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!

  • 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
    ChemRxiv, 2022
    10.26434/chemrxiv-2022-hprn7
  • Lin, S.; 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, XXX, 
    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, 2002, 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 Chemistry2022120, 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 A2022126, 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 Comput2022, 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, XXXX
      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 Rxiv, 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 Rxiv, 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 Rxiv, 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 Biology2022, 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 Comput2021, 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 Physics2021, 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 Comput2021, XXXX
      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 B2021 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 Science2021, 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. Pharmaceutics2021, 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 B2021, 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
    • 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
      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 B2021, 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 Physics2021, 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 Physics2021, 154(12), 074105
      10.1063/5.0045468
    • Babikov,  D.; Grushnikova E.; Gayday, I.; & Teplukhin, A.
      Four Isotope-Labeled Recombination Pathways of Ozone Formation
      Molecules2021, 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 Communications2021, 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 Physics2021, 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 Chemistry2022120, 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.
      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.
      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 B2020, 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 Physics2020, 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 Physics202036.
      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, 2020e26472
      10.1002/qua.26472
    • Sidky, H.; Chen, W.; & Ferguson, A.L.
      Molecular Latent Space Simulators
      Chemical Science2020, 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, 20207
      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 Physics2020, 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 2020XXX, 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 Physics2019, 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 201921(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 201910, 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 201910, 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 2019123, 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 2019123, 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 2019151, 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 201915, 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 201915, 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 2019151, 044114
      10.1063/1.5108840
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