Learning a reactive potential for silica-water through uncertainty attribution

Heaney, P. J., Prewitt, C. T. & Gibbs, G. V.Silica: Physical behavior, geochemistry, and materials applications, vol. 29 (Walter de Gruyter GmbH & Co KG, 2018).Weitkamp, J. Zeolites and catalysis. Solid state Ion. 131, 175–188 (2000).Article 
CAS 

Google Scholar 
Gao, Y. et al. Multifunctional Role of Silica in Pharmaceutical Formulations. AAPS PharmSciTech 2022 23:4 23, 1–18 (2022).
Google Scholar 
Halas, N. J. Nanoscience under glass: The versatile chemistry of silica nanostructures. ACS Nano 2, 179–183 (2008).Article 
CAS 
PubMed 

Google Scholar 
Bergna, H. & Roberts, W.Colloidal silica: fundamentals and applications https://books.google.com/books?hl=en&lr=&id=d0huBwAAQBAJ&oi=fnd&pg=PP1&ots=uYjxTawhd_&sig=Pf92yxgRxSVaJ1on0L2VdXsvb_c (2005).Raza, N. et al. Synthesis and characterization of amorphous precipitated silica from alkaline dissolution of olivine. RSC Adv. 8, 32651–32658 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dewati, R. et al. Precipitated Silica from Pumice and Carbon Dioxide Gas (Co2) in Bubble Column Reactor. J. Phys.: Conf. Ser. 953, 012226 (2018).
Google Scholar 
Cundy, C. S. & Cox, P. A. The hydrothermal synthesis of zeolites: History and development from the earliest days to the present time. Chem. Rev. 103, 663–701 (2003).Article 
CAS 
PubMed 

Google Scholar 
Rai, D. K., Beaucage, G., Vogtt, K., Ilavsky, J. & Kammler, H. K. In situ study of aggregate topology during growth of pyrolytic silica. J. Aerosol Sci. 118, 34–44 (2018).Article 
ADS 
CAS 

Google Scholar 
Meier, M., Sonnick, S., Asylbekov, E., Rädle, M. & Nirschl, H. Multi-scale characterization of precipitated silica. Powder Technol. 354, 45–51 (2019).Article 
CAS 

Google Scholar 
Van Beest, B. W., Kramer, G. J. & Van Santen, R. A. Force fields for silicas and aluminophosphates based on ab initio calculations. Phys. Rev. Lett. 64, 1955 (1990).Article 
ADS 

Google Scholar 
Carré, A., Horbach, J., Ispas, S. & Kob, W. New fitting scheme to obtain effective potential from Car-Parrinello molecular-dynamics simulations: Application to silica. Europhys. Lett. 82, 17001 (2008).Article 
ADS 

Google Scholar 
Flikkema, E. & Bromley, S. T. A new interatomic potential for nanoscale silica. Chem. Phys. Lett. 378, 622–629 (2003).Article 
ADS 
CAS 

Google Scholar 
Fogarty, J. C., Aktulga, H. M., Grama, A. Y., Van Duin, A. C. & Pandit, S. A. A reactive molecular dynamics simulation of the silica-water interface. J. Chem. Phys. 132, 174704 (2010).Article 
ADS 
PubMed 

Google Scholar 
Rimsza, J. M., Yeon, J., Van Duin, A. C. & Du, J. Water Interactions with Nanoporous Silica: Comparison of ReaxFF and ab Initio based Molecular Dynamics Simulations. J. Phys. Chem. C. 120, 24803–24816 (2016).Article 
CAS 

Google Scholar 
Musil, F. et al. Physics-inspired structural representations for molecules and materials. Chem. Rev. 121, 1–60 (2021).Article 

Google Scholar 
Axelrod, S. et al. Learning Matter: Materials Design with Machine Learning and Atomistic Simulations. Acc. Mater. Res. 3, 343–357 (2022).Article 
CAS 

Google Scholar 
Yao, Y. & Kanai, Y. Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network. J. Phys. Chem. Lett. 12, 6354–6362 (2021).Article 
CAS 
PubMed 

Google Scholar 
Liu, J., Lan, J. & He, X. Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks. J. Phys. Chem. A 126, 3926–3936 (2022).Article 
CAS 
PubMed 

Google Scholar 
Zaverkin, V., Holzmüller, D., Schuldt, R. & Kästner, J. Predicting properties of periodic systems from cluster data: A case study of liquid water. J. Chem. Phys. 156, 114103 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Erhard, L. C., Rohrer, J., Albe, K. & Deringer, V. L. A machine-learned interatomic potential for silica and its relation to empirical models. npj Comput. Mater. 2022 8:1 8, 1–12 (2022).
Google Scholar 
Balyakin, I. A., Rempel, S. V., Ryltsev, R. E. & Rempel, A. A. Deep machine learning interatomic potential for liquid silica. Phys. Rev. E 102, 52125 (2020).Article 
ADS 
CAS 

Google Scholar 
Erlebach, A. et al. A reactive neural network framework for water-loaded acidic zeolites. Nat Commun 15, 4215 https://doi.org/10.1038/s41467-024-48609-2 (2024).Trinh, T. T., Jansen, A. P. & Van Santen, R. A. Mechanism of oligomerization reactions of silica. J. Phys. Chem. B 110, 23099–23106 (2006).Article 
CAS 
PubMed 

Google Scholar 
Zhang, X. Q., Trinh, T. T., Van Santen, R. A. & Jansen, A. P. Mechanism of the initial stage of silicate oligomerization. J. Am. Chem. Soc. 133, 6613–6625 (2011).Article 
CAS 
PubMed 

Google Scholar 
Schaffer, C. L. & Thomson, K. T. Density functional theory investigation into structure and reactivity of prenucleation silica species. J. Phys. Chem. C. 112, 12653–12662 (2008).Article 
CAS 

Google Scholar 
Pereira, J. C., Catlow, C. R. & Price, G. D. Silica condensation reaction: an ab initio study. Chemical Communications 1387–1388 https://pubs.rsc.org/en/content/articlelanding/1998/cc/a801816b (1998).Elanany, M. et al. A quantum molecular dynamics simulation study of the initial hydrolysis step in sol-gel process. J. Phys. Chem. B 107, 1518–1524 (2003).Article 
CAS 

Google Scholar 
Schwalbe-Koda, D., Tan, A. R. & Gómez-Bombarelli, R. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks. Nat. Commun. 12, 1–12 (2021).Article 

Google Scholar 
Landrum, G. RDKit: Open-source cheminformatics www.rdkit.org (2006).Baerlocher, Ch. and McCusker, L.B. Database of Zeolite Structures http://www.iza-structure.org/databases/ (2021).Neese, F., Wennmohs, F., Becker, U. & Riplinger, C. The ORCA quantum chemistry program package. J. Chem. Phys. 152, 224108 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Morawietz, T., Singraber, A., Dellago, C. & Behler, J. How van der waals interactions determine the unique properties of water. Proc. Natl Acad. Sci. USA 113, 8368–8373 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. 34th Int. Conf. Mach. Learn., ICML 7, 4844–4866 (2017).
Google Scholar 
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic Attribution for Deep Networks. 34th Int. Conf. Mach. Learn., ICML 7, 5109–5118 (2017).
Google Scholar 
Fu, X. et al. Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations https://arxiv.org/abs/2210.07237v1 (2022).Skinner, L. B. et al. Benchmark oxygen-oxygen pair-distribution function of ambient water from x-ray diffraction measurements with a wide Q-range. 138, 074506 http://aip.scitation.org/doi/10.1063/1.4790861 (2013).Marsalek, O. & Markland, T. E. Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects. J. Phys. Chem. Lett. 8, 1545–1551 (2017).Article 
CAS 
PubMed 

Google Scholar 
Holz, M., Heil, S. R. & Sacco, A. Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements. Phys. Chem. Chem. Phys. 2, 4740–4742 (2000).Article 
CAS 

Google Scholar 
Easteal, A. J., Price, W. E. & Woolf, L. A. Diaphragm cell for high-temperature diffusion measurements. Tracer Diffusion coefficients for water to 363 K. J. Chem. Soc., Faraday Trans. 1: Phys. Chem. Condens. Phases 85, 1091–1097 (1989).Article 
CAS 

Google Scholar 
Ceriotti, M., Parrinello, M., Markland, T. E. & Manolopoulos, D. E. Efficient stochastic thermostatting of path integral molecular dynamics. J. Chem. Phys. 133, 124104 (2010).Article 
ADS 
PubMed 

Google Scholar 
Erlebach, A., Nachtigall, P. & Grajciar, L. Accurate large-scale simulations of siliceous zeolites by neural network potentials. npj Comput Mater 8, 174 https://doi.org/10.1038/s41524-022-00865-w (2022).Silverstein, T. P. & Heller, S. T. PKa Values in the Undergraduate Curriculum: What Is the Real pKa of Water? J. Chem. Educ. 94, 690–695 (2017).Article 
CAS 

Google Scholar 
Ceriotti, M. et al. Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges. Chem. Rev. 116, 7529–7550 (2016).Article 
CAS 
PubMed 

Google Scholar 
Wang, R., Carnevale, V., Klein, M. L. & Borguet, E. First-Principles Calculation of Water p Ka Using the Newly Developed SCAN Functional. J. Phys. Chem. Lett. 11, 54–59 (2020).Article 
CAS 
PubMed 

Google Scholar 
Perry, C. C. Biogenic Silica: A Model of Amorphous Structure Control. Growth, Dissolution and Pattern Formation in Geosystems 237–251 https://link.springer.com/chapter/10.1007/978-94-015-9179-9_11 (1999).Belton, D. J., Deschaume, O. & Perry, C. C. An overview of the fundamentals of the chemistry of silica with relevance to biosilicification and technological advances. FEBS J. 279, 1710–1720 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schütt, K., Unke, O. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In Proc. 38th International Conference on Machine Learning, Proc. Machine Learning Research Vol. 139 (eds Meila, M. & Zhang, T.) 9377–9388 (PMLR, 2021).Batzner, S. et al. E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. Nat. Commun. 2022 13:1 13, 1–11 (2021).
Google Scholar 
Musaelian, A. et al. Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics http://arxiv.org/abs/2204.05249 (2022).Schütt, K. T. et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Adv. Neural Inf. Process. Syst. 30, 992–1002 (2017).
Google Scholar 
Subramaniyan, A. K. & Sun, C. T. Continuum interpretation of virial stress in molecular simulations. Int. J. Solids Struct. 45, 4340–4346 (2008).Article 

Google Scholar 
SigOpt. https://sigopt.com/.Martinez, L., Andrade, R., Birgin, E. G. & Martínez, J. M. PACKMOL: a package for building initial configurations for molecular dynamics simulations. J. Comput.Chem. 30, 2157–2164 (2009).Article 
CAS 
PubMed 

Google Scholar 
Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool. Model. Simul. Mater. Sci. Eng. 18, 015012 (2009).Article 
ADS 

Google Scholar 
Yeh, I. C. & Hummer, G. System-size dependence of diffusion coefficients and viscosities from molecular dynamics simulations with periodic boundary conditions. J. Phys. Chem. B 108, 15873–15879 (2004).Article 
CAS 

Google Scholar 
Schütt, K. T. et al. SchNetPack: A Deep Learning Toolbox for Atomistic Systems. J. Chem. Theory Comput. 15, 448–455 (2019).Article 
PubMed 

Google Scholar 
Ong, S. P. et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).Article 
CAS 

Google Scholar 
Gaillac, R., Pullumbi, P. & Coudert, F. X. ELATE: an open-source online application for analysis and visualization of elastic tensors. J. Phys.: Condens. Matter 28, 275201 (2016).PubMed 

Google Scholar 
Shirts, M. R. & Chodera, J. D. Statistically optimal analysis of samples from multiple equilibrium states https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2671659/pdf/JCPSA6-000129-124105_1.pdf (2008).Dietschreit, J. C., Diestler, D. J. & Ochsenfeld, C. How to obtain reaction free energies from free-energy profiles. J. Chem. Phys. 156, 114105 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hulm, A., Dietschreit, J. C. & Ochsenfeld, C. Statistically optimal analysis of the extended-system adaptive biasing force (eABF) method. J. Chem. Phys. 157, 24110 (2022).Article 
CAS 

Google Scholar 
silica-water dataset with potentials. Mater Data Facility https://doi.org/10.18126/pzjr-x7pv.Roy, S. Figure_Source_Data https://figshare.com/articles/dataset/Figure_Source_Data/25928506 (2024).simonaxelrod et al.learningmatter-mit/NeuralForceField: NeuralForceField with Uncertainty attribution https://doi.org/10.5281/zenodo.11391758 (2024).Coudert, F. X. Systematic investigation of the mechanical properties of pure silica zeolites: stiffness, anisotropy, and negative linear compressibility. Phys. Chem. Chem. Phys. 15, 16012–16018 (2013).Article 
CAS 
PubMed 

Google Scholar