Atoms in Molecules from Alchemical Perturbation Density Functional Theory

Rudorff, Guido Falk von; Lilienfeld, O. Anatole von

doi:10.1021/acs.jpcb.9b07799

Cited by 29 publications

(30 citation statements)

References 105 publications

(195 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…λ = 0) only. In recent work, we have shown [14] that this infinite sum converges rather quickly, meaning that the first few terms recover the vast majority of the energy change between reference and target molecule and even allow decomposition into atomic energy as well as electron density contributions [44]. In practice, this means that its sufficient to evaluate the electron density and its first few derivatives for the base molecule only, so there is no combinatorial scaling with the total number of protonation sites in this method.…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate molecular deprotonation energies from quantum alchemy

Rudorff

Lilienfeld

2020

Phys. Chem. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

We assess the applicability of Alchemical Perturbation Density Functional Theory (APDFT) for quickly and accurately estimating deprotonation energies. We have considered all possible single and double deprotonations in one hundred small organic molecules drawn at random from QM9 [Ramakrishnan et al, JCTC 2015]. Numerical evidence is presented for 5'160 deprotonated species at both HF/def2-TZVP and CCSD/6-31G* level of theory. We show that the perturbation expansion formalism of APDFT quickly converges to reliable results: using CCSD electron densities and derivatives, regular Hartree-Fock is outperformed at second or third order for ranking all possible doubly or singly deprotonated molecules, respectively. CCSD single deprotonation energies are reproduced within 1.4 kcal/mol on average within third order APDFT. We introduce a hybrid approach were the computational cost of APDFT is reduced even further by mixing first order terms at a higher level of theory (CCSD) with higher order terms at a lower level of theory only (HF). We find that this approach reaches 2 kcal/mol accuracy in absolute deprotonation energies compared to CCSD at 2% of the computational cost of third order APDFT.

show abstract

Section: Introductionmentioning

confidence: 99%

Rapid and accurate molecular deprotonation energies from quantum alchemy

Rudorff

Lilienfeld

2020

Phys. Chem. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…131 In the linear scaling program LSQC, 132,133 PYSCF is used to generate reference wavefunctions and integrals for the cluster-in-molecule local correlation method. The APDFT (alchemical perturbation density functional theory) program 134,135 interfaces to PYSCF for QM calculations. In the PYSCF-NAO project, 136…”

Section: B External Projects That Build On Pyscfmentioning

confidence: 99%

Recent developments in the PySCF program package

Sun

Zhang

Banerjee

et al. 2020

The Journal of Chemical Physics

609

443

View full text Add to dashboard Cite

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science.

show abstract

“…For the easiest applications of APDFT, one can simply truncate the expansion in Equation ) to first order and assign Δ λ = 1, but higher‐order corrections for nonperiodic computational schemes have been developed by von Lilienfeld and co‐workers. [ 21 ] The first‐order approximation for APDFT is shown in Equation ).

\partial_{λ} normalΔ E^{0} = \sum_{I} normalΔ μ_{nI} \partial_{λ} N_{I} - \sum_{I} normalΔ F_{I} \partial_{λ} R_{I} + normalΔ μ_{e} \partial_{λ} N_{e}

…”

Section: Alchemical Perturbation Density Functional Theorymentioning

confidence: 99%

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Griego

Kitchin

Keith

2020

Int J of Quantum Chemistry

View full text Add to dashboard Cite

The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme, called alchemical perturbation density functional theory (APDFT), for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on the Kohn-Sham density functional theory (DFT) reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present benchmarking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH 4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are performed, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations. K E Y W O R D S adsorption energies, barrier heights, binding energies, computational catalysis, density functional theory, nudged elastic band calculations 1 | INTRODUCTION Advances in computational chemistry open new possibilities for impressively large-scale computational screening of hypothetical catalysts across materials space. [1-3] However, productively leveraging high-throughput screening has been challenging. For useful and insightful predictions, computational screening studies must be reproducible while also (a) determining important active sites that are stable under specified environmental conditions on large numbers of material compositions and (b) elucidating important elementary reaction steps with barrier heights that are

show abstract

Atoms in Molecules from Alchemical Perturbation Density Functional Theory

Cited by 29 publications

References 105 publications

Rapid and accurate molecular deprotonation energies from quantum alchemy

Rapid and accurate molecular deprotonation energies from quantum alchemy

Recent developments in the PySCF program package

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Contact Info

Product

Resources

About