التفاصيل البيبلوغرافية
| العنوان: |
Machine Learning K‑Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves |
| المؤلفون: |
Zhenlin Zhang, Xilin Yin, Wei Hu, Jinlong Yang |
| سنة النشر: |
2024 |
| المجموعة: |
Smithsonian Institution: Figshare |
| مصطلحات موضوعية: |
Biophysics, Medicine, Physiology, Biotechnology, Computational Biology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, interpolation point selection, integrated gpu toolkit, exchange plus random, exceptionally high prefactor, equilibrium polyynic geometry, capturing physical information, 100 × compared, quasiquadratic scaling alternative, computationally demanding cubic, computational challenges associated, 18 , overall computational scaling, 0 , scaling qrcp algorithm, around 1 h, 2 , conventional isdf algorithm, means algorithm reduces, computational scaling, means algorithm, qrcp completes |
| الوصف: |
The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called “SSM+” for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method’s capabilities by optimizing GPU acceleration using MATLAB’s integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ 0 from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si 128 , while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H 2 and ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| العلاقة: |
https://figshare.com/articles/journal_contribution/Machine_Learning_K_Means_Clustering_of_Interpolative_Separable_Density_Fitting_Algorithm_for_Accurate_and_Efficient_Cubic-Scaling_Exact_Exchange_Plus_Random_Phase_Approximation_within_Plane_Waves/25231721Test |
| DOI: |
10.1021/acs.jctc.3c01157.s001 |
| الإتاحة: |
https://doi.org/10.1021/acs.jctc.3c01157.s001Test |
| حقوق: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.A8E74650 |
| قاعدة البيانات: |
BASE |
