Liu Shubin
Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, United States.
Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290, United States.
ACS Phys Chem Au. 2024 Jan 19;4(2):135-142. doi: 10.1021/acsphyschemau.3c00067. eCollection 2024 Mar 27.
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 simulations in the next few decades.
有人认为，没有人能够确切地预测未来，但人们可以从过去吸取经验教训，并对未来数年做出有依据的预测。在这篇观点文章中，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，理论和计算化学中ML和QC方法的发展与成熟代表了求解薛定谔方程方式的两次范式转变。通过分别利用ML特征和QC量子比特，可以在这两种新范式中获得新的化学理解。然而，在此之前，我们在ML和QC领域仍面临障碍需要面对和克服。我们提出了应对这些挑战的可能途径。我们预计，与多尺度建模不同，层次建模将出现并蓬勃发展，在未来几十年成为模拟的主力军。
