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Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML

Journal Article · · Journal of Chemical Theory and Computation
 [1];  [2];  [3];  [4]
  1. Univ. of Vienna (Austria)
  2. Univ. of Toronto, ON (Canada); Technische Univ. Berlin (Germany); Institute for the Foundations of Learning and Data, Berlin (Germany); Vector Institute for Artificial Intelligence, Toronto, ON (Canada)
  3. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  4. Argonne National Laboratory (ANL), Argonne, IL (United States)
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In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With O(N3) scaling with the number of electrons N, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Finally, numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); European Research Council (ERC); Swiss National Science Foundation (SNSF)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2429573
Journal Information:
Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 6 Vol. 19; ISSN 1549-9618
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY