人工智能解決了Schr?dinger分子方程

人工智能解決了Schrödinger分子方程

The Schrödinger Equation is a crucial formalism at the center of quantum mechanics. It is used to work out how quantum systems are, and how they evolve. It is also very much a challenge to solve precisely for a system made of more than a few particles, with approximations use in most cases.

Schrödinger方程是量子力學(xué)中心的一個(gè)重要形式主義。它被用來(lái)研究量子系統(tǒng)是怎樣的，以及它們是怎樣進(jìn)化的。對(duì)于一個(gè)由多個(gè)粒子組成的系統(tǒng)來(lái)說(shuō)，精確地求解也是一個(gè)很大的挑戰(zhàn)，在大多數(shù)情況下使用近似方法。

Computational methods are used to solve the equation for many systems, and a new study published in Nature Chemistry has put forward a new method. The approach, called PauliNet, is a deep neural network that can get the exact solution for the equation for molecules with up to 30 electrons.

計(jì)算方法被用于求解許多系統(tǒng)的方程，一項(xiàng)發(fā)表在《自然化學(xué)》雜志上的新研究提出了一種新方法。這種方法被稱(chēng)為PauliNet，是一種深度神經(jīng)網(wǎng)絡(luò)，它可以得到含有多達(dá)30個(gè)電子的分子方程的精確解。

This AI is based on the Monte Carlo method, which uses random sampling to deliver numerical results of a mathematical function. This particular version was built with the knowledge of physical laws, including the important Pauli exclusion principle. The algorithm is named after this law.

該人工智能基于蒙特卡羅方法，該方法使用隨機(jī)抽樣來(lái)提供數(shù)學(xué)函數(shù)的數(shù)值結(jié)果。這個(gè)特殊的版本是建立在物理定律的知識(shí)，包括重要的泡利不相容原理。該算法就是根據(jù)這個(gè)規(guī)律命名的。

Solving the equation can provide insights into the formation and behavior of molecules that several of the current methods can’t provide. This has often been too laborious to be worth it, hence why this method could be a game-changer.

解出這個(gè)方程可以讓我們對(duì)分子的形成和行為有更深入的了解，這是目前許多方法所不能提供的。這通常過(guò)于費(fèi)力，不值得，因此這一方法可以改變游戲規(guī)則。

Dr Alfredo Carpineti

"Escaping the usual trade-off between accuracy and computational cost is the highest achievement in quantum chemistry," lead author Dr. Jan Hermann of Freie Universität Berlin, said in a statement. "As yet, the most popular such outlier is the extremely cost-effective density functional theory. We believe that deep 'Quantum Monte Carlo,' the approach we are proposing, could be equally, if not more successful. It offers unprecedented accuracy at a still acceptable computational cost."

“擺脫精確度和計(jì)算成本之間的通常平衡，是量子化學(xué)領(lǐng)域的最高成就，”Freie Universität Berlin的首席作者Jan Hermann博士在一份聲明中說(shuō)。到目前為止，最流行的異常值是極具成本效益的密度泛函理論。我們相信，我們正在提出的深度“量子蒙特卡洛”方法，即使不能取得更大的成功，也可以取得同樣的效果。它提供了前所未有的準(zhǔn)確性，但計(jì)算成本仍然可以接受。”

PauliNet allows for a solution of the Schrödinger Equation to be found for arbitrary molecules. The versatility and strong physical backbone of the software deliver these results. The equation is a mathematical description of the quantum state known as the wave function, and translating this wave function for many electrons into a computer language was not easy.

PauliNet允許為任意分子找到Schrödinger方程的解。軟件的多功能性和強(qiáng)大的物理主干提供了這些結(jié)果。這個(gè)方程是量子態(tài)波函數(shù)的數(shù)學(xué)描述，把這個(gè)波函數(shù)翻譯成計(jì)算機(jī)語(yǔ)言并不容易。

"Instead of the standard approach of composing the wave function from relatively simple mathematical components, we designed an artificial neural network capable of learning the complex patterns of how electrons are located around the nuclei," added Professor Frank Noé, who led the team effort.

“我們?cè)O(shè)計(jì)了一種人工神經(jīng)網(wǎng)絡(luò)，能夠?qū)W習(xí)電子在原子核周?chē)恢玫膹?fù)雜模式，而不是用相對(duì)簡(jiǎn)單的數(shù)學(xué)成分組成波函數(shù)的標(biāo)準(zhǔn)方法，”領(lǐng)導(dǎo)該團(tuán)隊(duì)的Frank Noé教授補(bǔ)充道。

There are still many kinks to iron out, but the researchers are excited about the possibilities of this algorithm.

還有許多問(wèn)題需要解決，但研究人員對(duì)這種算法的可能性感到興奮。

"This is still fundamental research," the authors agree, "but it is a fresh approach to an age-old problem in the molecular and material sciences, and we are excited about the possibilities it opens up."

“這仍然是基礎(chǔ)研究，”作者們表示同意，“但它是分子和材料科學(xué)中一個(gè)古老問(wèn)題的新方法，我們對(duì)它所帶來(lái)的可能性感到興奮。”