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引用次数: 0
摘要
可以说,没有人能够准确预测未来,但我们可以从过去的经验中汲取教训,对未来做出明智的预测。在本《视角》中,我们概述了如何利用理论和计算从波函数理论和密度泛函理论中获得化学理解的现状,然后展望了机器学习(ML)和量子计算机(QC)在未来几十年对理解传统化学概念可能产生的影响。我们认为,ML 和 QC 方法在理论化学和计算化学领域的发展和成熟,代表了关于如何求解薛定谔方程的两种范式转变。通过分别利用 ML 特征和 QC 量子,可以在这两种新范式中获得新的化学认识。不过,在此之前,我们在 ML 和 QC 领域仍有许多障碍需要克服。我们提出了应对这些挑战的可能途径。我们预计,与多尺度建模相比,分层建模将会出现并蓬勃发展,在未来几十年成为硅学模拟的主力军。
Harvesting Chemical Understanding with Machine Learning and Quantum Computers
It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of in silico simulations in the next few decades.
期刊介绍:
ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis