Chemical Informatics

Through the Chemical Informatics programs, CSD researchers develop, implement, validate and apply novel and efficient informatics methodologies in support of the measurement science and metrology activities within NIST and our external stakeholders as well.  Predicting the physical and chemical properties of matter in its various states from first principles is the grand challenge of quantum chemistry.  CSD makes use of highly accurate experimental data and sound theoretical principles to provide tools and procedures to rigorously assess the quality of first principles quantum chemical methodologies as applied to a wide variety of physical and chemical properties of interest to industry and the scientific community in general.  In the area of molecular simulations, CSD researchers make use of unconventional simulation methods that challenge traditional computational models, to efficiently produce thermophysical properties of single and continuous range of state points.  CSD’s efforts in computational chemistry have enabled advances in the physics of confined fluids and adsorption science and have improved the understanding of protein solution thermodynamics and colloidal self-assembly.  In addition, CSD develops reliable and efficient machine learning algorithms that allows scientists to efficiently process and analyze complex data measured in the laboratory.  Staff also develop tools to seamlessly merge experimental data and computations in order to predict and understand the mechanisms governing chemical transformation of different molecular species under a wide range of conditions. 

PROJECT AREAS 

• Connecting Thermodynamic and Dynamic Properties of Bulk and Confined Fluids 

• Consistency Analysis and Uncertainty in ‘omic Data 

• Data Tools for Environmental Metrology 

• Density Functional Tight Binding Methods 

• Environmental Metrology Measurement Assistant (EMMA) 

• Identification, Suitability Testing, and Property Determination from Untargeted Measurements 

• Machine Learning Fluid Equations of State 

• Machine Learning to Predict Food Provenance 

• Machine Learning to Predict Multicomponent Colloidal Crystals 

• Method Assessment for Non-Targeted Analyses (MANTA) Program 

• NIST Standard Reference Simulation Website 

• Solvation Thermodynamics 

• Theoretical Mass Spectrometry 

• Thermodynamics of ATP in Solution 

• Understanding Confined Fluids using Theory, Simulation, and Data-Driven Methods 

• Understanding Protein Solution Phase Behavior via Coarse-Grained Modeling 

• Validation of Density Functional Theory for Materials 

• Virtual Measurements 

Associated Publications

1. Bobbitt, N. S., Shi, K. H., Bucior, B. J., Chen, H. Y., Tracy-Amoroso, N., Li, Z., Sun, Y. Z. S., Merlin, J. H., Siepmann, J. I., Siderius, D. W., and Snurr, R. Q., "MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials," Journal of Chemical and Engineering Data, 68, 483-498 (2023). 

2. Hatch, H. W., Siderius, D. W., Errington, J. R., and Shen, V. K., "Efficiency Comparison of Single- and Multiple-Macrostate Grand Canonical Ensemble Transition-Matrix Monte Carlo Simulations," Journal of Physical Chemistry B, (2023). 

3. Choudhary, K., Yildirim, T., Siderius, D. W., Kusne, A. G., McDannald, A., and Ortiz-Montalvo, D. L., "Graph neural network predictions of metal organic framework CO2 adsorption properties," Computational Materials Science, 210, (2022). 

4. Irikura, K. K., "Theoretical dissociation, ionization, and spin-orbit energetics of the diatomic platinum hydrides PtH, PtH+, and PtH-," Journal of Chemical Physics, 157, (2022). 

5. Krekelberg, W. P. and Shen, V. K., "On the virial expansion of model adsorptive systems," Molecular Physics, 120, (2022). 

6. Mahynski, N. A., Ragland, J. M., Schuur, S. S., and Shen, V. K., "Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean," Environ. Sci. Technol., 56, 14361-14374 (2022). 

7. Mahynski, N. A., Han, B., Markiewitz, D., and Shen, V. K., "Derivable genetic programming for two-dimensional colloidal materials," Journal of Chemical Physics, 157, (2022). 

8. Monroe, J. I. and Shen, V. K., "Systematic control of collective variables learned from variational autoencoders," Journal of Chemical Physics, 157, (2022). 

9. Monroe, J. I. and Shen, V. K., "Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders," Journal of Chemical Theory and Computation, 18, 3622-3636 (2022). 

10. Nguyen, H. G. T., Toman, B., Martinez, J. C., Siderius, D. W., and van Zee, R. D., "Reference surface excess isotherms for carbon dioxide adsorption on ammonium ZSM-5 at various temperatures," Adsorption-Journal of the International Adsorption Society, 28, 15-25 (2022). 

11. Siderius, D. W., Hatch, H. W., Errington, J. R., and Shen, V. K., "Comments on "Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights"," Aiche Journal, 68, (2022). 

12. Siderius, D. W., Hatch, H. W., and Shen, V. K., "Temperature Extrapolation of Henry?s Law Constants and the Isosteric Heat of Adsorption," Journal of Physical Chemistry B, 126, 7999-8009 (2022). 

13. Blonder, N. and Delaglio, F., "The NMR Spectral Measurement Database: A System for Organizing and Accessing NMR Spectra of Therapeutic Proteins," Journal of Research of the National Institute of Standards and Technology, 126, (2021). 

14. Irikura, K. K., "Polarizability of atomic Pt, Pt+, and Pt-," Journal of Chemical Physics, 154, (2021). 

15. Irikura, K. K., "Thermochemical spin-orbit corrections for platinum cation (Pt+)," International Journal of Quantum Chemistry, 121, (2021). 

16. Mahynski, N. A., Hatch, H. W., Witman, M., Sheen, D. A., Errington, J. R., and Shen, V. K., "Flat-histogram extrapolation as a useful tool in the age of big data," Molecular Simulation, 47, 395-407 (2021). 

17. Mahynski, N. A. and Shen, V. K., "Symmetry-derived structure directing agents for two-dimensional crystals of arbitrary colloids," Soft Matter, 17, 7853-7866 (2021). 

18. Siderius, D. W., "Digitization of Adsorption Isotherms from "The Thermodynamics and Hysteresis of Adsorption"," Journal of Research of the National Institute of Standards and Technology, 126, (2021). 

19. Siderius, D. W., "Digitization of Adsorption Isotherms from" The Thermodynamics and Hysteresis of Adsorption"," Journal of Research of the National Institute of Standards and Technology, 126, 1-7 (2021). 

20. Hatch, H. W., "Parallel Prefetching for Canonical Ensemble Monte Carlo Simulations," Journal of Physical Chemistry A, 124, 7191-7198 (2020). 

21. Irikura, K. K., "Thermochemical spin-orbit corrections for atomic platinum (Pt)," International Journal of Quantum Chemistry, 120, (2020). 

22. Irikura, K. K., "Multireaction Approach to Quantum Thermochemistry," Journal of Physical Chemistry A, 124, 8088-8099 (2020). 

23. Mahynski, N. A., Mao, R. F., Pretti, E., Shen, V. K., and Mittal, J., "Grand canonical inverse design of multicomponent colloidal crystals," Soft Matter, 16 , 3187-3194 (2020). 

24. Monroe, J. I., Hatch, H. W., Mahynski, N. A., Shell, M. S., and Shen, V. K., "Extrapolation and interpolation strategies for efficiently estimating structural observables as a function of temperature and density," Journal of Chemical Physics, 153, (2020). 

25. Pretti, E., Shen, V. K., Mittal, J., and Mahynski, N. A., "Symmetry-Based Crystal Structure Enumeration in Two Dimensions," Journal of Physical Chemistry A, 124, 3276-3285 (2020). 

26. Rocha, W. F. D., do Prado, C. B., and Blonder, N., "Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods," Molecules, 25, (2020). 

27. Sheen, D. A., Shen, V. K., Brinson, R. G., Arbogast, L. W., Marino, J. P., and Delaglio, F., "Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics," Chemometrics and Intelligent Laboratory Systems, 199, (2020). 

28. Hatch, H., Siderius, D., and Shen, V., "Improving reproducibility of molecular simulations with reference data and open source software," Abstracts of Papers of the American Chemical Society, 257, (2019). 

29. Hatch, H. W., Hall, S. W., Errington, J. R., and Shen, V. K., "Improving the efficiency of Monte Carlo simulations of ions using expanded grand canonical ensembles," Journal of Chemical Physics, 151, (2019). 

30. Hatch, H. W. and Mccann, G. W., "Tabular Potentials for Monte Carlo Simulation of Supertoroids with Short-Range Interactions," Journal of Research of the National Institute of Standards and Technology, 124, (2019). 

31. Krekelberg, W. P., Mahynski, N. A., and Shen, V. K., "On the virial coefficients of confined fluids: Analytic expressions for the thermodynamic properties of hard particles with attractions in slit and cylindrical pores to second order," Journal of Chemical Physics, 150, (2019). 

32. Mahynski, N. A., Pretti, E., Shen, V. K., and Mittal, J., "Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly," Nature Communications, 10, (2019). 

33. Maula, T. A., Hatch, H. W., Shen, V. K., Rangarajan, S., and Mittal, J., "Designing molecular building blocks for the self-assembly of complex porous networks," Molecular Systems Design & Engineering, 4, 644-653 (2019). 

34. Nguyen, H. G. T., Horn, J. C., Bleakney, M., Siderius, D. W., and Espinal, L., "Understanding Material Characteristics through Signature Traits from Helium Pycnometry," Langmuir, 35, 2115-2122 (2019). 

35. Sheen, D. A., "interlab: A Python Module for Analyzing Interlaboratory Comparison Data," Journal of Research of the National Institute of Standards and Technology, 124, (2019). 

36. Hatch, H. W., Mahynski, N. A., Murphy, R. P., Blanco, M. A., and Shen, V. K., "Monte Carlo simulation of cylinders with short-range attractions," Aip Advances, 8, (2018). 

37. Hatch, H. W., Mahynski, N. A., and Shen, V. K., "FEASST: Free Energy and Advanced Sampling Simulation Toolkit," Journal of Research of the National Institute of Standards and Technology, 123, (2018). 

38. Irikura, K., "Electron-initiated photochemistry: Molecular ionization, excitation, and reactions," Abstracts of Papers of the American Chemical Society, 256 , (2018). 

39. Mahynski, N. A., Jiao, S., Hatch, H. W., Blanco, M. A., and Shen, V. K., "Predicting structural properties of fluids by thermodynamic extrapolation," Journal of Chemical Physics, 148, (2018). 

40. Sheen, D., Benner, B., Simon, Y., Rocha, W. F. C., Jones, C., Blonder, N., and Lippa, K., "Data harmonization in metabolomics for quality assurance and control," Abstracts of Papers of the American Chemical Society, 256, (2018). 

41. Shen, V. K., Siderius, D. W., and Mahynski, N. A., "Molecular simulation of capillary phase transitions in flexible porous materials," Journal of Chemical Physics, 148, (2018). 

42. Allison, T. and Tong, Y. Y., "Computational study of the effect of surface-bound disulfide on the oxygen reduction reaction," Abstracts of Papers of the American Chemical Society, 254, (2017). 

43. Irikura, K. K., "Ab Initio Computation of Energy Deposition During Electron Ionization of Molecules," Journal of Physical Chemistry A, 121, 7751-7760 (2017). 

44. Irikura, K. K., "Partial Ionization Cross Sections of Organic Molecules," Journal of Research of the National Institute of Standards and Technology, 122, 1-67 (2017). 

45. Krekelberg, W. P., Siderius, D. W., Shen, V. K., Truskett, T. M., and Errington, J. R., "Position-Dependent Dynamics Explain Pore-Averaged Diffusion in Strongly Attractive Adsorptive Systems," Langmuir, 33, 13955-13963 (2017). 

46. Krekelberg, W. P., Siderius, D. W., Shen, V. K., Truskett, T. M., and Errington, J. R., "Connection Between Thermodynamics and Dynamics of Simple Fluids in Pores: Impact of Fluid-Fluid Interaction Range and Fluid Solid Interaction Strength," Journal of Physical Chemistry C, 121, 16316-16327 (2017). 

47. Krekelberg, W. P., Siderius, D. W., Shen, V. K., Truskett, T. M., and Errington, J. R., "Position-dependent dynamics explain pore-averaged diffusion in strongly attractive adsorptive systems," Langmuir, 33, 13955-13963 (2017). 

48. Mahynski, N. A., Errington, J. R., and Shen, V. K., "Multivariable extrapolation of grand canonical free energy landscapes," Journal of Chemical Physics, 147, (2017). 

49. Mahynski, N. A., Blanco, M. A., Errington, J. R., and Shen, V. K., "Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods," Journal of Chemical Physics, 146, (2017). 

50. Mahynski, N. A. and Shen, V. K., "Controlling relative polymorph stability in soft porous crystals with a barostat," Journal of Chemical Physics, 146, (2017). 

51. Mahynski, N. A., Errington, J. R., and Shen, V. K., "Temperature extrapolation of multicomponent grand canonical free energy landscapes," Journal of Chemical Physics, 147, (2017). 

52. Mahynski, N. A. and Shen, V. K., "Tuning flexibility to control selectivity in soft porous crystals," Journal of Chemical Physics, 146, (2017). 

53. Sheen, D. A., Rocha, W. F. C., Lippa, K. A., and Bearden, D. W., "A scoring metric for multivariate data for reproducibility analysis using chemometric methods," Chemometrics and Intelligent Laboratory Systems, 162, 10-20 (2017). 

54. Sheen, D. A., "mumpce_py: A Python Implementation of the Method of Uncertainty Minimization Using Polynomial Chaos Expansions," Journal of Research of the National Institute of Standards and Technology, 122, (2017). 

55. Siderius, D. W., Mahynski, N. A., and Shen, V. K., "Relationship between pore-size distribution and flexibility of adsorbent materials: statistical mechanics and future material characterization techniques," Adsorption-Journal of the International Adsorption Society, 23, 593-602 (2017). 

56. Siderius, D. W., Krekelberg, W. P., Chiang, W. S., Shen, V. K., and Liu, Y., "Quasi-Two-Dimensional Phase Transition of Methane Adsorbed in Cylindrical Silica Mesopores," Langmuir, 33, 14252-14262 (2017). 

57. Irikura, K. K., "Semi-empirical estimation of ion-specific cross sections in electron ionization of molecules," Journal of Chemical Physics, 145, (2016). 

58. Ragland, J. M., Liebert, D., and Wirth, E., "Using Procedural Blanks to Generate Analyte-Specific Limits of Detection for Persistent Organic Pollutants Based on GC-MS Analysis," Analytical Chemistry, 86, 7696-7704 (2014). 

59. Sheen, D. A. and Tsang, W., "A comparison of literature models for the oxidation of normal heptane," Combustion and Flame, 161, 1984-1992 (2014). 

60. Ragland, J. M., Arendt, M. D., Kucklick, J. R., and Keller, J. M., "Persistent Organic Pollutants in Blood Plasma of Satellite-Tracked Adult Male Loggerhead Sea Turtles (Caretta Caretta)," Environmental Toxicology and Chemistry, 30, 1549-1556 (2011).