In our continuing effort to expand MolSSI’s outreach and enhance our benefit to the community, we provide a repository of MolSSI-sponsored publications. (Bolded names indicate a MOlSSI Software Fellow, Scientist, or member of the Board of Directors.). For an inventory of the MolSSI’s most highly cited publications, visit our Google Scholar listing!
- Kodrycka, M.; & Patkowski, K.
Efficient Density-Fitted Explicitly Correlated Dispersion and Exchange Dispersion Energies
J. Chem. Theory Comput., 2021, 17(3), 1435-1456
10.1021/acs.jctc.0c01158 - Banerjee, S.; & Sokolov, A.Y.
Efficient implementation of the single-reference algebraic diagrammatic construction theory for charged excitations: Applications to the TEMPO radical and DNA base pairs
Journal of Chemical Physics, 2021, 154(7), 124103
10.1063/5.0040317 - Deustua, J.E.; Shen, J.; Piecuch, P.
High-level coupled-cluster energetics by Monte Carlo sampling and moment expansions: Further details and comparisons
Journal of Chemical Physics, 2021, 154(12), 074105
10.1063/5.0045468 - Barnes, T.; Marin-Rimoldi, E.; Ellis, S.; Crawford, T.D.
The MolSSI Driver Interface Project: A framework for standardized, on-the-fly interoperability between computational molecular sciences codes
Computer Physics Communications, 2021, 261, 107688
10.1016/j.cpc.2020.107688 - Janet, J.P.; Duan, C.; Nandy, A.; Liu, F.; Kulik, H.J.
Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
Acc. Chem. Res. 2021, 54, 3, 532–545
10.1021/acs.accounts.0c00686 - Husch, T.; Sun, J.; Cheng, L.; Lee, S.J.R.; Miller, T.F.
Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
Journal of Chemical Physics, 2021, 154(6), 064108
10.1063/5.0032362 - Nash, J.; Barnes, T.; & Vaissier Welborn, V.
ELECTRIC: Electric fields Leveraged from multipole Expansion Calculations in Tinker Rapid Interface Code
Journal of Open Source Software (JOSS), 2020, 5(54), 2576
10.21105/joss.02576 - Duan, C.; Liu, F.; Nandy, A, & Kulik, H.J.
Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-Off in Multireference Diagnostics
J. Chem. Theory Comput., 2020, 16, 4376-4387.
10.1021/acs.jctc.0c00358 - Gayday, I.; Grushnikova, E.; & Babikov, D.
Influence of the Coriolis Effect on the Properties of Scattering Resonances in Symmetric and Asymmetric Isotopomers of Ozone
Physical Chemistry Chemical Physics, 2020, 22, 27560-27571.
10.1039/D0CP05060A - Riera, M.; Hirales, A.; Ghosh, R.; & Paesani, F.
Data-Driven Many-Body Model with Chemical Accuracy for CH4/H2O Mixtures
The Journal of Physical Chemistry B, 2020, 11207-11221
10.1021/acs.jpcb.0c08728 - Eriksen, J.J.; Anderson, T.A.; Deustua, J.E. ……. Gauss, J. (24 authors)
The Ground State Electronic Energy of Benzene
J. Phys. Chem. Lett., 2020, 11(20), 8922.
10.1021/acs.jpclett.0c02621 - Yao, Y.; Giner, E.; Li, J.; Toulouse, J.; & Umrigar, C.J.
Almost exact energies for the Gaussian-2 set with the semistochastic heat-bath configuration interaction method
Journal of Chemical Physics, 2020, 153(12), 4117
10.1063/5.0018577 - Zagorec-Marks, W.; Smith J.E.T.; Foreman, M.M.; Sharma, S.; & Weber, J.M.
Intrinsic Electronic Spectra of Cryogenically Prepared Protoporphyrin IX Ions in Vacuo: Deprotonation-induced Stark Shifts
Physical Chemistry Chemical Physics, 2020, 36.
10.1039/d0cp03614e - Stair, N.H.; & Evangelista, F. A.
Exploring Hilbert Space on a Budget: Novel Benchmark Set and Performance Metric for Testing Electronic Structure Methods in the Regime of Strong Correlation
Journal of Chemical Physics, 2020, 153(10), 4108
10.1063/5.0014928 - Hanwell, M.D.; Harris, C.; Genova, A.; Haghighatlari, M.; El Khatib, M.; Avery, P.; Hachmann, J.; & de Jong, W.A.
Open Chemistry, JupyterLab, REST, and Quantum Chemistry
International Journal of Quantum Chemistry, 2020, e26472
10.1002/qua.26472
- Sidky, H.; Chen, W.; & Ferguson, A.L.
Molecular Latent Space Simulators
Chemical Science, 2020, 35.
10.1039/D0SC03635H - Yuwono, S.H.; Chakraborty, A.; Deustua, J.E.; Shen, J.; & Piecuch, P.
Accelerating Convergence of Equation-of-Motion Coupled-Cluster Computations Using the Semi-stochastic CC(P;Q) Formalism
Molecular Physics, 2020, Article e1817592
10.1080/00268976.2020.1817592 - Duan C.; Liu, F.; Nandy, A.; & Kulik, H.J.
Semi-supervised Machine Learning Enables the Robust Detection of Multirefence Character at Low Cost
J. Phys. Chem. Lett., 2020, 11(16), 6640.
10.1021/acs.jpclett.0c02018 - Sezginel, K.T. & Wilmer, C.E.
Modeling Diffusion of Nanocars on a Cu (110) Surface
Molecular Systems Design & Engineering, 2020, 7
10.1039/C9ME00171A - Gao, X.; Ramezanghorbani, F.; Isayev, O.; Smith, J.S.; Roitberg, A.
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
J. Chem. Inf. Model., 2020, 60(7), 3408-3415.
10.1021/acs.jcim.0c00451 - Sun, Q.; Xhang, X.; Banerjee, S.; Bao, P.; Barbry, M., et al.
Recent Developments in the PySCF Program Package
Journal of Chemical Physics, 2020, 153(2)
10.1063/5.0006074 - Dick, S. & Fernandez-Serra, M.
Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density
Nature Communications, 2020, 11, 3509
10.1038/s41467-020-17265-7 - Smith, D.G.A.; Altarawy, D.; Burns, L.A.; Welborn, M.; Naden, L.N,; Ward, L.; Ellis, S.; Pritchard, B.; Crawford, T.D.
The MolSSI QCArchive Project: An Open-source Platform to Compute, Organize, and Share Quantum Chemistry Data
WIREs Computational Molecular Science, 2020
10.1002/wcms.1491 - Barca, G.M.J. … Deustua, J.E.; et al. (42 authors)
Recent Developments in the General Atomic and Molecular Electronic Structure System
Journal of Chemical Physics, 2020, 152(15), 4102
10.1063/5.0005188 - Qiu, Y.; Smith, D.G.A.; Stern, C.D.; Feng, M.; Jang, H.; and Wang, L.-P.
Driving Torsion Scans with Wavefront Propagation
Journal of Chemical Physics, 2020, 152(24), 4116.
10.1063/5.0009232 - Yao, Y.; Umrigar, J.; Elser, V.
Chemistry of the Spin-½ Kagome Heisenberg Antiferromagnet
Physical Review B 2020, 102.
10.1103/PhysRevB.102.014413 - Oliveira, M.J. T.; ….. Smith, D.G.A.,; Wu, V. W-Z.
The CECAM Electronic Structure Library and the Modular Software Development Paradigm
Journal of Chemical Physics, 2020, 152(2).
10.1063/5.0012901 - Vyas, R.; Dice, B.D.; Harper, E.S.; Spellings, M.P.; Anderson, J.A.; Glotzer, S.C.
freud: A Software Suite for High Throughput Analysis of Particle Simulation Data
Computer Physics Communications 2020, 254
10.1016/j.cpc.2020.107275 - Lim, N.M.; Osato, M.; Warren, G. L.; Mobley, D. L.
Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations
Journal of Chemical Theory and Computation 2020, 16(4), 2778-2794
10.1021/acs.jctc.9b01096 - Riera, M.; Yeh, E.P.; Paesani, F.
Data-Driven Many-Body Models for Molecular Fluids: CO2/H2O Mixtures as a Case Study
Journal of Chemical Theory and Computation 2020, 16(4), 2246-2257
10.1021/acs.jctc.9b01175 - Sidky, H.; Chen, W.; & Ferguson, A.L.
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Molecular Physics 2020, 18(5)
10.1080/00268976.2020.1737742 - Mostafanejad, M.; Liebenthal, M.D.; DePrince, A. E.
Global Hybrid Multiconfiguration Pair-Density Functional Theory.
Journal of Chemical Theory and Computation 2020,16(4), 2274-2283
10.1021/acs.jctc.9b01178 - Smith, D.G.A.; Altarawy, D.; Burns, L.A.; Welborn, M.; Naden, L.; Ward, L.; Ellis, S.J.; Crawford, T.D.
The MolSSI QCArchive Project: An open-source platform to compute, organize, and share quantum chemistry data.
ChemRvix 2020
https://chemrxiv.org/s/22566e14d96e43f7611a - Haghighatlari, M.; Vishwakarma, G.; Altarawy, D.; Subramanian, R.; Kota, B. U.; Sonpal, A.; Setlur, S.; Hachmann, J.
ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
WIREs Computational Molecular Science 2020, e1458
10.1002/wcms.1458 - Stair, N. H.; Huang, R.; Evangelista, F. A.
A Multireference Quantum Krylov Algorithm for Strongly Correlated Electrons
Journal of Chemical Theory and Computation 2020,16(4), 2236-2245
10.1021/acs.jctc.9b01125 - Sezginel, K. B.; Wilmer, C. E.
Modeling diffusion of nanocars on a Cu (110) surface
Molecular Systems Design & Engineering 2020, XXX, XXX
10.1039/c9me00171a - Mullinax, J.W.; Maradzike, E.; Koulias, L.N.; Mostafanejad, M.; Epifanovsky, E.; Gidofalvi, G.; DePrince, A. E.
Heterogeneous CPU + GPU Algorim for Variational Two-Electron Reduced-Density Matrix-Driven Complete Active-Space Self-Consistent Ffield Theory.
Journal of Chemical Theory and Computation 2019, 15(11) 6164-6178
10.1021/acs.jctc.9b00768
- Deustua, J.E.; Yuwono, S.H.; Shen, J.; & Piecuch, P.
Accurate excited-state energetics by a combination of Monte Carlo sampling and equation-of-motion coupled-cluster computations
Journal of Chemical Physics, 2019, 150, 11101
10.1063/1.5090346 - Mostafanejad, M.; Haney, J.; DePrince, A. E.
Kinetic-energy-based error quantification in Kohn–Sham density functional theory
Physical Chemistry Chemical Physics 2019, 21(48), 26492-26501
10.1039/c9cp04595c - Afzal, M. A. F.; Sonpal, A.; Haghighatlari, M.; Schultz, A. J.; Hachmann, J.
A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules
Chemical Science 2019, 10, 8374-8383
10.1039/c9sc02677k - Riera, M.; Lambros, E.; Nguyen, T. T.; Götz, A. W.; Paesani, F.
Low-order many-body interactions determine the local structure of liquid water
Chemical Science 2019, 10, 8211-8218
10.1039/c9sc03291f - Sidky, H.; Chen, W.; Ferguson, A. L.
High-Resolution Markov State Models for the Dynamics of Trp-Cage Miniprotein Constructed Over Slow Folding Modes Identified by State-Free Reversible VAMPnets
The Journal of Physical Chemistry B 2019, 123, 7999-8009
10.1021/acs.jpcb.9b05578 - Tazhigulov, R. N.; Gayvert, J. R.; Wei, M.; Bravaya, K. B.
eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins
The Journal of Physical Chemistry B 2019, 123, 6946-6951
10.1021/acs.jpcb.9b04816 - Chen, W.; Sidky, H.; Ferguson, A. L.
Capabilities and limitations of time-lagged autoencoders for slow mode discovery in dynamical systems
The Journal of Chemical Physics 2019, 151, 064123
10.1063/1.5112048 - Zhang, B.; Altarawy, D.; Barnes, T.; Turney, J. M.; Schaefer, H. F.
Janus: An Extensible Open-Source Software Package for Adaptive QM/MM Methods
Journal of Chemical Theory and Computation 2019, 15, 4362-4373
10.1021/acs.jctc.9b00182 - Abbott, A. S.; Turney, J. M.; Zhang, B.; Smith, D. G. A.; Altarawy, D.; Schaefer, H. F.
PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces
Journal of Chemical Theory and Computation 2019, 15, 4386-4398
10.1021/acs.jctc.9b00312 - Takeshita, T. Y.; Dou, W.; Smith, D. G. A.; de Jong, W. A.; Baer, R.; Neuhauser, D.; Rabani, E.
Stochastic resolution of identity second-order Matsubara Green’s function theory
The Journal of Chemical Physics 2019, 151, 044114
10.1063/1.5108840 - Carleo, G.; Choo, K.; Hofmann, D.; Smith, J. E.; Westerhout, T.; Alet, F.; Davis, E. J.; Efthymiou, S.; Glasser, I.; Lin, S.; Mauri, M.; Mazzola, G.; Mendl, C. B.; van Nieuwenburg, E.; O’Reilly, O.; Théveniaut, H.; Torlai, G.; Vicentini, F.; Wietek, A.
NetKet: A machine learning toolkit for many-body quantum systems
SoftwareX 2019, 10, 100311
10.1016/j.softx.2019.100311 - Chen, W.; Sidky, H.; Ferguson, A. L.
Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
The Journal of Chemical Physics 2019, 150, 214114
10.1063/1.5092521 - Lebold, K. M.; Noid, W. G.
Dual approach for effective potentials that accurately model structure and energetics
The Journal of Chemical Physics 2019, 150, 234107
10.1063/1.5094330 - Hays, J. M.; Cafiso, D. S.; Kasson, P. M.
Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data
The Journal of Physical Chemistry Letters 2019, 10, 3410-3414
10.1021/acs.jpclett.9b01407 - Afzal, M. A. F.; Haghighatlari, M.; Ganesh, S. P.; Cheng, C.; Hachmann, J.
Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
The Journal of Physical Chemistry C 2019, 123, 14610-14618
10.1021/acs.jpcc.9b01147 - Sun, S.; Williams-Young, D.; Li, X.
An ab Initio Linear Response Method for Computing Magnetic Circular Dichroism Spectra with Nonperturbative Treatment of Magnetic Field
Journal of Chemical Theory and Computation 2019, 15, 3162-3169
10.1021/acs.jctc.9b00095 - Vu, O.; Mendenhall, J.; Altarawy, D.; Meiler, J.
BCL::Mol2D—a robust atom environment descriptor for QSAR modeling and lead optimization
Journal of Computer-Aided Molecular Design 2019, 33, 477-486
10.1007/s10822-019-00199-8 - Bajaj, P.; Riera, M.; Lin, J. K.; Mendoza Montijo, Y. E.; Gazca, J.; Paesani, F.
Halide Ion Microhydration: Structure, Energetics, and Spectroscopy of Small Halide–Water Clusters
The Journal of Physical Chemistry A 2019, 123, 2843-2852
10.1021/acs.jpca.9b00816 - Nocito, D.; Beran, G. J. O.
Reduced computational cost of polarizable force fields by a modification of the always stable predictor-corrector
The Journal of Chemical Physics 2019, 150, 151103
10.1063/1.5092133 - Haghighatlari, M.; Hachmann, J.
Advances of machine learning in molecular modeling and simulation
Current Opinion in Chemical Engineering 2019, 23, 51-57
10.1016/j.coche.2019.02.009 - Richard, R. M.; Bertoni, C.; Boschen, J. S.; Keipert, K.; Pritchard, B.; Valeev, E. F.; Harrison, R. J.; de Jong, W. A.; Windus, T. L.
Developing a Computational Chemistry Framework for the Exascale Era
Computing in Science & Engineering 2019, 21, 48-58
10.1109/mcse.2018.2884921 - Zhuang, D.; Riera, M.; Schenter, G. K.; Fulton, J. L.; Paesani, F.
Many-Body Effects Determine the Local Hydration Structure of Cs+in Solution
The Journal of Physical Chemistry Letters 2019, 10, 406-412
10.1021/acs.jpclett.8b03829 - Pritchard, B. P.; Altarawy, D.; Didier, B.; Gibson, T. D.; Windus, T. L.
New Basis Set Exchange: An Open, Up-to-Date Resource for the Molecular Sciences Community
Journal of Chemical Information and Modeling 2019, 59, 4814-4820
10.1021/acs.jcim.9b00725 - Kodrycka, M.; Holzer, C.; Klopper, W.; Patkowski, K.
Explicitly Correlated Dispersion and Exchange Dispersion Energies in Symmetry-Adapted Perturbation Theory
Journal of Chemical Theory and Computation 2019, 15, 5965-5986
10.1021/acs.jctc.9b00547 - Lebold, K. M.; Noid, W. G.
Dual-potential approach for coarse-grained implicit solvent models with accurate, internally consistent energetics and predictive transferability
The Journal of Chemical Physics 2019, 151, 164113
10.1063/1.5125246 - Provazza, J.; Coker, D. F.
Multi-level description of the vibronic dynamics of open quantum systems
The Journal of Chemical Physics 2019, 151, 154114
10.1063/1.5120253 - Dick, S.; Fernandez-Serra, M.
Learning from the density to correct total energy and forces in first principle simulations
The Journal of Chemical Physics 2019, 151, 144102
10.1063/1.5114618 - Sun, S.; Williams-Young, D. B.; Stetina, T. F.; Li, X.
Generalized Hartree–Fock with Nonperturbative Treatment of Strong Magnetic Fields: Application to Molecular Spin Phase Transitions
Journal of Chemical Theory and Computation 2019, 15, 348-356
10.1021/acs.jctc.8b01140 - Zanette, C.; Bannan, C. C.; Bayly, C. I.; Fass, J.; Gilson, M. K.; Shirts, M. R.; Chodera, J. D.; Mobley, D. L.
Toward Learned Chemical Perception of Force Field Typing Rules
Journal of Chemical Theory and Computation 2019, 15, 402-423
10.1021/acs.jctc.8b00821 - Lebold, K. M.; Noid, W. G.
Systematic study of temperature and density variations in effective potentials for coarse-grained models of molecular liquids
The Journal of Chemical Physics 2019, 150, 014104
10.1063/1.5050509 - Paesani, F.; Bajaj, P.; Riera, M.
Chemical accuracy in modeling halide ion hydration from many-body representations
Advances in Physics: X 2019, 4, 1631212
10.1080/23746149.2019.1631212 - Sunseri, J.; King, J. E.; Francoeur, P. G.; Koes, D. R.
Convolutional neural network scoring and minimization in the D3R 2017 community challenge
Journal of Computer-Aided Molecular Design 2019, 33, 19-34
10.1007/s10822-018-0133-y - Riera, M.; Brown, S. E.; Paesani, F.
Isomeric Equilibria, Nuclear Quantum Effects, and Vibrational Spectra of M+(H2O)n=1–3 Clusters, with M = Li, Na, K, Rb, and Cs, through Many-Body Representations
The Journal of Physical Chemistry A 2018, 122, 5811-5821
10.1021/acs.jpca.8b04106 - Smith, D. G. A.; Burns, L. A.; Sirianni, D. A.; Nascimento, D. R.; Kumar, A.; James, A. M.; Schriber, J. B.; Zhang, T.; Zhang, B.; Abbott, A. S.; Berquist, E. J.; Lechner, M. H.; Cunha, L. A.; Heide, A. G.; Waldrop, J. M.; Takeshita, T. Y.; Alenaizan, A.; Neuhauser, D.; King, R. A.; Simmonett, A. C.; Turney, J. M.; Schaefer, H. F.; Evangelista, F. A.; DePrince, A. E.; Crawford, T. D.; Patkowski, K.; Sherrill, C. D.
Psi4NumPy: An Interactive Quantum Chemistry Programming Environment for Reference Implementations and Rapid Development
Journal of Chemical Theory and Computation 2018, 14, 3504-3511
10.1021/acs.jctc.8b00286 - Provazza, J.; Coker, D. F.
Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy
The Journal of Chemical Physics 2018, 148, 181102
10.1063/1.5031788 - Avery, P.; Ludowieg, H.; Autschbach, J.; Zurek, E.
Extended Hückel Calculations on Solids Using the Avogadro Molecular Editor and Visualizer
Journal of Chemical Education 2018, 95, 331-337
10.1021/acs.jchemed.7b00698 - Irrgang, M. E.; Hays, J. M.; Kasson, P. M.
gmxapi: A high-level interface for advanced control and extension of molecular dynamics simulations
Bioinformatics 2018, 34, 3945-3947
10.1093/bioinformatics/bty484 - Krylov, A.; Windus, T. L.; Barnes, T.; Marin-Rimoldi, E.; Nash, J. A.; Pritchard, B.; Smith, D. G. A.; Altarawy, D.; Saxe, P.; Clementi, C.; Crawford, T. D.; Harrison, R. J.; Jha, S.; Pande, V. S.; Head-Gordon, T.
Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science
The Journal of Chemical Physics 2018, 149, 180901
10.1063/1.5052551 - Bannan, C. C.; Mobley, D. L.; Skillman, A. G.
SAMPL6 challenge results from $$pK_a$$ p K a predictions based on a general Gaussian process model
Journal of Computer-Aided Molecular Design 2018, 32, 1165-1177
10.1007/s10822-018-0169-z
