Publications From 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!

  • 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).
  • 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
  • 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
  • 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
  • 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
  • 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). 
  • 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
  • Peyton, B.G.; Wang, Z.; & Crawford, T.D.
    Reduced Scaling Real-Time Coupled Cluster Theory
    J. Phys. Chem. A, 2023, 127, 40, 8486-8499.
  • Lehtola, S., & Marques, M.A.L.
    Reproducibility of density functional approximations: How new functionals should be reported
    The Journal of Chemical Physics, 2023, 114116 
  • 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
  • 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.
  • 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
  • Lehtola, S. & Marques, M.A.L. 
    Reproducibility of density functional approximations: How new functionals should be reported
    Chem ArXiv: 2307.07474, 2023
  • 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
  • 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
  • Lehtola, S. 
    Automatic Generation of Accurate and Cost-efficient Auxiliary Basis Sets
    Chem ArXiv:2306.11039, 2023
  • 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
  • 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
  • Moussa, J.E.
    Model selection in atomistic simulation
    The Journal of Chemical Physics, 2023, 158, 13, 134103
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • Burgin, T.; Ellis, S.; & Mayes, H.B.
    ATESA: An Automated Aimless Shooting Workflow
    J. Chem. Theory Comput., 2023, 19, 1, 235–244
  • 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
  • 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.
  • 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. 
  • 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
  • Lehtola, S.; & Marques,  M.A.L. 
    Many recent density functionals are numerically ill-behaved
    J. Chem Phys, 2022, 157, 174114
  • 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 Chemistry2022120, 20, e26359
  • 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.
    • Westheimer, B. M. & Gordon M.S.
      General, Rigorous Approach for the Treatment of Interfragment Covalent Bonds
      The Journal of Physical Chemistry A2022126, 39, 6995-7006
    • 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
    • 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
    • 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
    • Banerjee, S. & Sokolov, A.
      Non-Dyson Algebraic Diagrammatic Construction Theory for Charged Excitations in Solids
      J. Chem. Theory Comput, 2022, 18, 9
    • Stair, N.H. & Evengelista, A.
      QForte: An Efficient State-Vector Emulator and Quantum Algorithms Library for Molecular Electronic Structure
      J. Chem. Theory Comput2022, 18, 3
    • 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.
    • 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.
    • 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
    • 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.
    • 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.
    • 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 Rxiv, 2022
    • 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 Rxiv, 2022
    • Lehtola, S. & Karttunen, A.J.
      Free and open source software for computational chemistry education
      WIREs Computational Molecular Science, 2022
    • Lehtola, S.; & Marques, M.A.L.
      Many Recent Density Functionals are Numerically Ill-Behaved
      Chem Rxiv, 2022
    • 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 Biology2022, 72, 39-45
    • 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.
    • Lehtola, S.
      Straightforward and Accurate Automatic Auxiliary Basis Set Generation for Molecular Calculations with Atomic Orbital Basis Sets
      J. Chem. Theory Comput2021, 17, 11
    • 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).
    • 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
    • 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
    • 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 Physics2021, 155(17), 174114
    • 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
    • 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
    • Dick, S.; & Fernandez-Serra, M.
      Highly Accurate and Constrained Density Functional Obtained with Differentiable Programming
      Physical Review B2021 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 Science2021, 12, 13021-13036
    • 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. Pharmaceutics2021, 18, 8, 3037-3049
    • Shao, X.; Mi, W.; & Pavanello
      Revised Huang-Carter Nonlocal Kinetic Energy Functional for Semiconductors and Their Surfaces
      Physical Review B2021, 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
    • Stair, N.H.; & Evangelista, F.A.
      Simulating Many-Body Systems with a Projective Quantum Eigensolver
      PRX Quantum, 2021, 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
    • 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 Reviews2021, 121(16), 9927-10000
    • 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.
    • Yao, Y. & Umrigar, C.J.
      Orbital Optimization in Selected Configuration Interaction Methods
      J. Chem. Theory Comput., 2021, 17(7), 4183-4194.
    • Kodrycka, M.; & Patkowski, K.
      Efficient Density-Fitted Explicitly Correlated Dispersion and Exchange Dispersion Energies
      J. Chem. Theory Comput., 2021, 17(3), 1435-1456
    • 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
    • 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
    • Kidder, K.M.; Szukalo, R.J. & Noid, W.G.
      Energetic and Entropic Considerations for Coarse-Graining
      European Physical Journal B2021, 94,7
    • 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 Physics2021, 154(7), 124103
    • 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 Physics2021, 154(12), 074105
    • Babikov,  D.; Grushnikova E.; Gayday, I.; & Teplukhin, A.
      Four Isotope-Labeled Recombination Pathways of Ozone Formation
      Molecules2021, 26, 5
    • 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 Communications2021, 261, 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
    • 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 Physics2021, 154(6), 064108
    • 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
    • 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 Chemistry2022120, 20, e26359
    • 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.
    • 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 Physics2020, 22, 27560-27571.
    • 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 B2020, 11207-11221
    • 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.
    • 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 Physics2020, 153(12), 4117
    • 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 Physics202036.
    • 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
    • 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, 2020e26472
    • Sidky, H.; Chen, W.; & Ferguson, A.L.
      Molecular Latent Space Simulators
      Chemical Science2020, 35.
    • 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
    • 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.
    • Sezginel, K.T. & Wilmer, C.E.
      Modeling Diffusion of Nanocars on a Cu (110) Surface
      Molecular Systems Design & Engineering, 20207
    • 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.
    • Sun, Q.; Xhang, X.; Banerjee, S.; Bao, P.; Barbry, M., et al.
      Recent Developments in the PySCF Program Package
      Journal of Chemical Physics2020, 153(2)
    • Dick, S. & Fernandez-Serra, M.
      Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density
      Nature Communications, 2020, 11, 3509
    • 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
    • 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
    • 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.
    • Yao, Y.; Umrigar, J.; Elser, V.
      Chemistry of the Spin-½ Kagome Heisenberg Antiferromagnet
      Physical Review B 2020, 102.
    • 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).
    • 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
    • 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
    • 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
    • Sidky, H.; Chen, W.; & Ferguson, A.L.
      Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
      Molecular Physics 2020, 18(5)
    • 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
    • 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
    • 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
    • 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
    • Sezginel, K. B.; Wilmer, C. E.
      Modeling diffusion of nanocars on a Cu (110) surface
      Molecular Systems Design & Engineering 2020XXX, XXX
    • 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
    • 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 Physics2019, 150, 11101
    • Mostafanejad, M.; Haney, J.; DePrince, A. E.
      Kinetic-energy-based error quantification in Kohn–Sham density functional theory
      Physical Chemistry Chemical Physics 201921(48), 26492-26501
    • 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 201910, 8374-8383
    • Riera, M.; Lambros, E.; Nguyen, T. T.; Götz, A. W.; Paesani, F.
      Low-order many-body interactions determine the local structure of liquid water
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