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Journal of Computer Chemistry Japan

Next Generation Quantum Chemistry for Large Bio-Molecules

Hideo Sekino

Published Jan 1, 2007• 0 citations
Publisher

Abstract

巨大生体高分子への量子論の適用は新しい多体論を必要とするかもしれない。現存の量子化学的手法のそのままの形での拡張適用は理論的にも技術的にも破綻する可能性がある。生命科学に必要な精度を保障するにはシミュレーションにおける空間表現の問題が重要となる。次世代量子化学では半世紀以上にわたって使われているガウシアン基底を越える表現法が必要であろう。多重解像度多重ウェーブレット基底による分極率のような分子物性予測により、新しい効率の良い空間表現の試みを示した。また現在大規模量子化学計算の主流である密度汎関数法での物性算定の問題にも空間表現が関連している。

DOI
10.2477/jccj.6.167

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巨大生体高分子への量子論の適用は新しい多体論を必要とするかもしれない。現存の量子化学的手法のそのままの形での拡張適用は理論的にも技術的にも破綻する可能性がある。生命科学に必要な精度を保障するにはシミュレーションにおける空間表現の問題が重要となる。次世代量子化学では半世紀以上にわたって使われているガウシアン基底を越える表現法が必要であろう。多重解像度多重ウェーブレット基底による分極率のような分子物性予測により、新しい効率の良い空間表現の試みを示した。また現在大規模量子化学計算の主流である密度汎関数法での物性算定の問題にも空間表現が関連している。
量研では，新しい学術領域｢量子生命科学｣の確立を目指した活動を，国内外の研究者と共に精力的に進めている。量子生命科学とは，最先端の量子技術と量子科学の知見を総合的に利用し，従来不可能であった極微の空間・時間スケールあるいは超高感度での生体内部の観測や生体分子の計測，生命機能のモデリングなどを実現することにより，量子論・量子力学を基盤とした視点から生命全般の根本原理を明らかにすることを目標とし，その原理の産業応用も視野に入れた分野である。本稿では，平成31年4月に量研内に発足した｢量子生命科学領域｣における研究のアプローチを紹介する。
We demonstrate the use of dataflow technology in the computation of the correlation energy in molecules at the Møller-Plesset perturbation theory (MP2) level. Specifically, we benchmark density fitting (DF)-MP2 for as many as 168 atoms (in valinomycin) and show that speed-ups between 3 and 3.8 times can be achieved when compared to the MOLPRO package run on a single CPU. Acceleration is achieved by offloading the matrix multiplications steps in DF-MP2 to Dataflow Engines (DFEs). We project that the acceleration factor could be as much as 24 with the next generation of DFEs.
Abstract Quantum mechanical and classical aspects are of equal importance in chemistry, and theoretical chemistry must be able to deal with this situation. Classical quantities with which a chemist has to deal include, for example, the temperature and the chemical potential of a substance, and chirality, knot‐type and tertiary structures of molecules. Classical concepts of this type are no more consequences of traditional quantum chemistry than is the theory of complex chemical processes (for example adsorption, heterogeneous catalysis) and macroscopic systems. Algebraic quantum mechanics affords a general framework for the discussion of classical quantities and large systems. There are other independent approaches for the theoretical treatment of chemical problems; these can be summarized by using the keywords “fractals”, “chaotic motion”, “quasicrystals” and “knot theory”. The relationships between these four research topics and their connection with algebraic quantum mechanics, as well as their importance for theoretical chemistry, were discussed at a NATO Workshop in 1987 (see Ref. [1]). In an extended theoretical chemistry, the notion of “molecules” will not play a role as central as it does in quantum chemistry. In the theory of large systems in particular, the introduction of complementary approaches appears to be appropriate; the term “molecule” as used by the chemist does not appear either in the description of disordered systems by means of fractals or in the thermodynamic limit of algebraic statistical mechanics.
We review the current capabilities of local electron correlation methods up to the coupled cluster model with single, double, and perturbative triple excitations [CCSD(T)], which is a gold standard in quantum chemistry. The main computational aspects of the local method types are assessed from the perspective of applications, but the focus is kept on how to achieve chemical accuracy (i.e., <1 kcal/mol uncertainty), as well as on the broad scope of chemical problems made accessible. The performance of state-of-the-art methods are also compared, including the most employed DLPNO and, in particular, our local natural orbital (LNO) CCSD(T) approach. The high accuracy and efficiency of the LNO method makes chemically accurate CCSD(T) computations accessible for molecules of hundreds of atoms with resources affordable to a broad computational community (days on a single CPU and 10–100 GB of memory). Recent developments in LNO-CCSD(T) enable systematic convergence and robust error estimates even for systems of complicated electronic structure or larger size (up to 1000 atoms). The predictive power of current local CCSD(T) methods, usually at about 1–2 order of magnitude higher cost than hybrid density functional theory (DFT), has become outstanding on the palette of computational chemistry applicable for molecules of practical interest. We also review more than 50 LNO-based and other advanced local-CCSD(T) applications for realistic, large systems across molecular interactions as well as main group, transition metal, bio-, and surface chemistry. The examples show that properly executed local-CCSD(T) can contribute to binding, reaction equilibrium, rate constants, etc. which are able to match measurements within the error estimates. These applications demonstrate that modern, open-access, and broadly affordable local methods, such as LNO-CCSD(T), already enable predictive computations and atomistic insight for complicated, real-life molecular processes in realistic environments.