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!

  • 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
  • Lehtola, S.
    Straightforward and Accurate Automatic Auxiliary Basis Set Generation for Molecular Calculations with Atomic Orbital Basis Sets
    J. Chem. Theory Comput2021, 17, 11
  • 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 Comput2021, XXXX
  • 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
  • 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
  • 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
  • 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
    Chemical Science 201910, 8211-8218
  • 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 2019123, 7999-8009
  • 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 2019123, 6946-6951
  • 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 2019151, 064123
  • 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 201915, 4362-4373
  • 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 201915, 4386-4398
  • 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 2019151, 044114
  • 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 201910, 100311
  • Chen, W.; Sidky, H.; Ferguson, A. L.
    Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
    The Journal of Chemical Physics 2019150, 214114
  • Lebold, K. M.; Noid, W. G.
    Dual approach for effective potentials that accurately model structure and energetics
    The Journal of Chemical Physics 2019150, 234107
  • Hays, J. M.; Cafiso, D. S.; Kasson, P. M.
    Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data
    The Journal of Physical Chemistry Letters 201910, 3410-3414
  • 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 2019123, 14610-14618
  • 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 201915, 3162-3169
  • 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 201933, 477-486
  • 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 2019123, 2843-2852
  • 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 2019150, 151103
  • Haghighatlari, M.; Hachmann, J.
    Advances of machine learning in molecular modeling and simulation
    Current Opinion in Chemical Engineering 201923, 51-57
  • 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 201921, 48-58
  • 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 201910, 406-412
  • 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 201959, 4814-4820
  • 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 201915, 5965-5986
  • 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 2019151, 164113
  • Provazza, J.; Coker, D. F.
    Multi-level description of the vibronic dynamics of open quantum systems
    The Journal of Chemical Physics 2019151, 154114
  • Dick, S.; Fernandez-Serra, M.
    Learning from the density to correct total energy and forces in first principle simulations
    The Journal of Chemical Physics 2019151, 144102
  • 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 201915, 348-356
  • 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 201915, 402-423
  • 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 2019150, 014104
  • Paesani, F.; Bajaj, P.; Riera, M.
    Chemical accuracy in modeling halide ion hydration from many-body representations
    Advances in Physics: X 20194, 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 201933, 19-34
  • 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 2018122, 5811-5821
  • 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 201814, 3504-3511
  • Provazza, J.; Coker, D. F.
    Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy
    The Journal of Chemical Physics 2018148, 181102
  • Avery, P.; Ludowieg, H.; Autschbach, J.; Zurek, E.
    Extended Hückel Calculations on Solids Using the Avogadro Molecular Editor and Visualizer
    Journal of Chemical Education 201895, 331-337
  • Irrgang, M. E.Hays, J. M.; Kasson, P. M.
    gmxapi: A high-level interface for advanced control and extension of molecular dynamics simulations
    Bioinformatics 201834, 3945-3947
  • 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 2018149, 180901
  • 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 201832, 1165-1177