1932

Abstract

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units–accelerated quantum chemistry in the cloud, artificial intelligence–driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-061020-053438
2023-04-24
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/physchem/74/1/annurev-physchem-061020-053438.html?itemId=/content/journals/10.1146/annurev-physchem-061020-053438&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Owens JD, Houston M, Luebke D, Green S, Stone JE, Phillips JC. 2008. GPU computing. Proc. IEEE 96:879–99
    [Google Scholar]
  2. 2.
    Ufimtsev IS, Martínez TJ. 2008. Graphical processing units for quantum chemistry. Comput. Sci. Eng. 10:26–34
    [Google Scholar]
  3. 3.
    Salomon-Ferrer R, Case DA, Walker RC. 2013. An overview of the Amber biomolecular simulation package. WIREs Comput. Mol. Sci. 3:198–210
    [Google Scholar]
  4. 4.
    Arthur EJ, Brooks CL 3rd. 2016. Efficient implementation of constant pH molecular dynamics on modern graphics processors. J. Comput. Chem. 37:2171–80
    [Google Scholar]
  5. 5.
    Kutzner C, Páll S, Fechner M, Esztermann A, de Groot BL, Grubmüller H 2015. Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J. Comput. Chem. 36:1990–2008
    [Google Scholar]
  6. 6.
    Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y et al. 2017. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLOS Comput. Biol. 13:e1005659
    [Google Scholar]
  7. 7.
    Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Isborn CM et al. 2021. TeraChem: a graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics. WIREs Comput. Mol. Sci. 11:e1494
    [Google Scholar]
  8. 8.
    Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Kokkila-Schumacher SIL et al. 2020. TeraChem: accelerating electronic structure and ab initio molecular dynamics with graphical processing units. J. Chem. Phys. 152:224110
    [Google Scholar]
  9. 9.
    Barca GMJ, Alkan M, Galvez-Vallejo JL, Poole DL, Rendell AP, Gordon MS. 2021. Faster self-consistent field (SCF) calculations on GPU clusters. J. Chem. Theory Comput. 17:7486–503
    [Google Scholar]
  10. 10.
    Kussmann J, Laqua H, Ochsenfeld C. 2021. Highly efficient resolution-of-the-identity density functional theory calculations on central and graphics processing units. J. Chem. Theory Comput. 17:1512–21
    [Google Scholar]
  11. 11.
    Casalino L, Dommer AC, Gaieb Z, Barros EP, Sztain T et al. 2021. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. Int. J. High Perform. Comput. Appl. 35:432–51
    [Google Scholar]
  12. 12.
    Sisto A, Stross C, van der Kamp MW, O'Connor M, McIntosh-Smith S et al. 2017. Atomistic non-adiabatic dynamics of the LH2 complex with a GPU-accelerated ab initio exciton model. Phys. Chem. Chem. Phys. 19:14924–36
    [Google Scholar]
  13. 13.
    Kulik HJ, Zhang J, Klinman JP, Martínez TJ. 2016. How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase. J. Phys. Chem. B 120:11381–94
    [Google Scholar]
  14. 14.
    Liu F, Luehr N, Kulik HJ, Martínez TJ. 2015. Quantum chemistry for solvated molecules on graphical processing units using polarizable continuum models. J. Chem. Theory Comput. 11:3131–44
    [Google Scholar]
  15. 15.
    Liu F, Sanchez DM, Kulik HJ, Martínez TJ. 2019. Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models. Int. J. Quantum Chem. 119:e25760
    [Google Scholar]
  16. 16.
    Isborn CM, Luehr N, Ufimtsev IS, Martínez TJ. 2011. Excited-state electronic structure with configuration interaction singles and Tamm–Dancoff time-dependent density functional theory on graphical processing units. J. Chem. Theory Comput. 7:1814–23
    [Google Scholar]
  17. 17.
    Jones CM, List NH, Martínez TJ. 2021. Resolving the ultrafast dynamics of the anionic green fluorescent protein chromophore in water. Chem. Sci. 12:11347–63
    [Google Scholar]
  18. 18.
    Jones CM, List NH, Martínez TJ. 2022. Steric and electronic origins of fluorescence in GFP and GFP-like proteins. J. Am. Chem. Soc. 144:12732–46
    [Google Scholar]
  19. 19.
    Yu JK, Liang R, Liu F, Martínez TJ. 2019. First-principles characterization of the elusive I fluorescent state and the structural evolution of retinal protonated Schiff base in bacteriorhodopsin. J. Am. Chem. Soc. 141:18193–203
    [Google Scholar]
  20. 20.
    Liang R, Liu F, Martínez TJ. 2019. Nonadiabatic photodynamics of retinal protonated Schiff base in channelrhodopsin 2. J. Phys. Chem. Lett. 10:2862–68
    [Google Scholar]
  21. 21.
    Liang R, Yu JK, Meisner J, Liu F, Martinez TJ. 2021. Electrostatic control of photoisomerization in channelrhodopsin 2. J. Am. Chem. Soc. 143:5425–37
    [Google Scholar]
  22. 22.
    Wang L-P, Titov A, McGibbon R, Liu F, Pande VS, Martínez TJ. 2014. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 6:1044–48
    [Google Scholar]
  23. 23.
    Pieri E, Lahana D, Chang AM, Aldaz CR, Thompson KC, Martínez TJ. 2021. The non-adiabatic nanoreactor: towards the automated discovery of photochemistry. Chem. Sci. 12:7294–307
    [Google Scholar]
  24. 24.
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R et al. 2010. A view of cloud computing. Commun. ACM 53:50–58
    [Google Scholar]
  25. 25.
    Strieth-Kalthoff F, Sandfort F, Segler MHS, Glorius F. 2020. Machine learning the ropes: principles, applications and directions in synthetic chemistry. Chem. Soc. Rev. 49:6154–68
    [Google Scholar]
  26. 26.
    Coley CW, Green WH, Jensen KF. 2018. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51:1281–89
    [Google Scholar]
  27. 27.
    Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18:463–77
    [Google Scholar]
  28. 28.
    Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP et al. 2019. Exploiting machine learning for end-to-end drug discovery and development. Nat. Mater. 18:435–41
    [Google Scholar]
  29. 29.
    Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. 2018. Machine learning for molecular and materials science. Nature 559:547–55
    [Google Scholar]
  30. 30.
    Behler J, Parrinello M. 2007. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98:146401
    [Google Scholar]
  31. 31.
    Noé F, Tkatchenko A, Müller K-R, Clementi C. 2020. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–90
    [Google Scholar]
  32. 32.
    Dick S, Fernandez-Serra M. 2020. Machine learning accurate exchange and correlation functionals of the electronic density. Nat. Commun. 11:3509
    [Google Scholar]
  33. 33.
    Kirkpatrick J, McMorrow B, Turban DHP, Gaunt AL, Spencer JS et al. 2020. Pushing the frontiers of density functionals by solving the fractional electron problem. Science 374:1385–89
    [Google Scholar]
  34. 34.
    Snyder JC, Rupp M, Hansen K, Müller K-R, Burke K 2012. Finding density functionals with machine learning. Phys. Rev. Lett. 108:253002
    [Google Scholar]
  35. 35.
    Lehtola S, Kartunnen AJ. 2022. Free and open software for computational chemistry education. WIREs Comput. Mol. Sci. 12:e1610
    [Google Scholar]
  36. 36.
    Kim S, Chen J, Cheng T, Gindulyte A, He J et al. 2019. PubChem 2019 update: improved access to chemical data. Nucl. Acids Res. 47:D1102–9
    [Google Scholar]
  37. 37.
    Raucci U. 2022. Cutting-edge technologies in computational chemistry Video. Mar. 17. https://www.youtube.com/watch?v=66nzmh3o8rc
  38. 38.
    Wolf TJA, Sanchez DM, Yang J, Parrish RM, Nunes JPF et al. 2019. The photochemical ring-opening of 1,3-cyclohexadiene imaged by ultrafast electron diffraction. Nat. Chem. 11:504–9
    [Google Scholar]
  39. 39.
    Sanchez DM, Raucci U, Martínez TJ. 2021. In silico discovery of multistep chemistry initiated by a conical intersection: the challenging case of donor–acceptor Stenhouse adducts. J. Am. Chem. Soc. 143:20015–21
    [Google Scholar]
  40. 40.
    Qiu Y, Smith DGA, Stern CD, Feng M, Jang H, Wang L-P. 2020. Driving torsion scans with wavefront propagation. J. Chem. Phys. 152:244116
    [Google Scholar]
  41. 41.
    Whitten JL. 1973. Coulombic potential energy integrals and approximations. J. Chem. Phys. 58:4496
    [Google Scholar]
  42. 42.
    Almlof J, Faegri K, Korsell K. 1982. Principles for a direct SCF approach to LCAO-MO ab-initio calculations. J. Comput. Chem. 3:385–99
    [Google Scholar]
  43. 43.
    Maurer SA, Lambrecht DS, Flaig D, Ochsenfeld C. 2012. Distance-dependent Schwartz-based integral estimates for two-electron integrals: reliable tightness versus rigorous upper bounds. J. Chem. Phys. 136:144107
    [Google Scholar]
  44. 44.
    Ufimtsev IS, Martínez TJ. 2008. Quantum chemistry on graphical processing units. 1. Strategies for two-electron integral evaluation. J. Chem. Theory Comput. 4:222–31
    [Google Scholar]
  45. 45.
    Ufimtsev IS, Martinez TJ. 2009. Quantum chemistry on graphical processing units. 2. Direct self-consistent-field implementation. J. Chem. Theory Comput. 5:1004–15
    [Google Scholar]
  46. 46.
    Ufimtsev IS, Martinez TJ. 2009. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J. Chem. Theory Comput. 5:2619–28
    [Google Scholar]
  47. 47.
    Titov AV, Ufimtsev IS, Luehr N, Martinez TJ. 2013. Generating efficient quantum chemistry codes for novel architectures. J. Chem. Theory Comput. 9:213–21
    [Google Scholar]
  48. 48.
    Gordon MS, Barca G, Leang SS, Poole D, Rendell AP et al. 2020. Novel computer architectures and quantum chemistry. J. Phys. Chem. A 124:4557–82
    [Google Scholar]
  49. 49.
    Götz AW, Wölfle T, Walker RC. 2010. Quantum chemistry on graphics processing units. Annu. Rep. Comput. Chem. 6:21–35
    [Google Scholar]
  50. 50.
    Barca GMJ, Bertoni C, Carrington L, Datta D, De Silva N et al. 2020. Recent developments in the General Atomic and Molecular Electronic Structure System. J. Chem. Phys. 152:154102
    [Google Scholar]
  51. 51.
    Levine BG, Martinez TJ. 2003. Hijacking the PlayStation2 for computational chemistry. Abstr. Pap. Am. Chem. Soc. 226:U426
    [Google Scholar]
  52. 52.
    Stone JE, Hardy DJ, Ufimtsev IS, Schulten K. 2010. GPU-accelerated molecular modeling coming of age. J. Mol. Graph. Model. 29:116–25
    [Google Scholar]
  53. 53.
    Wilhite DL, Euwema RN. 1974. Charge-conserving integral approximations for ab initio quantum chemistry. J. Chem. Phys. 61:375
    [Google Scholar]
  54. 54.
    Yasuda K. 2008. Two-electron integral evaluation on the graphics processor unit. J. Comput. Chem. 29:334–42
    [Google Scholar]
  55. 55.
    Luehr N, Ufimtsev IS, Martínez TJ. 2011. Dynamic precision for electron repulsion integral evaluation on graphical processing units (GPUs). J. Chem. Theory Comput. 7:949–54
    [Google Scholar]
  56. 56.
    Vysotskiy VP, Cederbaum LS. 2011. Accurate quantum chemistry in single precision arithmetic: correlation energy. J. Chem. Theory Comput. 7:320–26
    [Google Scholar]
  57. 57.
    Pokhilko P, Epifanovsky E, Krylov AI. 2018. Double precision is not needed for many-body calculations: emergent conventional wisdom. J. Chem. Theory Comput. 14:4088–96
    [Google Scholar]
  58. 58.
    Docker Inc 2022. Home page. Docker https://www.docker.com
    [Google Scholar]
  59. 59.
    Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J. 2016. Borg, Omega, and Kubernetes: lessons learned from three container-management systems over a decade. Queue 14:70–93
    [Google Scholar]
  60. 60.
    Thackston R, Fortenberry RC. 2015. The performance of low-cost commercial cloud computing as an alternative in computational chemistry. J. Comput. Chem. 36:926–33
    [Google Scholar]
  61. 61.
    Fortenberry RC, Thackston R. 2015. Optimal cloud use of quartic force fields: the first purely commercial cloud computing based study for rovibrational analysis of SiCH. Int. J. Quantum Chem. 115:1650–57
    [Google Scholar]
  62. 62.
    Moghadam BT, Alvarsson J, Holm M, Eklund M, Carlsson L, Spjuth O. 2015. Scaling predictive modeling in drug development with cloud computing. J. Chem. Inf. Model. 55:19–25
    [Google Scholar]
  63. 63.
    Seritan S, Thompson K, Martínez TJ. 2020. TeraChem cloud: a high-performance computing service for scalable distributed GPU-accelerated electronic structure calculations. J. Chem. Inf. Model. 60:2126–37
    [Google Scholar]
  64. 64.
    Open Eye Scientific. 2022. Orion. OpenEye, Cadence Molecular Sciences https://www.eyesopen.com/orion
  65. 65.
    Smith DGA, Altarawy D, Burns LA, Welborn M, Naden LN et al. 2021. The MolSSI QCArchive project: an open-source platform to compute, organize, and share quantum chemistry data. WIREs Comput. Mol. Sci. 11:e1491
    [Google Scholar]
  66. 66.
    Raucci U, Valentini A, Pieri E, Weir H, Seritan S, Martínez TJ. 2021. Voice-controlled quantum chemistry. Nat. Comput. Sci. 1:42–45
    [Google Scholar]
  67. 67.
    Contreras ML, Allendes C, Alvarez LT, Rozas R. 1990. Computational perception and recognition of digitized molecular structures. J. Chem. Inf. Comput. Sci. 30:302–7
    [Google Scholar]
  68. 68.
    McDaniel JR, Balmuth JR. 1992. Kekule: OCR-optical chemical (structure) recognition. J. Chem. Inf. Comput. Sci. 32:373–78
    [Google Scholar]
  69. 69.
    Rajan K, Brinkhaus HO, Zielesny A, Steinbeck C. 2020. A review of optical chemical structure recognition tools. J. Cheminf. 12:60
    [Google Scholar]
  70. 70.
    Beard EJ, Cole JM. 2020. ChemSchematicResolver: a toolkit to decode 2D chemical diagrams with labels and R-groups into annotated chemical named entities. J. Chem. Inf. Model. 60:2059–72
    [Google Scholar]
  71. 71.
    Filippov IV, Nicklaus MC. 2009. Optical structure recognition software to recover chemical information: OSRA, an open source solution. J. Chem. Inf. Model. 49:740–43
    [Google Scholar]
  72. 72.
    Gkoutos GV, Rzepa H, Clark RM, Adjei O, Johal H. 2003. Chemical machine vision: automated extraction of chemical metadata from raster images. J. Chem. Inf. Comput. Sci. 43:1342–55
    [Google Scholar]
  73. 73.
    Ibison P, Jacquot M, Kam F, Neville AG, Simpson RW et al. 1993. Chemical literature data extraction: the CLiDE Project. J. Chem. Inf. Comput. Sci. 33:338–44
    [Google Scholar]
  74. 74.
    Ouyang TY. 2007. Recognition of hand drawn chemical diagrams MS Thesis MIT Cambridge, MA:
  75. 75.
    Park J, Rosania GR, Shedden KA, Nguyen M, Lyu N, Saitou K. 2009. Automated extraction of chemical structure information from digital raster images. Chem. Cent. J. 3:4
    [Google Scholar]
  76. 76.
    Valko AT, Johnson AP. 2009. CLiDE Pro: the latest generation of CLiDE, a tool for optical chemical structure recognition. J. Chem. Inf. Model. 49:780–87
    [Google Scholar]
  77. 77.
    Oldenhof M, Arany A, Moreau Y, Simm J. 2020. ChemGrapher: optical graph recognition of chemical compounds by deep learning. J. Chem. Inf. Model. 60:4506–17
    [Google Scholar]
  78. 78.
    Staker J, Marshall K, Abel R, McQuaw CM 2019. Molecular structure extraction from documents using deep learning. J. Chem. Inf. Model. 59:1017–29
    [Google Scholar]
  79. 79.
    Rajan K, Zielesny A, Steinbeck C. 2020. DECIMER: towards deep learning for chemical image recognition. J. Cheminf. 12:65
    [Google Scholar]
  80. 80.
    Pic2mol 2022. The missing image-to-structure feature for ChemDraw. Pic2mol. https://pic2mol.com/
    [Google Scholar]
  81. 81.
    Clevert D-A, Le T, Winter R, Montanari F. 2021. Img2Mol – accurate SMILES recognition from molecular graphical depictions. Chem. Sci. 12:14174–81
    [Google Scholar]
  82. 82.
    Weir H, Thompson K, Woodward A, Choi B, Braun A, Martínez TJ. 2021. ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning. Chem. Sci. 12:10622–33
    [Google Scholar]
  83. 83.
    Weininger D. 1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28:31–36
    [Google Scholar]
  84. 84.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (NIPS 2017)5998–6008. San Diego, CA: NeurIPS
    [Google Scholar]
  85. 85.
    OpenBabel 2022. Open Babel: the open source chemistry toolbox. OpenBabel https://openbabel.org/wiki/Main_Page
  86. 86.
    Mathpix Inc 2022. Convert printed and handwritten chemical diagrams to SMILES. Mathpix Inc. https://mathpix.com/blog/handwritten-chem-diagrams
    [Google Scholar]
  87. 87.
    Martinez Group 2022. Interactive QC repository. Martinez Group https://github.com/mtzgroup/ARPC_InteractiveQC
    [Google Scholar]
  88. 88.
    Sakshuwong S, Weir H, Raucci U, Martínez TJ. 2022. Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry. J. Chem. Phys. 156:204801
    [Google Scholar]
  89. 89.
    Söldner R, Rheinländer S, Meyer T, Olszowy M, Austerjost J. 2022. Human–device interaction in the life science laboratory. Adv. Biochem. Eng. Biotechnol. 182:83–113
    [Google Scholar]
  90. 90.
    Rakotomalala F, Randriatsarafara HN, Hajalalaina AR, Ravonimanantsoa NMV. 2021. Voice user interface: literature review, challenges and future directions. Syst. Theory Control Comput. J. 1:65–89
    [Google Scholar]
  91. 91.
    Hirschberg J, Manning CD. 2015. Advances in natural language processing. Science 349:261–66
    [Google Scholar]
  92. 92.
    Nadkarni PM, Ohno-Machado L, Chapman WW. 2011. Natural language processing: an introduction. J. Am. Med. Inform. Assoc. 18:544–51
    [Google Scholar]
  93. 93.
    Hocky GM, White AD. 2022. Natural language processing models that automate programming will transform chemistry research and teaching. Digit. Discov. 1:79–83
    [Google Scholar]
  94. 94.
    White AD, Hocky GM, Gandhi HA, Ansari M, Cox S et al. 2022. Do large language models know chemistry?. ChemRxiv https://doi.org/10.26434/chemrxiv-2022-3md3n
    [Google Scholar]
  95. 95.
    Chen M, Tworek J, Jun H, Yuan Q, Pinto HPdO, et al. 2021. Evaluating large language models trained on code. arXiv:2107.03374 [cs.LG]
  96. 96.
    Benzeghiba M, De Mori R, Deroo O, Dupont S, Erbes T et al. 2007. Automatic speech recognition and speech variability: a review. Speech Commun. 49:763–86
    [Google Scholar]
  97. 97.
    Seritan S, Wang Y, Ford JE, Valentini A, Gold T, Martínez TJ. 2021. InteraChem: virtual reality visualizer for reactive interactive molecular dynamics. J. Chem. Educ. 98:3486–92
    [Google Scholar]
  98. 98.
    Google Inc 2022. GoogleCloud, cloud speech-to-text. Google Inc https://cloud.google.com/speech-to-text
    [Google Scholar]
  99. 99.
    Cruz-Neira C, Sandin DJ, DeFanti TA, Kenyon RV, Hart JC. 1992. The CAVE: audio visual experience automatic virtual environment. Commun. ACM 35:64
    [Google Scholar]
  100. 100.
    Humphrey W, Dalke A, Schulten K. 1996. VMD: visual molecular dynamics. J. Mol. Graph. 14:33–38
    [Google Scholar]
  101. 101.
    VRPN 2022. Virtual reality peripheral network – official github repository. VRPN https://github.com/vrpn/vrpn
    [Google Scholar]
  102. 102.
    Pietikäinen O, Hämäläinen P, Lehtinen J, Karttunen AJ. 2021. VRChem: a virtual reality molecular builder. Appl. Sci. 11:10767
    [Google Scholar]
  103. 103.
    Nanome Inc 2022. Home page. Nanome https://nanome.ai
    [Google Scholar]
  104. 104.
    Norrby M, Grebner C, Eriksson J, Boström J. 2015. Molecular Rift: virtual reality for drug designers. J. Chem. Inf. Model. 55:2475–84
    [Google Scholar]
  105. 105.
    Goddard TD, Brilliant AA, Skillman TL, Vergenz S, Tyrwhitt-Drake J et al. 2018. Molecular visualization on the Holodeck. J. Mol. Biol. 430:3982–96
    [Google Scholar]
  106. 106.
    O'Connor MB, Bennie SJ, Deeks HM, Jamieson-Binnie A, Jones AJ et al. 2019. Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: an open-source multi-person framework. J. Chem. Phys. 150:220901
    [Google Scholar]
  107. 107.
    Wang Y, Seritan S, Lahana D, Ford JE, Valentini A et al. 2022. InteraChem: exploring excited states in virtual reality with ab initio interactive molecular dynamics. J. Chem. Theory Comput. 18:3308–17
    [Google Scholar]
  108. 108.
    Azuma RT. 1997. A survey of augmented reality. Presence Virtual Augment. Real. 6:355–85
    [Google Scholar]
  109. 109.
    Billinghurst M, Clark A, Lee G 2015. A survey of augmented reality. Found. Trends Hum.-Comput. Interact. 8:73–272
    [Google Scholar]
  110. 110.
    Rodríguez FC, Dal Peraro M, Abriata LA. 2021. Democratizing interactive, immersive experiences for science education with WebXR. Nat. Comput. Sci. 1:631–32
    [Google Scholar]
  111. 111.
    Singhal S, Bagga S, Goyal P, Saxena V. 2012. Augmented chemistry: Interactive education system. Int. J. Comput. Appl. 49:1–5
    [Google Scholar]
  112. 112.
    Jiménez ZA. 2019. Teaching and learning chemistry via augmented and immersive virtual reality. ACS Symp. Ser. 1318:31–52
    [Google Scholar]
  113. 113.
    Fombona-Pascual A, Fombona J, Vicente R 2022. Augmented reality, a review of a way to represent and manipulate 3D chemical structures. J. Chem. Inf. Model. 62:1863–72
    [Google Scholar]
  114. 114.
    Apple Computer 2022. ARKit. Apple https://developer.apple.com/documentation/arkit
    [Google Scholar]
  115. 115.
    Pixar Animation Studios 2021. Introduction to USD. Pixar Animation Studios https://graphics.pixar.com/usd/docs/Introduction-to-USD.html
    [Google Scholar]
  116. 116.
    HelixAIInc 2022. HelixAI – voice powered digital laboratory assistants for scientific laboratories. HelixAIInc https://www.askhelix.io/
    [Google Scholar]
  117. 117.
    Cambre J, Liu Y, Taylor RE, Kulkarni C. 2019. Vitro: designing a voice assistant for the scientific lab workplace. In Proceedings of the 2019 Designing Interactive Systems Conference (DIS 19)pp. 1531–42 New York: ACM
    [Google Scholar]
  118. 118.
    Raucci U. 2021. ChemVox: voice-controlled quantum chemistry Video. Jan. 14. https://youtu.be/Mxyw9381K-g
  119. 119.
    Ranga JS. 2018. Multipurpose use of Explain Everything iPad app for teaching chemistry courses. J. Chem. Educ. 95:895–98
    [Google Scholar]
  120. 120.
    Williams AJ, Pence HE. 2011. Smart phones, a powerful tool in the chemistry classroom. J. Chem. Educ. 88:683–86
    [Google Scholar]
  121. 121.
    Libman D, Huang L. 2013. Chemistry on the go: review of chemistry apps on smartphones. J. Chem. Educ. 90:320–25
    [Google Scholar]
  122. 122.
    Williams AJ, Ekins S, Clark AM, Jack JJ, Apodaca RL. 2011. Mobile apps for chemistry in the world of drug discovery. Drug Discov. Today 16:928–39
    [Google Scholar]
  123. 123.
    Fonseca CSC, Zacarias M, Figueiredo M. 2021. MILAGE LEARN+: a mobile learning app to aid the students in the study of organic chemistry. J. Chem. Educ. 98:1017–23
    [Google Scholar]
  124. 124.
    Feldt J, Mata RA, Dieterich JM. 2012. Atomdroid: a computational chemistry tool for mobile platforms. J. Chem. Inf. Model. 52:1072–78
    [Google Scholar]
  125. 125.
    Sunset Lake Software 2022. Molecules. Sunset Lake Software http://www.sunsetlakesoftware.com/molecules/index.html
    [Google Scholar]
  126. 126.
    Google Play 2022. Mobile Molecular Modeling -Mo3. Google LLC https://play.google.com/store/apps/details?id=club.amase.mocubed
    [Google Scholar]
  127. 127.
    WebMO LLC 2022. WebMO app features. WebMO LLC https://www.webmo.net/features/app.html
    [Google Scholar]
  128. 128.
    Aw JK, Boellaard KC, Tan TK, Yap J, Loh YP et al. 2020. Interacting with three-dimensional molecular structures using an augmented reality mobile app. J. Chem. Educ. 97:3877–81
    [Google Scholar]
  129. 129.
    Yang S, Mei B, Yue X. 2018. Mobile augmented reality assisted chemical education: insights from elements 4D. J. Chem. Educ. 95:1060–62
    [Google Scholar]
  130. 130.
    Jones OAH, Spichkova M, Spencer MJS. 2018. Chirality-2: development of a multilevel mobile gaming app to support the teaching of introductory undergraduate-level organic chemistry. J. Chem. Educ. 95:1216–20
    [Google Scholar]
  131. 131.
    Rodríguez FC, Frattini G, Krapp LF, Martinez-Hung H, Moreno DM et al. 2021. MoleculARweb: a web site for chemistry and structural biology education through interactive augmented reality out of the box in commodity devices. J. Chem. Educ. 98:2243–55
    [Google Scholar]
  132. 132.
    Sung R-J, Wilson AT, Lo SM, Crowl LM, Nardi J et al. 2020. BiochemAR: an augmented reality educational tool for teaching macromolecular structure and function. J. Chem. Educ. 97:147–53
    [Google Scholar]
  133. 133.
    Sakshuwong S. 2021. MolAR: bringing chemical structures to life with augmented reality and machine learning Video. Aug. 18. https://youtu.be/bLqkzz1vZL4
  134. 134.
    Raucci U. 2021. Touchscreen projector Video. Jan. 4. https://youtu.be/YvrVDrN2bIg
  135. 135.
    Sehnal D, Bittrich S, Deshpande M, Svobodová R, Berka K et al. 2021. Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. Nucl. Acids Res. 49:W431–37
    [Google Scholar]
  136. 136.
    Sakshuwong S, Raucci U. 2022. MolAR. Martinez Group https://mtzgroup.github.io/molar/
    [Google Scholar]
  137. 137.
    Cassidy KC, Šefčík J, Raghav Y, Chang A, Durrant JD 2020. ProteinVR: web-based molecular visualization in virtual reality. PLOS Comput. Biol. 16:e1007747
    [Google Scholar]
  138. 138.
    Balo AR, Wang M, Ernst OP. 2017. Accessible virtual reality of biomolecular structural models using the Autodesk Molecule Viewer. Nat. Methods 14:1122–23
    [Google Scholar]
  139. 139.
    MolView 2015. Home page. https://molview.org
  140. 140.
    Lee J, Patel DS, Ståhle J, Park S-J, Kern NR et al. 2019. CHARMM-GUI membrane builder for complex biological membrane simulations with glycolipids and lipoglycans. J. Chem. Theory Comput. 15:775–86
    [Google Scholar]
  141. 141.
    Kochnev Y, Hellemann E, Cassidy KC, Durrant JD. 2020. Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser. Bioinformatics 36:4513–15
    [Google Scholar]
  142. 142.
    Polik WF, Schmidt JR. 2022. WebMO: web-based computational chemistry calculations in education and research. WIREs Comput. Mol. Sci. 12:e1554
    [Google Scholar]
  143. 143.
    Entos Inc 2022. Envision: interactive chemistry. Entos Inc https://www.entos.ai/envision
    [Google Scholar]
  144. 144.
    TeraChem Web Services 2021. Quantum chemistry in one click. Martinez Group https://tws.dev.mtzlab.com/
    [Google Scholar]
  145. 145.
    Jmol 2022. Jmol: an open-source Java viewer for chemical structures in 3D. Jmol http://www.jmol.org/
  146. 146.
    Raucci U. 2022. TeraChem web services Video. Aug. 17. https://youtu.be/DxMPZTvh5Gw
  147. 147.
    Vincent-Ruz P. 2022. The secret silos of #ChemTwitter. C&EN https://cen.acs.org/sections/the-secret-silos-of-chemtwitter.html#intro
    [Google Scholar]
  148. 148.
    Twitter Inc 2022. Twitter API. Twitter Inc https://developer.twitter.com/en/docs/twitter-api
    [Google Scholar]
  149. 149.
    Yoshikawa N, Kubo R, Yamamoto KZ. 2021. Twitter integration of chemistry software tools. J. Cheminform. 13:46
    [Google Scholar]
  150. 150.
    Raucci U. 2022. Quantum chemistry calculation on Twitter!! Video Jan. 16. https://youtu.be/yrpVwqvSk-w
    [Google Scholar]
  151. 151.
    Fasola J, Matarić MJ. 2010. Robot motivator: increasing user enjoyment and performance on a physical/cognitive task Paper presented at the 2010 IEEE 9th International Conference on Development and Learning Ann Arbor, MI: Aug. 18–21
  152. 152.
    Vasalya A, Ganesh G, Kheddar A. 2018. More than just co-workers: presence of humanoid robot co-worker influences human performance. PLOS ONE 13:e0206698
    [Google Scholar]
  153. 153.
    Spaulding S, Gordon G, Breazeal C 2016. Affect-aware student models for robot tutors. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016) J Thangarajah, K Tuyls, C Jonker, S Marsella 864–72. New York: ACM
    [Google Scholar]
  154. 154.
    Foster ME, Craenen B, Deshmukh A, Lemon O, Bastianelli E et al. 2019. MuMMER: socially intelligent human-robot interaction in public spaces. arXiv:1909.06749 [cs.RO]
  155. 155.
    Metta G, Sandini G, Vernon D, Natale L, Nori F 2008. The iCub humanoid robot: an open platform for research in embodied cognition Paper presented at the 8th Workshop on Performance Metrics for Intelligent Systems Gaithersburg, MA: Aug. 19–21
  156. 156.
    Metta G, Natale L, Nori F, Sandini G, Vernon D et al. 2010. The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Net 23:1125–34
    [Google Scholar]
  157. 157.
    Raucci U. 2022. iCub and quantum chemistry Video. Mar. 16. https://youtu.be/0ETxoz2PDeM
/content/journals/10.1146/annurev-physchem-061020-053438
Loading
/content/journals/10.1146/annurev-physchem-061020-053438
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error