While the many-body expansion (MBE) and counterpoise treatments are commonly used to mitigate the high scaling of accurate ab initio methods, researchers may need to piece together tools and scripts if their primary chosen software does not support targeted features. To further modular software in quantum chemistry, the arbitrary-order, multiple-model-chemistry, counterpoise-enabled MBE implementation from Psi4 has been extracted into an independent, lightweight, and open-source Python module, QCManyBody, with new schema underpinning, application programming interface, and software integrations. The package caters to direct users by facilitating single-point and geometry optimization MBE calculations backed by popular quantum chemistry codes through the QCEngine runner and by defining a schema for requesting and reporting many-body computations. It also serves developers and integrators by providing minimal, composable, and extensible interfaces. The design and flexibility of QCManyBody are demonstrated via integrations with geomeTRIC, OptKing, Psi4, QCEngine, and the QCArchive project.

Topics
Quantum chemistry, Ab-initio methods, Many body systems, Crystal lattices
1.
M. S.
Gordon
,
D. G.
Fedorov
,
S. R.
Pruitt
, and
L. V.
Slipchenko
, “
Fragmentation methods: A route to accurate calculations on large systems
,”
Chem. Rev.
112
,
632
–
672
(
2012
).
https://doi.org/10.1021/cr200093j
Google Scholar
Crossref
Search ADS
PubMed
 
2.
J. M.
Herbert
, “
Fantasy versus reality in fragment-based quantum chemistry
,”
J. Chem. Phys.
151
,
170901
(
2019
).
https://doi.org/10.1063/1.5126216
Google Scholar
Crossref
Search ADS
 
3.
J. P.
Heindel
 and
S. S.
Xantheas
, “
The many-body expansion for aqueous systems revisited: I. Water–water interactions
,”
J. Chem. Theory Comput.
16
,
6843
–
6855
(
2020
).
https://doi.org/10.1021/acs.jctc.9b00749
Google Scholar
Crossref
Search ADS
 
4.
S. F.
Boys
 and
F.
Bernardi
, “
The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors
,”
Mol. Phys.
19
,
553
–
566
(
1970
).
https://doi.org/10.1080/00268977000101561
Google Scholar
Crossref
Search ADS
 
5.
J. F.
Ouyang
,
M. W.
Cvitkovic
, and
R. P. A.
Bettens
, “
Trouble with the many-body expansion
,”
J. Chem. Theory Comput.
10
,
3699
–
3707
(
2014
).
https://doi.org/10.1021/ct500396b
Google Scholar
Crossref
Search ADS
 
6.
R. M.
Richard
,
B. W.
Bakr
, and
C. D.
Sherrill
, “
Understanding the many-body basis set superposition error: Beyond Boys and Bernardi
,”
J. Chem. Theory Comput.
14
,
2386
–
2400
(
2018
).
https://doi.org/10.1021/acs.jctc.7b01232
Google Scholar
Crossref
Search ADS
 
7.
E.
Epifanovsky
,
A. T. B.
Gilbert
,
X.
Feng
,
J.
Lee
,
Y.
Mao
,
N.
Mardirossian
,
P.
Pokhilko
,
A. F.
White
,
M. P.
Coons
,
A. L.
Dempwolff
,
Z.
Gan
,
D.
Hait
,
P. R.
Horn
,
L. D.
Jacobson
,
I.
Kaliman
,
J.
Kussmann
,
A. W.
Lange
,
K. U.
Lao
,
D. S.
Levine
,
J.
Liu
,
S. C.
McKenzie
,
A. F.
Morrison
,
K. D.
Nanda
,
F.
Plasser
,
D. R.
Rehn
,
M. L.
Vidal
,
Z.-Q.
You
,
Y.
Zhu
,
B.
Alam
,
B. J.
Albrecht
,
A.
Aldossary
,
E.
Alguire
,
J. H.
Andersen
,
V.
Athavale
,
D.
Barton
,
K.
Begam
,
A.
Behn
,
N.
Bellonzi
,
Y. A.
Bernard
,
E. J.
Berquist
,
H. G. A.
Burton
,
A.
Carreras
,
K.
Carter- Fenk
,
R.
Chakraborty
,
A. D.
Chien
,
K. D.
Closser
,
V.
Cofer-Shabica
,
S.
Dasgupta
,
M.
de Wergifosse
,
J.
Deng
,
M.
Diedenhofen
,
H.
Do
,
S.
Ehlert
,
P.-T.
Fang
,
S.
Fatehi
,
Q.
Feng
,
T.
Friedhoff
,
J.
Gayvert
,
Q.
Ge
,
G.
Gidofalvi
,
M.
Goldey
,
J.
Gomes
,
C. E.
González-Espinoza
,
S.
Gulania
,
A. O.
Gunina
,
M. W. D.
Hanson-Heine
,
P. H. P.
Harbach
,
A.
Hauser
,
M. F.
Herbst
,
M.
Hernández Vera
,
M.
Hodecker
,
Z. C.
Holden
,
S.
Houck
,
X.
Huang
,
K.
Hui
,
B. C.
Huynh
,
M.
Ivanov
,
Á.
Jász
,
H.
Ji
,
H.
Jiang
,
B.
Kaduk
,
S.
Kähler
,
K.
Khistyaev
,
J.
Kim
,
G.
Kis
,
P.
Klunzinger
,
Z.
Koczor-Benda
,
J. H.
Koh
,
D.
Kosenkov
,
L.
Koulias
,
T.
Kowalczyk
,
C. M.
Krauter
,
K.
Kue
,
A.
Kunitsa
,
T.
Kus
,
I.
Ladjańszki
,
A.
Landau
,
K. V.
Lawler
,
D.
Lefrancois
,
S.
Lehtola
,
R. R.
Li
,
Y.-P.
Li
,
J.
Liang
,
M.
Liebenthal
,
H.-H.
Lin
,
Y.-S.
Lin
,
F.
Liu
,
K.-Y.
Liu
,
M.
Loipersberger
,
A.
Luenser
,
A.
Manjanath
,
P.
Manohar
,
E.
Mansoor
,
S. F.
Manzer
,
S.-P.
Mao
,
A. V.
Marenich
,
T.
Markovich
,
S.
Mason
,
S. A.
Maurer
,
P. F.
McLaughlin
,
M. F. S. J.
Menger
,
J.-M.
Mewes
,
S. A.
Mewes
,
P.
Morgante
,
J. W.
Mullinax
,
K. J.
Oosterbaan
,
G.
Paran
,
A. C.
Paul
,
S. K.
Paul
,
F.
Pavošević
,
Z.
Pei
,
S.
Prager
,
E. I.
Proynov
,
Á.
Rák
,
E.
Ramos-Cordoba
,
B.
Rana
,
A. E.
Rask
,
A.
Rettig
,
R. M.
Richard
,
F.
Rob
,
E.
Rossomme
,
T.
Scheele
,
M.
Scheurer
,
M.
Schneider
,
N.
Sergueev
,
S. M.
Sharada
,
W.
Skomorowski
,
D. W.
Small
,
C. J.
Stein
,
Y.-C.
Su
,
E. J.
Sundstrom
,
Z.
Tao
,
J.
Thirman
,
G. J.
Tornai
,
T.
Tsuchimochi
,
N. M.
Tubman
,
S. P.
Veccham
,
O.
Vydrov
,
J.
Wenzel
,
J.
Witte
,
A.
Yamada
,
K.
Yao
,
S.
Yeganeh
,
S. R.
Yost
,
A.
Zech
,
I. Y.
Zhang
,
X.
Zhang
,
Y.
Zhang
,
D.
Zuev
,
A.
Aspuru-Guzik
,
A. T.
Bell
,
N. A.
Besley
,
K. B.
Bravaya
,
B. R.
Brooks
,
D.
Casanova
,
J.-D.
Chai
,
S.
Coriani
,
C. J.
Cramer
,
G.
Cserey
,
A. E.
DePrince
III,
R. A.
DiStasio
, Jr.,
A.
Dreuw
,
B. D.
Dunietz
,
T. R.
Furlani
,
W. A.
Goddard
III,
S.
Hammes-Schiffer
,
T.
Head-Gordon
,
W. J.
Hehre
,
C.-P.
Hsu
,
T.-C.
Jagau
,
Y.
Jung
,
A.
Klamt
,
J.
Kong
,
D. S.
Lambrecht
,
W.
Liang
,
N. J.
Mayhall
,
C. W.
McCurdy
,
J. B.
Neaton
,
C.
Ochsenfeld
,
J. A.
Parkhill
,
R.
Peverati
,
V. A.
Rassolov
,
Y.
Shao
,
L. V.
Slipchenko
,
T.
Stauch
,
R. P.
Steele
,
J. E.
Subotnik
,
A. J. W.
Thom
,
A.
Tkatchenko
,
D. G.
Truhlar
,
T.
Van Voorhis
,
T. A.
Wesolowski
,
K. B.
Whaley
,
H. L.
Woodcock
III,
P. M.
Zimmerman
,
S.
Faraji
,
P. M. W.
Gill
,
M.
Head-Gordon
,
J. M.
Herbert
, and
A. I.
Krylov
, “
Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package
,”
J. Chem. Phys.
155
,
084801
(
2021
).
https://doi.org/10.1063/5.0055522
Google Scholar
Crossref
Search ADS
 
8.
D. G. A.
Smith
,
L. A.
Burns
,
A. C.
Simmonett
,
R. M.
Parrish
,
M. C.
Schieber
,
R.
Galvelis
,
P.
Kraus
,
H.
Kruse
,
R.
Di Remigio
,
A.
Alenaizan
,
A. M.
James
,
S.
Lehtola
,
J. P.
Misiewicz
,
M.
Scheurer
,
R. A.
Shaw
,
J. B.
Schriber
,
Y.
Xie
,
Z. L.
Glick
,
D. A.
Sirianni
,
J. S.
O’Brien
,
J. M.
Waldrop
,
A.
Kumar
,
E. G.
Hohenstein
,
B. P.
Pritchard
,
B. R.
Brooks
,
H. F.
Schaefer
III,
A. Y.
Sokolov
,
K.
Patkowski
,
A. E.
DePrince
III,
U.
Bozkaya
,
R. A.
King
,
F. A.
Evangelista
,
J. M.
Turney
,
T. D.
Crawford
, and
C. D.
Sherrill
, “
PSI4 1.4: Open-source software for high-throughput quantum chemistry
,”
J. Chem. Phys.
152
,
184108
(
2020
).
https://doi.org/10.1063/5.0006002
Google Scholar
Crossref
Search ADS
 
9.
J. M.
Turney
 (
2024
). “
PSI4: Open-source quantum chemistry
,” GitHub for the current version, see https://github.com/psi4/psi4, for the originating project, see https://psicode.org/
Google Scholar
 
10.
M. J.
Frisch
,
G. W.
Trucks
,
H. B.
Schlegel
,
G. E.
Scuseria
,
M. A.
Robb
,
J. R.
Cheeseman
,
G.
Scalmani
,
V.
Barone
,
G. A.
Petersson
,
H.
Nakatsuji
,
X.
Li
,
M.
Caricato
,
A. V.
Marenich
,
J.
Bloino
,
B. G.
Janesko
,
R.
Gomperts
,
B.
Mennucci
,
H. P.
Hratchian
,
J. V.
Ortiz
,
A. F.
Izmaylov
,
J. L.
Sonnenberg
,
D.
Williams-Young
,
F.
Ding
,
F.
Lipparini
,
F.
Egidi
,
J.
Goings
,
B.
Peng
,
A.
Petrone
,
T.
Henderson
,
D.
Ranasinghe
,
V. G.
Zakrzewski
,
J.
Gao
,
N.
Rega
,
G.
Zheng
,
W.
Liang
,
M.
Hada
,
M.
Ehara
,
K.
Toyota
,
R.
Fukuda
,
J.
Hasegawa
,
M.
Ishida
,
T.
Nakajima
,
Y.
Honda
,
O.
Kitao
,
H.
Nakai
,
T.
Vreven
,
K.
Throssell
,
J. A.
Montgomery
, Jr.,
J. E.
Peralta
,
F.
Ogliaro
,
M. J.
Bearpark
,
J. J.
Heyd
,
E. N.
Brothers
,
K. N.
Kudin
,
V. N.
Staroverov
,
T. A.
Keith
,
R.
Kobayashi
,
J.
Normand
,
K.
Raghavachari
,
A. P.
Rendell
,
J. C.
Burant
,
S. S.
Iyengar
,
J.
Tomasi
,
M.
Cossi
,
J. M.
Millam
,
M.
Klene
,
C.
Adamo
,
R.
Cammi
,
J. W.
Ochterski
,
R. L.
Martin
,
K.
Morokuma
,
O.
Farkas
,
J. B.
Foresman
, and
D. J.
Fox
,
Gaussian 16
,
Gaussian, Inc.
,
Wallingford, CT
,
2016
.
11.
E.
Aprà
,
E. J.
Bylaska
,
W. A.
de Jong
,
N.
Govind
,
K.
Kowalski
,
T. P.
Straatsma
,
M.
Valiev
,
H. J. J.
van Dam
,
Y.
Alexeev
,
J.
Anchell
,
V.
Anisimov
,
F. W.
Aquino
,
R.
Atta-Fynn
,
J.
Autschbach
,
N. P.
Bauman
,
J. C.
Becca
,
D. E.
Bernholdt
,
K.
Bhaskaran-Nair
,
S.
Bogatko
,
P.
Borowski
,
J.
Boschen
,
J.
Brabec
,
A.
Bruner
,
E.
Cauët
,
Y.
Chen
,
G. N.
Chuev
,
C. J.
Cramer
,
J.
Daily
,
M. J. O.
Deegan
,
T. H.
Dunning
,
M.
Dupuis
,
K. G.
Dyall
,
G. I.
Fann
,
S. A.
Fischer
,
A.
Fonari
,
H.
Früchtl
,
L.
Gagliardi
,
J.
Garza
,
N.
Gawande
,
S.
Ghosh
,
K.
Glaesemann
,
A. W.
Götz
,
J.
Hammond
,
V.
Helms
,
E. D.
Hermes
,
K.
Hirao
,
S.
Hirata
,
M.
Jacquelin
,
L.
Jensen
,
B. G.
Johnson
,
H.
Jónsson
,
R. A.
Kendall
,
M.
Klemm
,
R.
Kobayashi
,
V.
Konkov
,
S.
Krishnamoorthy
,
M.
Krishnan
,
Z.
Lin
,
R. D.
Lins
,
R. J.
Littlefield
,
A. J.
Logsdail
,
K.
Lopata
,
W.
Ma
,
A. V.
Marenich
,
J.
Martin del Campo
,
D.
Mejia-Rodriguez
,
J. E.
Moore
,
J. M.
Mullin
,
T.
Nakajima
,
D. R.
Nascimento
,
J. A.
Nichols
,
P. J.
Nichols
,
J.
Nieplocha
,
A.
Otero-de-la Roza
,
B.
Palmer
,
A.
Panyala
,
T.
Pirojsirikul
,
B.
Peng
,
R.
Peverati
,
J.
Pittner
,
L.
Pollack
,
R. M.
Richard
,
P.
Sadayappan
,
G. C.
Schatz
,
W. A.
Shelton
,
D. W.
Silverstein
,
D. M. A.
Smith
,
T. A.
Soares
,
D.
Song
,
M.
Swart
,
H. L.
Taylor
,
G. S.
Thomas
,
V.
Tipparaju
,
D. G.
Truhlar
,
K.
Tsemekhman
,
T.
Van Voorhis
,
Á.
Vázquez-Mayagoitia
,
P.
Verma
,
O.
Villa
,
A.
Vishnu
,
K. D.
Vogiatzis
,
D.
Wang
,
J. H.
Weare
,
M. J.
Williamson
,
T. L.
Windus
,
K.
Woliński
,
A. T.
Wong
,
Q.
Wu
,
C.
Yang
,
Q.
Yu
,
M.
Zacharias
,
Z.
Zhang
,
Y.
Zhao
, and
R. J.
Harrison
, “
NWChem: Past, present, and future
,”
J. Chem. Phys.
152
,
184102
(
2020
).
https://doi.org/10.1063/5.0004997
Google Scholar
Crossref
Search ADS
 
12.
Y. J.
Franzke
,
C.
Holzer
,
J. H.
Andersen
,
T.
Begušić
,
F.
Bruder
,
S.
Coriani
,
F.
Della Sala
,
E.
Fabiano
,
D. A.
Fedotov
,
S.
Fürst
,
S.
Gillhuber
,
R.
Grotjahn
,
M.
Kaupp
,
M.
Kehry
,
M.
Krstić
,
F.
Mack
,
S.
Majumdar
,
B. D.
Nguyen
,
S. M.
Parker
,
F.
Pauly
,
A.
Pausch
,
E.
Perlt
,
G. S.
Phun
,
A.
Rajabi
,
D.
Rappoport
,
B.
Samal
,
T.
Schrader
,
M.
Sharma
,
E.
Tapavicza
,
R. S.
Treß
,
V.
Voora
,
A.
Wodyński
,
J. M.
Yu
,
B.
Zerulla
,
F.
Furche
,
C.
Hättig
,
M.
Sierka
,
D. P.
Tew
, and
F.
Weigend
, “
Turbomole: Today and tomorrow
,”
J. Chem. Theory Comput.
19
,
6859
–
6890
(
2023
).
https://doi.org/10.1021/acs.jctc.3c00347
Google Scholar
Crossref
Search ADS
 
13.
H.-J.
Werner
,
P. J.
Knowles
,
F. R.
Manby
,
J. A.
Black
,
K.
Doll
,
A.
Heßelmann
,
D.
Kats
,
A.
Köhn
,
T.
Korona
,
D. A.
Kreplin
,
Q.
Ma
,
T. F.
Miller
III,
A.
Mitrushchenkov
,
K. A.
Peterson
,
I.
Polyak
,
G.
Rauhut
, and
M.
Sibaev
, “
The Molpro quantum chemistry package
,”
J. Chem. Phys.
152
,
144107
(
2020
).
https://doi.org/10.1063/5.0005081
Google Scholar
Crossref
Search ADS
 
14.
S.
Simon
,
M.
Duran
, and
J. J.
Dannenberg
, “
How does basis set superposition error change the potential surfaces for hydrogen-bonded dimers?
,”
J. Chem. Phys.
105
,
11024
–
11031
(
1996
).
https://doi.org/10.1063/1.472902
Google Scholar
Crossref
Search ADS
 
15.
P.
Salvador
 and
M. M.
Szczȩśniak
, “
Counterpoise-corrected geometries and harmonic frequencies of N-body clusters: Application to (HF)n (n = 3, 4)
,”
J. Chem. Phys.
118
,
537
–
549
(
2003
).
https://doi.org/10.1063/1.1527011
Google Scholar
Crossref
Search ADS
 
16.
B. G.
Peyton
 and
T. D.
Crawford
, “
Basis set superposition errors in the many-body expansion of molecular properties
,”
J. Phys. Chem. A
123
,
4500
–
4511
(
2019
).
https://doi.org/10.1021/acs.jpca.9b03864
Google Scholar
Crossref
Search ADS
 
17.
J.
Řezáč
, “
Cuby: An integrative framework for computational chemistry
,”
J. Comput. Chem.
37
,
1230
–
1237
(
2016
).
https://doi.org/10.1002/jcc.24312
Google Scholar
Crossref
Search ADS
 
18.
J.
Řezáč
,
O. V.
Kontkanen
, and
M.
Nováček
, “
Working with benchmark datasets in the Cuby framework
,”
J. Chem. Phys.
160
,
202501
(
2024
).
https://doi.org/10.1063/5.0203372
Google Scholar
Crossref
Search ADS
 
19.
J.
Řezáč
, Cuby—Ruby framework for computational chemistry, version 4, http://cuby4.molecular.cz (accessed April 2024).
20.
P.
Seeber
,
S.
Seidenath
,
J.
Steinmetzer
, and
S.
Gräfe
, “
Growing spicy ONIOMs: Extending and generalizing concepts of ONIOM and many body expansions
,”
Wiley Interdiscip. Rev.: Comput. Mol. Sci.
13
,
e1644
(
2023
).
https://doi.org/10.1002/wcms.1644
Google Scholar
Crossref
Search ADS
 
21.
P.
Seeber
 and
S.
Seidenath
, Spicy: Computational chemistry with multilayer fragment methods. For the current version, see https://gitlab.com/theoretical-chemistry-jena/quantum-chemistry/Spicy (accessed April 2024).
22.
L. W.
Chung
,
W. M. C.
Sameera
,
R.
Ramozzi
,
A. J.
Page
,
M.
Hatanaka
,
G. P.
Petrova
,
T. V.
Harris
,
X.
Li
,
Z.
Ke
,
F.
Liu
,
H.-B.
Li
,
L.
Ding
, and
K.
Morokuma
, “
The ONIOM method and its applications
,”
Chem. Rev.
115
,
5678
–
5796
(
2015
).
https://doi.org/10.1021/cr5004419
Google Scholar
Crossref
Search ADS
PubMed
 
23.
R. M.
Richard
 and
J. M.
Herbert
, “
A generalized many-body expansion and a unified view of fragment-based methods in electronic structure theory
,”
J. Chem. Phys.
137
,
064113
(
2012
).
https://doi.org/10.1063/1.4742816
Google Scholar
Crossref
Search ADS
 
24.
D. R.
Broderick
 and
J. M.
Herbert
, “
Scalable generalized screening for high-order terms in the many-body expansion: Algorithm, open-source implementation, and demonstration
,”
J. Chem. Phys.
159
,
174801
(
2023
).
https://doi.org/10.1063/5.0174293
Google Scholar
Crossref
Search ADS
 
25.
D.
Broderick
,
P.
Bowling
, and
J.
Herbert
, Fragme∩t: Fragmentation code for the Herbert group. For the current version, see https://gitlab.com/john-herbert-group/fragment (accessed April 2024).
26.
E. F.
Bull-Vulpe
,
M.
Riera
,
A. W.
Götz
, and
F.
Paesani
, “
MB-Fit: Software infrastructure for data-driven many-body potential energy functions
,”
J. Chem. Phys.
155
,
124801
(
2021
).
https://doi.org/10.1063/5.0063198
Google Scholar
Crossref
Search ADS
 
27.
M.
Riera
,
C.
Knight
,
E. F.
Bull-Vulpe
,
X.
Zhu
,
H.
Agnew
,
D. G. A.
Smith
,
A. C.
Simmonett
, and
F.
Paesani
, “
MBX: A many-body energy and force calculator for data-driven many-body simulations
,”
J. Chem. Phys.
159
,
054802
(
2023
).
https://doi.org/10.1063/5.0156036
Google Scholar
Crossref
Search ADS
 
28.
K.
Focke
,
M.
De Santis
,
M.
Wolter
,
J. A.
Martinez B
,
V.
Vallet
,
A. S.
Pereira Gomes
,
M.
Olejniczak
, and
C. R.
Jacob
, “
Interoperable workflows by exchanging grid-based data between quantum-chemical program packages
,”
J. Chem. Phys.
160
,
162503
(
2024
).
https://doi.org/10.1063/5.0201701
Google Scholar
Crossref
Search ADS
 
29.
V.
Blum
,
R.
Asahi
,
J.
Autschbach
,
C.
Bannwarth
,
G.
Bihlmayer
,
S.
Blügel
,
L. A.
Burns
,
T. D.
Crawford
,
W.
Dawson
,
W. A.
de Jong
,
C.
Draxl
,
C.
Filippi
,
L.
Genovese
,
P.
Giannozzi
,
N.
Govind
,
S.
Hammes-Schiffer
,
J. R.
Hammond
,
B.
Hourahine
,
A.
Jain
,
Y.
Kanai
,
P. R. C.
Kent
,
A. H.
Larsen
,
S.
Lehtola
,
X.
Li
,
R.
Lindh
,
S.
Maeda
,
N.
Makri
,
J.
Moussa
,
T.
Nakajima
,
J. A.
Nash
,
M. J. T.
Oliveira
,
P. D.
Patel
,
G.
Pizzi
,
G.
Pourtois
,
B. P.
Pritchard
,
E.
Rabani
,
M.
Reiher
,
L.
Reining
,
X.
Ren
,
M.
Rossi
,
H. B.
Schlegel
,
N.
Seriani
,
L. V.
Slipchenko
,
A.
Thom
,
E. F.
Valeev
,
B.
Van Troeye
,
L.
Visscher
,
V.
Vlcek
,
H.-J.
Werner
,
D. B.
Williams-Young
, and
T.
Windus
, “
Roadmap on methods and software for electronic structure based simulations in chemistry and materials
,”
Electron. Struct.
(to be published) (
2024
).
https://doi.org/10.1088/2516-1075/ad48ec
Google Scholar
 
30.
R.
Di Felice
,
M. L.
Mayes
,
R. M.
Richard
,
D. B.
Williams-Young
,
G. K.-L.
Chan
,
W. A.
de Jong
,
N.
Govind
,
M.
Head-Gordon
,
M. R.
Hermes
,
K.
Kowalski
,
X.
Li
,
H.
Lischka
,
K. T.
Mueller
,
E.
Mutlu
,
A. M. N.
Niklasson
,
M. R.
Pederson
,
B.
Peng
,
R.
Shepard
,
E. F.
Valeev
,
M.
van Schilfgaarde
,
B.
Vlaisavljevich
,
T. L.
Windus
,
S. S.
Xantheas
,
X.
Zhang
, and
P. M.
Zimmerman
, “
A perspective on sustainable computational chemistry software development and integration
,”
J. Chem. Theory Comput.
19
,
7056
–
7076
(
2023
).
https://doi.org/10.1021/acs.jctc.3c00419
Google Scholar
Crossref
Search ADS
 
31.
D. G. A.
Smith
,
D.
Altarawy
,
L. A.
Burns
,
M.
Welborn
,
L. N.
Naden
,
L.
Ward
,
S.
Ellis
,
B. P.
Pritchard
, and
T. D.
Crawford
, “
The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data
,”
Wiley Interdiscip. Rev.: Comput. Mol. Sci.
11
,
e1491
(
2021
).
https://doi.org/10.1002/wcms.1491
Google Scholar
Crossref
Search ADS
 
32.
B. P.
Britchard
,
D. G. A.
Smith
,
D.
Altarawy
,
M.
Welborn
, and
L.
Naden
 (
2024
). “
QCFractal: A distributed compute and database platform for quantum chemistry
,” GitHub for the current version, see https://github.com/MolSSI/QCFractal
Google Scholar
 
33.
D. G. A.
Smith
,
A. T.
Lolinco
,
Z. L.
Glick
,
J.
Lee
,
A.
Alenaizan
,
T. A.
Barnes
,
C. H.
Borca
,
R.
Di Remigio
,
D. L.
Dotson
,
S.
Ehlert
,
A. G.
Heide
,
M. F.
Herbst
,
J.
Hermann
,
C. B.
Hicks
,
J. T.
Horton
,
A. G.
Hurtado
,
P.
Kraus
,
H.
Kruse
,
S. J. R.
Lee
,
J. P.
Misiewicz
,
L. N.
Naden
,
F.
Ramezanghorbani
,
M.
Scheurer
,
J. B.
Schriber
,
A. C.
Simmonett
,
J.
Steinmetzer
,
J. R.
Wagner
,
L.
Ward
,
M.
Welborn
,
D.
Altarawy
,
J.
Anwar
,
J. D.
Chodera
,
A.
Dreuw
,
H. J.
Kulik
,
F.
Liu
,
T. J.
Martínez
,
D. A.
Matthews
,
H. F.
Schaefer
III,
J.
Šponer
,
J. M.
Turney
,
L.-P.
Wang
,
N.
De Silva
,
R. A.
King
,
J. F.
Stanton
,
M. S.
Gordon
,
T. L.
Windus
,
C. D.
Sherrill
, and
L. A.
Burns
, “
Quantum chemistry common driver and databases (QCDB) and quantum chemistry engine (QCEngine): Automation and interoperability among computational chemistry programs
,”
J. Chem. Phys.
155
,
204801
(
2021
).
https://doi.org/10.1063/5.0059356
Google Scholar
Crossref
Search ADS
 
34.
Conda-forge community
, The Conda-forge project: Community-based software distribution built on the Conda package format and ecosystem,
2015
.
35.
D. G. A.
Smith
,
L. A.
Burns
,
S.
Lee
,
L.
Ward
, and
D. L.
Dotson
 (
2024
). “
QCEngine: Quantum chemistry program executor and IO standardizer (QCSchema)
,” GitHub for the current version, see https://github.com/MolSSI/QCEngine
Google Scholar
 
36.
F.
Ballesteros
,
J. A.
Tan
, and
K. U.
Lao
, “
An accurate and efficient fragmentation approach via the generalized many-body expansion for density matrices
,”
J. Chem. Phys.
159
,
074107
(
2023
).
https://doi.org/10.1063/5.0160810
Google Scholar
Crossref
Search ADS
 
37.
U.
Góra
,
R.
Podeszwa
,
W.
Cencek
, and
K.
Szalewicz
, “
Interaction energies of large clusters from many-body expansion
,”
J. Chem. Phys.
135
,
224102
(
2011
).
https://doi.org/10.1063/1.3664730
Google Scholar
Crossref
Search ADS
 
38.
R. M.
Richard
,
K. U.
Lao
, and
J. M.
Herbert
, “
Understanding the many-body expansion for large systems. I. Precision considerations
,”
J. Chem. Phys.
141
,
014108
(
2014
).
https://doi.org/10.1063/1.4885846
Google Scholar
Crossref
Search ADS
 
39.
B. H.
Wells
 and
S.
Wilson
, “
Van der Waals interaction potentials: Many-body basis set superposition effects
,”
Chem. Phys. Lett.
101
,
429
–
434
(
1983
).
https://doi.org/10.1016/0009-2614(83)87508-3
Google Scholar
Crossref
Search ADS
 
40.
P.
Valiron
 and
I.
Mayer
, “
Hierarchy of counterpoise corrections for N-body clusters: Generalization of the Boys-Bernardi scheme
,”
Chem. Phys. Lett.
275
,
46
–
55
(
1997
).
https://doi.org/10.1016/s0009-2614(97)00689-1
Google Scholar
Crossref
Search ADS
 
41.
J.
Rezac
 and
D. R.
Salahub
, “
Multilevel fragment-based approach (MFBA): A novel hybrid computational method for the study of large molecules
,”
J. Chem. Theory Comput.
6
,
91
–
99
(
2010
).
https://doi.org/10.1021/ct900413s
Google Scholar
Crossref
Search ADS
 
42.
G. J. O.
Beran
 and
K.
Nanda
, “
Predicting organic crystal lattice energies with chemical accuracy
,”
J. Phys. Chem. Lett.
1
,
3480
–
3487
(
2010
).
https://doi.org/10.1021/jz101383z
Google Scholar
Crossref
Search ADS
 
43.
N. J.
Mayhall
 and
K.
Raghavachari
, “
Molecules-in-molecules: An extrapolated fragment-based approach for accurate calculations on large molecules and materials
,”
J. Chem. Theory Comput.
7
,
1336
–
1343
(
2011
).
https://doi.org/10.1021/ct200033b
Google Scholar
Crossref
Search ADS
 
44.
J. C.
Howard
 and
G. S.
Tschumper
, “
N-body:many-body QM:QM vibrational frequencies: Application to small hydrogen-bonded clusters
,”
J. Chem. Phys.
139
,
184113
(
2013
).
https://doi.org/10.1063/1.4829463
Google Scholar
Crossref
Search ADS
 
45.
J. C.
Howard
 and
G. S.
Tschumper
, “
Benchmark structures and harmonic vibrational frequencies near the CCSD(T) complete basis set limit for small water clusters: (H2O)n=2,3,4,5,6
,”
J. Chem. Theory Comput.
11
,
2126
–
2136
(
2015
).
https://doi.org/10.1021/acs.jctc.5b00225
Google Scholar
Crossref
Search ADS
 
46.
S.
Colvin
,
E.
Jolibois
,
H.
Ramezani
,
A. G.
Badaracco
,
T.
Dorsey
,
D.
Montague
,
S.
Matveenko
,
M.
Trylesinski
,
S.
Runkle
,
D.
Hewitt
, and
A.
Hall
 (
2024
). “
pydantic: Data parsing and validation using Python type hints
,” GitHub for the current version, see https://github.com/pydantic/pydantic
Google Scholar
 
47.

Snippets 1 and 2 and Ref. 97 of Ref. 33 for an overview of the QCElemental Molecule object.

48.
C. R.
Harris
,
K. J.
Millman
,
S. J.
van der Walt
,
R.
Gommers
,
P.
Virtanen
,
D.
Cournapeau
,
E.
Wieser
,
J.
Taylor
,
S.
Berg
,
N. J.
Smith
,
R.
Kern
,
M.
Picus
,
S.
Hoyer
,
M. H.
van Kerkwijk
,
M.
Brett
,
A.
Haldane
,
J. F.
del Río
,
M.
Wiebe
,
P.
Peterson
,
P.
Gérard-Marchant
,
K.
Sheppard
,
T.
Reddy
,
W.
Weckesser
,
H.
Abbasi
,
C.
Gohlke
, and
T. E.
Oliphant
, “
Array programming with NumPy
,”
Nature
585
,
357
–
362
(
2020
).
https://doi.org/10.1038/s41586-020-2649-2
Google Scholar
Crossref
Search ADS
PubMed
 
49.
D. G. A.
Smith
,
L. A.
Burns
,
L.
Naden
, and
M.
Welborn
 (
2024
). “
QCElemental: Periodic table, physical constants, and molecule parsing for quantum chemistry
,” GitHub for the current version, see https://github.com/MolSSI/QCElemental
Google Scholar
 
50.
K. U.
Lao
,
K.-Y.
Liu
,
R. M.
Richard
, and
J. M.
Herbert
, “
Understanding the many-body expansion for large systems. II. Accuracy considerations
,”
J. Chem. Phys.
144
,
164105
(
2016
).
https://doi.org/10.1063/1.4947087
Google Scholar
Crossref
Search ADS
 
51.
A.
Heide
 and
R. A.
King
 (
2024
). “
optking: A Python version of the PSI4 geometry optimizer
,” GitHub for the current version, see https://github.com/psi-rking/optking
Google Scholar
 
52.
L.-P.
Wang
 and
C.
Song
, “
Geometry optimization made simple with translation and rotation coordinates
,”
J. Chem. Phys.
144
,
214108
(
2016
).
https://doi.org/10.1063/1.4952956
Google Scholar
Crossref
Search ADS
 
53.
L.-P.
Wang
,
H.
Park
,
D. G. A.
Smith
, and
Y.
Qiu
 (
2024
). “
geomeTRIC: A geometry optimization code that includes the TRIC coordinate system
,” GitHub for the current version, see https://github.com/leeping/geomeTRIC
Google Scholar
 
54.

Figure 4(b), the “6b” was optimized with CP, and the “4b” used Cfour58 backend. Otherwise, the computations used VMFC and NWChem as labeled.

55.
C. H.
Borca
,
B. W.
Bakr
,
L. A.
Burns
, and
C. D.
Sherrill
, “
CrystaLattE: Automated computation of lattice energies of organic crystals exploiting the many-body expansion to achieve dual-level parallelism
,”
J. Chem. Phys.
151
,
144103
(
2019
).
https://doi.org/10.1063/1.5120520
Google Scholar
Crossref
Search ADS
 
56.
C. H.
Borca
,
C. D.
Sherrill
,
Z. L.
Glick
,
P. M.
Nelson
, and
L. A.
Burns
 (
2024
). “
CrystaLattE: Automating the calculation of crystal lattice energies
,” GitHub For the current version, see https://github.com/carlosborca/CrystaLattE
Google Scholar
 
57.
P. M.
Nelson
 and
C. D.
Sherrill
, “
Convergence of three-body energies in crystalline acetic acid, formamide, and imidazole
” (unpublished).
58.
D. A.
Matthews
,
L.
Cheng
,
M. E.
Harding
,
F.
Lipparini
,
S.
Stopkowicz
,
T.-C.
Jagau
,
P. G.
Szalay
,
J.
Gauss
, and
J. F.
Stanton
, “
Coupled-cluster techniques for computational chemistry: The CFOUR program package
,”
J. Chem. Phys.
152
,
214108
(
2020
).
https://doi.org/10.1063/5.0004837
Google Scholar
Crossref
Search ADS
 
© 2024 Author(s). Published under an exclusive license by AIP Publishing.
2024
Author(s)
You do not currently have access to this content.