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Machine Learning

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

Evgeny Epifanovsky(Q Chem (United States)), Andrew T. B. Gilbert(Australian National University), Xintian Feng(University of Southern California), Joonho Lee(University of California, Berkeley), Yuezhi Mao(University of California, Berkeley), Narbe Mardirossian(California Institute of Technology), Pavel Pokhilko(University of Southern California), Alec F. White(University of California, Berkeley), Marc P. Coons(The Ohio State University), Adrian L. Dempwolff(Heidelberg University), Zhengting Gan(Q Chem (United States)), Diptarka Hait(University of California, Berkeley), Paul R. Horn(University of California, Berkeley), Leif D. Jacobson(The Ohio State University), Ilya Kaliman(University of Southern California), Jörg Kußmann(Ludwig-Maximilians-Universität München), Adrian W. Lange(The Ohio State University), Ka Un Lao(The Ohio State University), Daniel S. Levine(University of California, Berkeley), Jie Liu(University of Science and Technology of China), Simon C. McKenzie(Australian National University), Adrian F. Morrison(Q Chem (United States)), Kaushik Nanda(University of Southern California), Felix Plasser(Loughborough University), Dirk R. Rehn(Heidelberg University), Marta L. Vidal(Technical University of Denmark), Zhi-Qiang You(Q Chem (United States)), Ying Zhu(The Ohio State University), Bushra Alam(The Ohio State University), Benjamin Albrecht(University of Pittsburgh), Abdulrahman Aldossary(University of California, Berkeley), Ethan Alguire(University of Pennsylvania), Josefine H. Andersen(Technical University of Denmark), Vishikh Athavale(University of Pennsylvania), Dennis L. Barton(University of Luxembourg), Khadiza Begam(Kent State University), Andrew Behn(University of California, Berkeley), Nicole Bellonzi(University of Pennsylvania), Yves Bernard(University of Southern California), Eric Berquist(University of Pittsburgh), Hugh G. A. Burton(University of Cambridge), Abel Carreras(Donostia International Physics Center), Kevin Carter-Fenk(The Ohio State University), Romit Chakraborty(Australian National University), Alan D. Chien(University of Michigan), Kristina D. Closser(Australian National University), D. Vale Cofer-Shabica(University of Pennsylvania), Saswata Dasgupta(The Ohio State University), Marc de Wergifosse(University of Southern California), Jia Deng(Australian National University), Michael Diedenhofen, Hainam Do(University of Nottingham), Sebastian Ehlert, Po-Tung Fang(National Taiwan University), Shervin Fatehi(Australian National University), Qingguo Feng(Kent State University), Triet Friedhoff(University of Notre Dame), James R. Gayvert(Boston University), Qinghui Ge(University of California, Berkeley), Gergely Gidofalvi(Gonzaga University), Matthew Goldey(University of California, Berkeley), Joe Gomes(University of California, Berkeley), Cristina E. González‐Espinoza(University of Geneva), Sahil Gulania(University of Southern California), Anastasia O. Gunina(University of Southern California), Magnus W. D. Hanson‐Heine(University of Nottingham), Phillip H. P. Harbach(Heidelberg University), Andreas Hauser(Graz University of Technology), Michael F. Herbst(The University of Sydney), Mario Hernández Vera(Ludwig-Maximilians-Universität München), Manuel Hodecker(Heidelberg University), Zachary C. Holden(The Ohio State University), Shannon E. Houck(Virginia Tech), Xunkun Huang(Xiamen University), Kerwin Hui(National Taiwan University), Bang C. Huynh(University of Cambridge), Maxim Ivanov(University of Southern California), Ádám Jász, Hyunjun Ji(Korea Advanced Institute of Science and Technology), Hanjie Jiang(University of Michigan), Benjamin Kaduk(Massachusetts Institute of Technology), Sven Kähler(University of Southern California), Kirill Khistyaev(University of Southern California), Jaehoon Kim(Korea Advanced Institute of Science and Technology), Gergely Kis, Phil Klunzinger(Digital Wave (United States)), Zsuzsanna Koczor-Benda(Ludwig-Maximilians-Universität München), Joong Hoon Koh(University of Notre Dame), Dmytro Kosenkov(Purdue University West Lafayette), Laura Koulias(Florida State University), Tim Kowalczyk(University of Southern California), Caroline M. Krauter(Heidelberg University), Karl Y. Kue(Institute of Chemistry, Academia Sinica), Alexander A. Kunitsa(Boston University), Thomas Kus(University of Southern California), István Ladjánszki, Arie Landau(University of Southern California), Keith V. Lawler(University of California, Berkeley), Daniel Lefrancois(Heidelberg University), Susi Lehtola(University of Southern California)
August 23, 2021The Journal of Chemical Physics1,361 citations
PDFDOI

1.4k

Citations

26

Influential Citations

The Journal of Chemical Physics

Venue

2021

Year

Abstract

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

Analysis

Why This Paper Matters

This paper is significant because it documents the fifth major release of Q-Chem, a widely used quantum chemistry software package. With over 1,300 citations, it represents a key resource for researchers in computational chemistry and materials science. The paper highlights the continued evolution of Q-Chem as a community-driven, open-teamware project, which is rare in commercial software ecosystems. The inclusion of novel many-body methods and high-performance computing features makes it relevant for AI practitioners working on molecular simulations, drug discovery, and materials design.

Technical Contributions

  • Many-body methods: New variants of coupled-cluster and configuration-interaction approaches, algebraic diagrammatic construction, and variational reduced density-matrix methods.
  • Spectroscopy tools: Core-level spectroscopy, metastable resonances, vibronic spectra, and nuclear-electronic orbital method.
  • Energy decomposition analysis: Several techniques for analyzing intermolecular interactions.
  • High-performance computing: Multithreaded parallelism and GPU support for accelerated calculations.
  • Modular design: Open teamware model enabling contributions from over 100 academic developers.

Results

The paper does not provide specific numerical results or benchmarks but describes the capabilities of Q-Chem 5. It notes that the software supports a comprehensive library of exchange-correlation functionals and correlated methods, and that the high-performance features enable calculations on larger systems than previously possible. The community of over 100 developers indicates widespread adoption and collaborative development.

Significance

For the AI community, Q-Chem 5 provides a robust platform for generating training data for machine learning models in quantum chemistry. Its modular design and open-source-like model facilitate integration with AI workflows. The advances in many-body methods and spectroscopy tools enable more accurate simulations, which can improve the quality of data used for training neural networks in molecular property prediction and materials discovery. The paper underscores the importance of software infrastructure in bridging quantum chemistry and AI.

quantum-chemistrymany-body-methodsdensity-functional-theoryhigh-performance-computingopen-source-softwarespectroscopy

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