Characterizing the free energy landscape of water ionization has been a great challenge due to limitations from expensive ab initio calculations and strong rare-event features. Lacking equilibrium sampling pathway will cause ambiguities in mechanistic study. Here, we obtain convergent surfaces through nanosecond timescale metadynamics simulations with classical nuclei enhanced by atomic neural network potentials, which yields good reproduction constant (pK_{w}=14.14) rate (1.369×10^{-3}...

The accurate evaluation of electron correlations is highly necessary for the proper descriptions electronic structures in strongly correlated molecules, ranging from bond-dissociating polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, paper, a new ab-initio quantum chemistry program Kylin 1.0 correlation calculations at various many-body levels, including configuration interaction (CI), perturbation theory (PT), density matrix renormalization group...

The neural network quantum state (NNQS) method has demonstrated promising results in ab initio chemistry, achieving remarkable accuracy molecular systems. However, efficient calculation of systems with large active spaces remains challenging. This study introduces a novel approach that bridges tensor states the transformer-based NNQS-Transformer (QiankunNet) to enhance and convergence for relatively spaces. By transforming into space configuration interaction type wave functions, QiankunNet...

In this article, several optimization methods of two-electron repulsion integral calculations on a graphic processing unit (GPU) are presented. These based the investigations method presented by McMurchie and Davidson (MD). A new Boys function evaluation for GPU calculation is introduced. The series summation, error function, finite sum formula combined; thus, good performance can be achieved. By taking some theoretical study McMurchie–Davidson recurrence relations, three major approaches...

Neural network methods have shown promise for solving complex quantum many-body systems. In this study, we develop a novel approach through incorporating the density-matrix renormalization group (DMRG) method with neural state method. The results demonstrate that, when tensor-network pre-training is introduced into network, high efficiency can be achieved systems strong correlations.

With the view of achieving a better performance in task assignment and load-balancing, top-level designed forecasting system for predicting computational times density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The time assumed as intrinsic property molecule. Based on this assumption, established using "reinforced concrete", which combines cheminformatics, several machine-learning (ML) models, framework many-world interpretation (MWI) multiverse ansatz....

The mixed-precision optimization is an effective emerging technique for quantum chemistry methods to obtain better computational performance and maintain the chemical accuracy. Here, we developed a two-level implementation density matrix renormalization group (DMRG) method. This based on idea that DMRG process iterative process. Therefore, first several iteration steps can be executed in single precision. A feasible single-precision may generate moderate accuracy, when few double-precision...

a ( 西北大学 现代物理研究所 陕西省理论物理前沿重点实验室 西安 710127

Abstract: In the last decades, many algorithms have been developed to use high-performance computing (HPC) techniques accelerate density matrix renormalization group (DMRG) method, an effective method for solving large active space strong correlation problems. this article, previous DMRG parallelization at different levels of parallelism are introduced. The heterogeneous acceleration methods and mixed-precision implementation also presented discussed. This mini-review concludes with some...

In this paper, we introduced some optimization methods used to optimize two-electron repulsion integral calculation on Knights Landing architecture. We developed a schedule for parallelism and vectorization, compared two different calculating lower incomplete gamma function. Our achieved 1.7 speedup KNL than CPU platform.

A top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent (TDDFT) calculations is presented. The time assumed as the intrinsic property molecule. Basing on this assumption, established using "reinforced concrete", which combines cheminformatics, several machine-learning (ML) models, and framework many-world interpretation (MWI) in multiverse ansatz. Herein, cheminformatics used to recognize topological structure molecules,...

Using the mixed precision strategy to optimize quantum chemistry codes has been proved promising in saving computational cost and maintaining chemical accuracy. Here, an efficient mixed-precision density matrix renormalization group (DMRG) scheme, containing a two-level hierarchy, is developed demonstrated. At coarse-grained level, based on discovery that single-precision orthogonalization may cause DMRG generate totally wrong answer, feasible single-precision-sweep method with...

Water autoionization plays a critical role in determining pH and properties of various chemical biological processes occurring the water mediated environment. The strikingly unsymmetrical potential energy surface dissociation process poses great challenge to mechanistic study. Here, we demonstrate that reliable sampling ionization path is accessible through nanosecond timescale metadynamics simulation enhanced by machine learning neural network potentials with ab initio precision, which...

The accurate evaluation of electron correlations is highly necessary for the proper descriptions electronic structures in strongly correlated molecules, ranging from bond-dissociating polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, paper, a new ab-initio quantum chemistry program Kylin 1.0 correlation calculations at various many-body levels, including configuration interaction (CI), perturbation theory (PT), density matrix renormalization group...