, 4699). It enhances productivity and allows the sampling programs to evolve to their population-expansion versions, for example, EDGA with population expansion (PE-EDGA). Further, examples of 1,2-dioxetanone and firefly dioxetanone anion (FDO- ) molecules demonstrated the following: (a) parallel efficiencies can be persistently improved by simply by increasing the proportions of the asynchronous executions and (b) a sampled CAS-type CI wave function of a bi-radical-state FDO- molecule utilizing the full valence (30e,26o) active space can be constructed within a few hours with using thousands of cores.

Variational Multistate Density Functional Theory for a Balanced Treatment of Static and Dynamic Correlations.

Here, an approach to variational multistate density functional theory (vMSDFT) is explored. In this approach, the Kohn-Sham orbitals as well as configuration coefficients were simultaneously optimized, thus yielding a full variational minimum. Furthermore, this work also proposes two important improvements on the MSDFT framework. First, a "point-to-point correction" is used to correct the static correlation present in the DFT framework. Therefore, double counting of static correlation in vMSDFT is mitigated. Second, a general form to construct the transition density functional in the vMSDFT framework is proposed, which allows for the properties of vMSDFT wave functions to be standardized to the complete active space self-consistent field properties. The utility of vMSDFT is illustrated on molecular systems of interest including bond breaking, diradicals, excited states, and conical intersections. The numerical results suggest that the accuracy of vMSDFT is in close agreement with the high-level multireference methods.

Elucidating the multi-configurational character of the firefly dioxetanone anion and its prototypes in the biradical region using full valence active spaces.

We analyzed the near-degenerate states of the firefly dioxetanone anion (FDO-) and its prototypes, especially in the biradical region, using multi-configurational approaches. The importance of utilizing full valence active spaces by means of density-matrix renormalization group self-consistent field (DMRG-SCF) calculations was described. Our results revealed that the neglect of some valence orbitals can affect the quantitative accuracy in later multi-reference calculations or the qualitative conclusion when optimizing conical intersections. Using all of the relevant valence orbitals of FDO-, we confirmed that there were two conical intersections, as reported in previous work, and that the intersecting states were changed when the active space was enlarged. Beyond these, we found that there were strong interactions between states in the biradical regions, in which the changes in entanglements can be used to visualize the interacting state evolution.

Approximate Analytical Gradients and Nonadiabatic Couplings for the State-Average Density Matrix Renormalization Group Self-Consistent-Field Method.

We present an approximate scheme for analytical gradients and nonadiabatic couplings for calculating state-average density matrix renormalization group self-consistent-field wave function. Our formalism follows closely the state-average complete active space self-consistent-field (SA-CASSCF) ansatz, which employs a Lagrangian, and the corresponding Lagrange multipliers are obtained from a solution of the coupled-perturbed CASSCF (CP-CASSCF) equations. We introduce a definition of the matrix product state (MPS) Lagrange multipliers based on a single-site tensor in a mixed-canonical form of the MPS, such that a sweep procedure is avoided in the solution of the CP-CASSCF equations. We apply our implementation to the optimization of a conical intersection in 1,2-dioxetanone, where we are able to fully reproduce the SA-CASSCF result up to arbitrary accuracy.

OpenMolcas: From Source Code to Insight.

In this Article we describe the OpenMolcas environment and invite the computational chemistry community to collaborate. The open-source project already includes a large number of new developments realized during the transition from the commercial MOLCAS product to the open-source platform. The paper initially describes the technical details of the new software development platform. This is followed by brief presentations of many new methods, implementations, and features of the OpenMolcas program suite. These developments include novel wave function methods such as stochastic complete active space self-consistent field, density matrix renormalization group (DMRG) methods, and hybrid multiconfigurational wave function and density functional theory models. Some of these implementations include an array of additional options and functionalities. The paper proceeds and describes developments related to explorations of potential energy surfaces. Here we present methods for the optimization of conical intersections, the simulation of adiabatic and nonadiabatic molecular dynamics, and interfaces to tools for semiclassical and quantum mechanical nuclear dynamics. Furthermore, the Article describes features unique to simulations of spectroscopic and magnetic phenomena such as the exact semiclassical description of the interaction between light and matter, various X-ray processes, magnetic circular dichroism, and properties. Finally, the paper describes a number of built-in and add-on features to support the OpenMolcas platform with postcalculation analysis and visualization, a multiscale simulation option using frozen-density embedding theory, and new electronic and muonic basis sets.

Externally-Contracted Multireference Configuration Interaction Method Using a DMRG Reference Wave Function.

The recent development of the density matrix renormalization group (DMRG) method in multireference quantum chemistry makes it practical to evaluate static correlation in a large active space, while dynamic correlation provides a critical correction to the DMRG reference for strong-correlated systems and is usually obtained using multireference perturbation (MRPT) or configuration interaction (MRCI) methods with internal contraction (ic) approximation. These methods can use an active space scalable to relatively larger size references than has previously been possible. However, they are still hardly applicable to systems with an active space larger than 30 orbitals and/or a large basis set because of high computation and storage costs of high-order reduced density matrices (RDMs) and the crucial dependence of the MRCI Hamiltonian dimension on the number of virtual orbitals. In this work, we propose a new effective implementation of DMRG-MRCI, in which we use reconstructed CASCI-type configurations from DMRG wave function via the entropy-driving genetic algorithm (EDGA) [ Luo et al. J. Chem. Theory Comput. 2017 , 13 , 4699 - 4710 . ] and integrate it with MRCI by an external contraction (ec) scheme. This bypasses the bottleneck of computing high-order RDMs in traditional DMRG dynamic correlation methods with ic approximation, and the number of MRCI configurations is not dependent on the number of virtual orbitals. Therefore, the DMRG-ec-MRCI method is promising for dealing with a larger active space than 30 orbitals and large basis sets. We demonstrate the capability of our DMRG-ec-MRCI method in several benchmark applications, including the evaluation of the potential energy curve of Cr2, single-triplet gaps of higher n-acene molecules, and the energy of the Eu-BTBP(NO3)3 complex.

Efficient Reconstruction of CAS-CI-Type Wave Functions for a DMRG State Using Quantum Information Theory and a Genetic Algorithm.

We improve the methodology to construct a complete active space-configuration interaction (CAS-CI) expansion for density-matrix renormalization group (DMRG) wave functions using a matrix-product state representation, inspired by the sampling-reconstructed CAS [SR-CAS; Boguslawski , K. ; J. Chem. Phys. 2011 , 134 , 224101 ] algorithm. In our scheme, the genetic algorithm, in which the "crossover" and "mutation" processes can be optimized based on quantum information theory, is employed when reconstructing a CAS-CI-type wave function in the Hilbert space. Analysis of results for ground and excited state wave functions of conjugated molecules, transition metal compounds, and a lanthanide complex illustrate that our scheme is very efficient for searching the most important CI expansions in large active spaces.

Second-Order Self-Consistent-Field Density-Matrix Renormalization Group.

We present a matrix-product state (MPS)-based quadratically convergent density-matrix renormalization group self-consistent-field (DMRG-SCF) approach. Following a proposal by Werner and Knowles (J. Chem. Phys. 1985, 82, 5053), our DMRG-SCF algorithm is based on a direct minimization of an energy expression which is correct to second order with respect to changes in the molecular orbital basis. We exploit a simultaneous optimization of the MPS wave function and molecular orbitals in order to achieve quadratic convergence. In contrast to previously reported (augmented Hessian) Newton-Raphson and superconfiguration-interaction algorithms for DMRG-SCF, energy convergence beyond a quadratic scaling is possible in our ansatz. Discarding the set of redundant active-active orbital rotations, the DMRG-SCF energy converges typically within two to four cycles of the self-consistent procedure.

Multiconfigurational Effects in Theoretical Resonance Raman Spectra.

We analyze resonance Raman spectra of the nucleobase uracil in the short-time approximation calculated with multiconfigurational methods. We discuss the importance of static electron correlation by means of density-matrix renormalization group self-consistent field (DMRG-SCF) calculations. Our DMRG-SCF results reveal that a minimal active orbital space that leads to a qualitatively correct description of the resonance Raman spectrum of uracil should encompass parts of the σ/σ* bonding/anti-bonding orbitals of the pyrimidine ring. We trace these findings back to the considerable entanglement between the σ/σ* bonding/anti-bonding as well as valence π/π* orbitals in the excited-state electronic structure of uracil, which indicates non-negligible non-dynamical correlation effects that are less pronounced in the electronic ground state.

New Approaches for ab initio Calculations of Molecules with Strong Electron Correlation.

Reliable quantum chemical methods for the description of molecules with dense-lying frontier orbitals are needed in the context of many chemical compounds and reactions. Here, we review developments that led to our new computational toolbox which implements the quantum chemical density matrix renormalization group in a second-generation algorithm. We present an overview of the different components of this toolbox.

Density-matrix renormalization group algorithm with multi-level active space.

The density-matrix renormalization group (DMRG) method, which can deal with a large active space composed of tens of orbitals, is nowadays widely used as an efficient addition to traditional complete active space (CAS)-based approaches. In this paper, we present the DMRG algorithm with a multi-level (ML) control of the active space based on chemical intuition-based hierarchical orbital ordering, which is called as ML-DMRG with its self-consistent field (SCF) variant ML-DMRG-SCF. Ground and excited state calculations of H2O, N2, indole, and Cr2 with comparisons to DMRG references using fixed number of kept states (M) illustrate that ML-type DMRG calculations can obtain noticeable efficiency gains. It is also shown that the orbital re-ordering based on hierarchical multiple active subspaces may be beneficial for reducing computational time for not only ML-DMRG calculations but also DMRG ones with fixed M values.