Publications from The MolSSI
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!
- Naden, L.N.; Nash, J.A.; Crawford, T.D.; & Ringer McDonald, A.
Cookiecutter for Computational Molecular Sciences: A Best Practices Ready Python Project Generator
Journal of Chemical Education, 2024, 101(11), 5105-5109
10.1021/acs.jchemed.4c00793 - Windom, Z.A.; Perera, A.; & Barlett, R.J.
An “ultimate” coupled cluster method based entirely on T2
J. Chem Phys., 2024, 161, 184106
10.1063/5.0228453 - Manderna, R.; Vu, N.; Foley IV, J.J.
Comparing parameterized and self-consistent approaches to ab initio cavity quantum electrodynamics for electronic strong coupling
The Journal of Chemical Physics, 2024, 161(17), 174105
10.1063/5.0230565 - Vizcarra, C.L.; Trainor, R.F.; Ringer McDonald, A.; Richardson, C.T.; Potoyan, D.; Nash, J.A.; Lundgren, B.; Luchko, T.; Hocky, G.M.; Foley IV, J.J.; Atherton, T.L. & Stokes, G.Y.
An interdisciplinary effort to integrate coding into science courses
Nature Computational Sciences, 2024
https://doi.org/10.1038/s43588-024-00708-2 - Mostafanejad, M.
Unification of Popular Artificial Neural Network Activation Functions
Fractional Calculus and Applied Analysis, 2024
https://doi.org/10.1007/s13540-024-00347-4 - L.A. Burns; C.D. Sherrill; & P.B. Pritchard
QCManyBody: A Flexible Implementation of the Many-Body Expansion
The Journal of Chemical Physics, 2024, 161(15), 2501
https://doi.org/10.1063/5.0231843 - P. Eastman; B.P. Pritchard; J.D. Chodera; & T.E. Markland
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Journal of Chemical Theory and Computation, 2024, 20(19), 8583-8593.
https://doi.org/10.1021/acs.jctc.4c00794 - Hemmati, R.; Mostafanejad, M.; & Ortiz, J.V.
Numerical analysis of the complete active-space extended Koopmans’s theorem
The Journal of Chemical Physics, 2024, 161, 094101
https://doi.org/10.1063/5.0226057 - Thorpe, J.H.; Windom, Z. W.; Bartlett, R.J.; & Matthews, D.A.
Factorized Quadruples and a Predictor of Higher-Level Correlation in Thermochemistry
The Journal of Physical Chemistry A, 2024, 128(36), 7720-7732.
https://doi.org/10.1021/acs.jpca.4c04460 - Jiang, A.; Glick Z.L.; Poole, D.; Turney, J.M.; Sherrill, D.C.; & Schaefer, III, H.F.
Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian
The Journal of Chemical Physics, 2024, 161, 082502
https://doi.org/10.1063/5.0219963 - Windom, Z.W.; Claudino, D.; & Bartlett, R.J.
An Attractive Way to Correct for Missing Singles Excitations in Unitary Coupled Cluster Doubles Theory
Journal of Physical Chemistry A, 2024, 128(33), 7036-7045
https://doi.org/10.1021/acs.jpca.4c03935 - Miller, E.R.; Hoehn, S.J.; Kumar, A.; Jiang, D.; & Parker, S.M.
Ultrafast photochemistry and electron diffraction for cyclobutanone in the S2 state: Surface hopping with time-dependent density functional theory
The Journal of Chemical Physics, 2024, 161 (3), 034105
https://doi.org/10.1063/5.0203679 - Fuchs, W.; Ringer McDonald, A.; Guatam, A.; & Kazerouni, A.M.
Recommendations for Improving End-User Programming Education: A Case Study with Undergraduate Chemistry Students
Journal of Chemical Education, 2024, 10(8), 3085-3096
https://doi.org/10.1021/acs.jchemed.4c00219 - Eastman, P.; Pritchard, B.P.; Chodera, J.D.; & Markland, T.E.
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Chem arXiv: 2024, 2406:13112
https://doi.org/10.48550/arXiv.2406.13112 - Barnes, T.A.; Ellis, S.; Chen, J.; Plimpton, S.J.; Nash, J.A.
Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project
The Journal of Chemical Physics, 2024, 160, 214114
https://doi.org/10.1063/5.0214279 - Windom, Z.W.; Claudino, D.; Bartlett, R.J.
A new “gold standard”: Perturbative triples corrections in unitary coupled cluster theory and prospects for quantum computing
The Journal of Chemical Physics, 2024, 160, 214113
https://doi.org/10.1063/5.0202567 - Rios-Vargas, V.; Shao., X,; Trickey, S.B.; Pavanello, M.
Effective Wang-Teter kernels for improved orbital-free density functional theory simulations
Physical Review B, 2024, 110, 085129
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.110.085129 - Burgin, T.; Pollard, B.C.; Knott, B.C.; Mayes, H.B.; Crowley, M.F.; McGeehan, J.E.; Beckham, G.T.; & Woodcock. H.L.
The reaction mechanism of the Ideonella sakaeinsis PETase enzyme
Communications Chemistry, 2024, 7(65).
10.1038/s42004-024-01154-x - Lotthammer, J.M.; Ginell, G.M.; Griffith, D.; Emenecker, R.J. & Holehouse, A.S.
Direct prediction of intrinsically disordered protein conformational properties from sequences
Nature Methods, 2024
10.1038/s41592-023-02159-5 - Zhang, T.; Banerjee, S.; Koulias, L.N.; Valeev, E.F.; DePrince III, A.E.; & Li, X.
Dirac–Coulomb–Breit Molecular Mean-Field Exact-Two-Component Relativistic Equation-of-Motion Coupled-Cluster Theory
The Journal of Physical Chemistry A., 2024, 128(17), 3408-3418
https://doi.org/10.1021/acs.jpca.3c08167 - Pavlicek, A.; Windom, Z.A.; Perera, A.; & Bartlett, R.J.
A comparison of QTP functionals against coupled-cluster methods for EAs of small organic molecules
J. Chem Phys., 2024, 160, 014106
jcp/article/160/1/014106/2932469 - Zhao, C.; Kleiman, D.E.; Shukla, D.
Resolving binding pathways and solvation thermodynamics of plant hormone receptors
Journal of Biological Chemistry, 2023, 299(12), 105456
10.1016/j.jbc.2023.105456 - Kleiman, D.E.; Nadeem, H.; & Shukla, D.
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
J. Phys. Chem. B, 2023, 127, 50, 10669-10681
10.1021/acs.jpcb.3c04843 - Pretti, E., & Shell, M.S.
Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6
Proceedings of the National Academy of Sciences, 2023, 120(48).
10.1073/pnas.2309995120 - Jones, M.S.; Shmilovich, K.; & Ferguson, A.L.
DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
J. Chem. Theory Comput, 2023, 19, 21, 7908-7923
10.1021/acs.jctc.3c00840 - Peyton, B.G.; Wang, Z.; & Crawford, T.D.
Reduced Scaling Real-Time Coupled Cluster Theory
J. Phys. Chem. A, 2023, 127, 40, 8486-8499.
10.1021/acs.jpca.3c05151 - Lehtola, S., & Marques, M.A.L.
Reproducibility of density functional approximations: How new functionals should be reported
The Journal of Chemical Physics, 2023, 114116
10.1063/5.0167763 - Ojha, A.A., Votapka, L.W.; & Amaro. R.
QMrebind: Incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations
Chemical Science, 2023
10.1039/D3SC04195F - Li Manni, G. . . . Scott, T.R. . . . Lehtola. S. . . . Lindh, R.
The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry
J. Chem. Theory Comput., 2023, 19, 20, 6933-6991.
10.1021/acs.jctc.3c00182 - Riera, M., Knight, C.; Bull-Vulpe, E.F.; Zhu, X; Agnew, H.; Smith, D.G.A.; Simmonett, A.C.; Paesani F.
MBX: A many-body energy and force calculator for data-driven many-body simulations
The Journal of Chemical Physics, 2023, 159(5), 054802
10.1063/5.0156036 - Lehtola, S. & Marques, M.A.L.
Reproducibility of density functional approximations: How new functionals should be reported
Chem arXiv: 2307.07474, 2023
10.48550/arXiv.2307.07474 - I. Quintela Matos & F.A. Escobedo
Self-Assembling of Nonadditive Mixtures Containing Patchy Particles with Tunable Interactions
J. Phys. Chem B., 2023, 127(41), 8982-8992
10.1021/acs.jpcb.3c05302 - Lalmansingh, J.M., Keeley, A.T., Ruff, K. M.; Pappu, R.V., & Holehouse, A.S.
SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins
J. Chem. Theory Comput. 2023, 19(16), 5609-5620
10.1021/acs.jctc.3c00190 - Lehtola, S.
Automatic Generation of Accurate and Cost-efficient Auxiliary Basis Sets
Chem arXiv:2306.11039, 2023
arxiv.org/abs/2306.11039 - Banerjee, S., & Sokolov, A. Y.
Algebraic Diagrammatic Construction Theory for Simulating Charged Excited States and Photoelectron Spectra
J. Chem. Theory Comput., 2023, 19, 11, 3037-3053
10.1021/acs.jctc.3c00251 - Windom, Z.A. & Bartlett, R.J.
On the iterative diagonalization of matrices in quantum chemistry: Reconciling preconditioner design with Brillouin–Wigner perturbation theory
Journal of Chemical Physics, 2023, 158, 134107
10.1063/5.0139295 - Moussa, J.E.
Model selection in atomistic simulation
The Journal of Chemical Physics, 2023, 158, 13, 134103
10.1063/5.0142248 - Lehtola, S.
Meta-GGA Density Functional Calculations on Atoms with Spherically Symmetric Densities in the Finite Element Formalism
J. Chem. Theory Comput, 2023, 19, 9, 2502-2517
10.1021/acs.jctc.3c00183 - Shmilovich, K. & Ferguson A.L.
Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables
J. Phys. Chem. A, 2023, 127, 15, 3497-3517
10.1021/acs.jpca.3c00505 - Rappoport, D.; Bekoe, S.; Mohanam, L.N.; Le, S.; George, N.; & Furche, F.
Libkrylov, a Modular Open-Source Software Library for Extremely Large On-the-Fly Matrix Computations
Journal of Computational Chemistry, 2023, 44(11), 1105-1118
10.1002/jcc.27068 - Pitman, M.; Hahn, D.F.; Tresadern, G.; & Mobley, D.L.
To Design Scalable Free Energy Perturbation Networks, Optimal Is not Enough
J. Chem. Inf. Model, 2023, 63(6), 1776-1793
10.1021/acs.jcim.2c01579 - Hsu, W.-T.; Piomponi, V.; Merz, P.T., Bussi, G.; & Shirts, M.R.
Alchemical Metadynamics: Adding Alchemical Variables to Metadynamics to Enhance Sampling in Free Energy Calculations
J. Chem. Theory Comput, 2023, 19(6), 1805–1817
10.1021/acs.jctc.2c01258 - Ojha, A.A.; Thakur, S.; & Amaro, R. E.
DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling
J. Chem. Theory Comput. 2023, 19, 4, 1342-1359.
10.1021/acs.jctc.2c00282 - Burgin, T.; Ellis, S.; & Mayes, H.B.
ATESA: An Automated Aimless Shooting Workflow
J. Chem. Theory Comput., 2023, 19, 1, 235–244
10.1021/acs.jctc.2c00543 - Eastman, P.; Behara, P.K.; Dodson, D.L.; Galvelis, R.; Herr, J.E.; Horton, J.T; Mao, Y.; Chodera, J.D.; Pritchard, B.P.; Wang, Y.; De Fabritiis, G.; & Markland, T.E.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Scientific Data, 2023, Volume. 10, Article #11
s41597-022-01882-6 - Schwalbe, S.; Trepte, K.; & Lehtola, S.
How Good are Recent Density Functionals for Ground and Excited States of One-Electron Systems?
J. Chem. Phys., 2022, 157, 17411.
10.1063/5.0120515 - Banerjee, S.; Fortunato, M.T.; Xue, C.; Huang, J.; Sokolov, A.Y.; & Turro, C.
Electrochemical Strategy for Proton Relay Installation Enhances the Activity of a Hydrogen Evolution Electrocatalyst
J. Am. Chem. Soc., 2022, 144, 44, 20267–20277.
10.1021/jacs.2c06011 - Gaidai, I.; Babikov, D.; Teplukhin A.; Kendrick, B.K.; Mniszewski, S.M.; Zhang, Y.; Tretiak, S.; & Dub, P.A.
Molecular dynamics on quantum annealers
Scientific Reports, 2022, 12, Article 16824
www.nature.com/articles/s41598-022-21163-x#citeas - Lehtola, S.; & Marques, M.A.L.
Many recent density functionals are numerically ill-behaved
J. Chem Phys, 2022, 157, 174114
10.1063/5.0121187 - McDonald, A.R.; Nash, J.A.; Nerenberg, P.S.; Ball, K.A.; Sode, O.; Foley IV, J.J.; Windus, T.L.; Crawford, T.D.
Building Capacity for Undergraduate Education and Training in Computational Molecular Science: A Collaboration between the MERCURY Consortium and the Molecular Sciences Software Institute
Quantum Chemistry, 2022, 120, 20, e26359
10.1002/qua.26359 - Craig, P.A.; Nash, J.A.; & Crawford, T.D.
Python Scripting for Biochemistry and Molecular Biology in Jupyter Notebooks
Biochemistry and Molecular Biology Education, 2022, 50, 5, 479-482.
10.1002/bmb.21676
- Westheimer, B. M. & Gordon M.S.
General, Rigorous Approach for the Treatment of Interfragment Covalent Bonds
The Journal of Physical Chemistry A, 2022, 126, 39, 6995-7006
10.1021/acs.jpca.2c04015 - Gibson, J.; Hire, A.; & Hennig, R.G.
Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures
npj Computational Materials, 2022, Article number 211
www.nature.com/articles/s41524-022-00891-8 - Smith, James E.T.; Lee, J.; & Sharma, S.
Near-exact nuclear gradients of complete active space self-consistent field wave functions
Journal of Chemical Physics, 2022, 157, 094104
10.1063/5.0085515 - Stahl, T.L.; Banerjee, S.; & Sokolov, A. Y.
Quantifying and reducing spin contamination in algebraic diagrammatic construction theory of charged excitations
Journal of Chemical Physics, 2022, 157, 044106
10.1063/5.0097333 - Banerjee, S. & Sokolov, A.
Non-Dyson Algebraic Diagrammatic Construction Theory for Charged Excitations in Solids
J. Chem. Theory Comput, 2022, 18, 9
10.1021/acs.jctc.2c00565 - Stair, N.H. & Evengelista, A.
QForte: An Efficient State-Vector Emulator and Quantum Algorithms Library for Molecular Electronic Structure
J. Chem. Theory Comput. 2022, 18, 3
10.1021/acs.jctc.1c01155 - Duan, C.; Nandy A.; Adamji, H.; Roman-Leshkov, Y.; & Kulik, H.
Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis
J. Chem. Theory Comput. 2022, 18, 7, 4282–4292.
10.1021/acs.jctc.2c00331 - Schwalbe, S.; Trepte, K.; & Lehtola, S.
How Good are Recent Density Functionals for Ground and Excited States of One-Electron Systems?
Chem arXiv:2208.06482, 2022.
arxiv.org/abs/2208.06482 - Teale A, Helgaker T, Savin A, Adamo C, Aradi B, Arbuznikov A, Crawford, T.D., et al. (70 authors)
DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science
Phys. Chem. Chem. Phys., 2022
pubs.rsc.org/en/content/articlelanding/2022/cp/d2cp02827a - Irrgang, M.E.; Davis, C.; & Kasson, P.M.
gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support
PLoS Comput Biol, 2022, 18(2) Feb.
ncbi.nlm.nih.gov/pmc/articles/PMC8880871/ - Duan, C.; Chu, D.B.K.; Nandy A.; Adamji, H.; & Kulik, H.
Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
Chemical Science, 2022, 17.
rsc.org/en/content/articlelanding/2022/SC/D2SC00393G - Duan, C.; Nandy, A.; Adamji, H. Roman-Leshkov, Y. & Kulik, H.
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Chem arXiv, 2022
arxiv.org/abs/2203.01276 - Pathak, S.; Rackers, J.A.; Lopez, I.E.; Fernandez, R.L.; Lee, A.J.; Bricker, W.P.; & Lehtola, S.
Accurate Hellmann-FeynmanForces with Optimized Aton-Centered Gaussian Basis Sets
Chem arXiv, 2022
arxiv.org/abs/2207.03587 - Lehtola, S. & Karttunen, A.J.
Free and open source software for computational chemistry education
WIREs Computational Molecular Science, 2022
doi/10.1002/wcms.1610 - Lehtola, S.; & Marques, M.A.L.
Many Recent Density Functionals are Numerically Ill-Behaved
Chem arXiv, 2022
arxiv.org/abs/2206.14062 - Ringer McDonald, A.; Roberts, R.; Koeppe, J.R.; & Hall, B.L.
Undergraduate Structural Biology Education: A shift from Users to Developers of Computation and Simulation Tools
Current Opinion in Structural Biology, 2022, 72, 39-45
10.1016/j.sbi.2021.07.012 - Nash, J.A.; Mostafanejad, M.; Crawford, T.D.; & Ringer McDonald, A.
MolSSI Education: Empowering the Next Generation of Computational Molecular Scientists
Computing in Science and Engineering, 2022, April.
10.22541/au.164910777.74730983/v1 - Lehtola, S.
Straightforward and Accurate Automatic Auxiliary Basis Set Generation for Molecular Calculations with Atomic Orbital Basis Sets
J. Chem. Theory Comput. 2021, 17, 11
10.1021/acs.jctc.1c00607 - Kidder, K.M.; Szukalo, R.J.; & Noid, W.G. (with contributions from Lebold K.)
Energetic and entropic considerations for coarse-graining
The European Physical Journal B, 2021, 94(153).
10.1140/epjb/s10051-021-00153-4 - Smith, D.G.A., Lolinco, A.T. . . . Burns, L.A.
Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs
Journal of Chemical Physics, 2021, 155, 20
10.1063/5.0059356 - Westheimer, M.B. and Gordon, M.S.
Scalable ab initio fragmentation methods based on a truncated expansion of the non-orthogonal molecular orbital model
Journal of Chemical Physics, 2021, 155, 15
10.1063/5.0064864 - Gururangan, K.; Deustua, J. E.; Shen, J.; & Piecuch, P.
High-level coupled-cluster energetics by merging moment expansions with selected configuration interaction
Journal of Chemical Physics, 2021, 155(17), 174114
10.1063/5.0064400?journalCode=jcp - Sarka, Janos; & Poirier, B.
Hitting the Trifecta: How to Simultaneously Push the Limits of Schrödinger Solution with Respect to System Size, Convergence Accuracy, and Number of Computed States
J. Chem. Theory Comput. 2021, 17, 12, 7732–7744
10.1021/acs.jctc.1c00824 - Qiu, Y.; Smith, D.G.A.; Boothroyd, S.; Hang, J.; Hahn, D.F.; Wagner, J.; Bannan, C.C.; Gokey, T.; Lim, V.T.; Stern, C.D.; Rizzi, A.; Tjanaka, B.; Tresaden, G.; Lucase, X.; Shirts, M.R.; Gilson, M.K.; Chodera, J.D.; Bayly, C.I.; Mobley, D.L.; & Wang, L.P.
Development and Benchmarking of Open Force Field v1.0.0—the Parsley Small-Molecule Force Field
J. Chem, Theory Comput. 2021, 17, 10 6262-6280
10.1021/acs.jctc.1c00571 - Dick, S.; & Fernandez-Serra, M.
Highly Accurate and Constrained Density Functional Obtained with Differentiable Programming
Physical Review B, 2021 104, L161109
10.1103/PhysRevB.104.L161109 - Duan, C.; Chen, S.; Taylor, M.G.; Liu, F.; & Kulik, H.J.
Machine Learning to Tame Divergent Density Functional Approximations: A New Path to Consensus Materials Design Principles
Chemical Science, 2021, 12, 13021-13036
10.1039/D1SC03701C - Dana, A.G.; Wu, H.; Ranasinghe, D.S.; Pickard IV, F.C.; Wood, G.P.F.; Zelesky, T.; Sluggett, G.W.; Mustakis, J.; & Green, W.H.
Kinetic Modeling of API Oxidation: (1) The AIBN/H2O/CH3OH Radical “Soup”
Mol. Pharmaceutics, 2021, 18, 8, 3037-3049
10.1021/acs.molpharmaceut.1c00261 - Shao, X.; Mi, W.; & Pavanello
Revised Huang-Carter Nonlocal Kinetic Energy Functional for Semiconductors and Their Surfaces
Physical Review B, 2021, 104, 045118
10.1103/PhysRevB.104.045118 - Gayday, I.; Teplukhin, A.; Moussa, J.; & Babikov, D.
SpectrumSDT: A Program for Parallel Calculation of Coupled Rotational-Vibrational Energies and Lifetimes of Bound States and Scattering Resonances in Triantomic Systems
Computer Physics Communications, 2021, 267, 108084
S001046552100196X?via%3Dihub - Stair, N.H.; & Evangelista, F.A.
Simulating Many-Body Systems with a Projective Quantum Eigensolver
PRX Quantum, 2021, 2, 030301
10.1103/PRXQuantum.2.030301 - Nash, J.A. & Pritchard, B.P.
Coding, Software Engineering, and Molecular Science — Teaching a Multidisciplinary Course to Chemistry Graduate Students
From Teaching Programming across the Chemistry Curriculum, 2021, ACS Symposium Series, Vol. 1387, Chapter 11, pp. 159-171
https://pubs.acs.org/doi/full/10.1021/bk-2021-1387.ch011 - Nandy, A.; Duanb, C.; Taylor, M.G.; Liu, F.; Steeves, A.H. & Kulik, H.J.
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Chemical Reviews, 2021, 121(16), 9927-10000
10.1021/acs.chemrev.1c00347 - Magoulas, I,; Gururangan, K.; Piecuch, P.; Deustua, J. E.; & Shen, J.
Is Externally Corrected Coupled Cluster Always Better Than the Underlying Truncated Configuration Interaction?
J. Chem. Theory Comput., 2021, 17(7), 4006-4027.
10.1021/acs.jctc.1c00181 - Yao, Y. & Umrigar, C.J.
Orbital Optimization in Selected Configuration Interaction Methods
J. Chem. Theory Comput., 2021, 17(7), 4183-4194.
10.1021/acs.jctc.1c00385 - 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 - Clark, A.E.; Adams, H.; Hernandez, R.; Krylov, A.; Niklasson, A.M.N.; Sarupria, S.; Wang, Y.; Wild, S.M.; Yang, Q.
The Middle Science: Traversing Scle in Complex Many-Body Systems
ACS Centr. Sci. 2021, 7, 8, 1271-1287
10.1021/acscentsci.1c00685 - Yao, Y.; Giner, E.; Anderson, T.A.; Tolouse, J.; & Umrigar
Accurate energies of transition metal atoms, ions, and monoxides using selected configuration interaction and density-based basis-set correction
The Journal of Chemical Physics, 2021, 155, 20414
10.1063/5.0072296 - Kidder, K.M.; Szukalo, R.J. & Noid, W.G.
Energetic and Entropic Considerations for Coarse-Graining
European Physical Journal B, 2021, 94,7
10.1140/epjb/s10051-021-00153-4 - 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 - Babikov, D.; Grushnikova E.; Gayday, I.; & Teplukhin, A.
Four Isotope-Labeled Recombination Pathways of Ozone Formation
Molecules, 2021, 26, 5
10.3390/molecules26051289 - 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 - McDonald, A.R.; Nash, J.A.; Nerenberg, P.S.; Ball, K.A.; Sode, O.; Foley IV, J.J.; Windus, T.L.; Crawford, T.D.
Building Capacity for Undergraduate Education and Training in Computational Molecular Science: A Collaboration between the MERCURY Consortium and the Molecular Sciences Software Institute
Quantum Chemistry, 2022, 120, 20, e26359
10.1002/qua.26359 - 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.
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Generalized Hartree–Fock with Nonperturbative Treatment of Strong Magnetic Fields: Application to Molecular Spin Phase Transitions
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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
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Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy
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Extended Hückel Calculations on Solids Using the Avogadro Molecular Editor and Visualizer
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Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science
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SAMPL6 challenge results from $$pK_a$$ p K a predictions based on a general Gaussian process model
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