Alchemical perturbation density functional theory

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

doi:10.1103/physrevresearch.2.023220

Cited by 43 publications

(90 citation statements)

References 54 publications

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“…Hence, while the corresponding electronic Hamiltonians are alchemical mirror images of each other, their respective solutions to the electronic Schrödinger equation are not necessarily equal. More specifically, for reflections around atoms with identical chemical environments, we show that the electronic energy of corresponding alchemical enantiomers must be degenerate up to the third order within alchemical perturbation DFT (APDFT) ( 31 ) (see the “Materials and Methods” section below), higher-order terms carrying different signs. Hence, approximate alchemical enantiomers are only approximately degenerate in their electronic energy.…”

Section: Resultsmentioning

confidence: 99%

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Rudorff

Lilienfeld

2021

Sci. Adv.

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Brute-force compute campaigns relying on demanding ab initio calculations routinely search for previously unknown materials in chemical compound space (CCS), the vast set of all conceivable stable combinations of elements and structural configurations. Here, we demonstrate that four-dimensional chirality arising from antisymmetry of alchemical perturbations dissects CCS and defines approximate ranks, which reduce its formal dimensionality and break down its combinatorial scaling. The resulting “alchemical” enantiomers have the same electronic energy up to the third order, independent of respective covalent bond topology, imposing relevant constraints on chemical bonding. Alchemical chirality deepens our understanding of CCS and enables the establishment of trends without empiricism for any materials with fixed lattices. We demonstrate the efficacy for three cases: (i) new rules for electronic energy contributions to chemical bonding; (ii) analysis of the electron density of BN-doped benzene; and (iii) ranking over 2000 and 4 million BN-doped naphthalene and picene derivatives, respectively.

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Section: Resultsmentioning

confidence: 99%

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Rudorff

Lilienfeld

2021

Sci. Adv.

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“…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

703

443

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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.

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“…First order APDFT approximations require relatively simple arithmetic operations, and they bring effectively no additional computational cost once a reference calculation has been calculated with a quantum mechanics method such as DFT. Non‐linear contributions in principle can also be treated using higher order terms, 20 but these require more quantum mechanics calculations. Note that throughout the rest of this work, “APDFT” will refer to APDFT truncated to first order.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative approach that technically requires no a priori model training is alchemical perturbation density functional theory (APDFT). [17][18][19][20] This approach is sometimes referred to as "computational alchemy," but it should not be confused with computational schemes where atomic or molecular interactions are gradually tuned out over the course of a molecular dynamics simulation to define convenient paths for carrying out thermodynamic integration. 21 In contrast, APDFT is generally carried out with static quantum mechanics calculations and is a means to predict a hypothetical energy contribution based on a perturbation on a reference system.…”

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confidence: 99%

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

et al. 2020

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Alchemical perturbation density functional theory (APDFT) has promise for enabling computational screening of hypothetical catalyst sites. Here, we analyze errors in first order APDFT calculation schemes for binding energies of CH x , NH x , OH x , and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone approximates accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn-Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT extends this number to more than 20,000 and provides a recipe for developing other machine learning-based APDFT models. K E Y W O R D S adsorption, binding energies, high throughput screening 1 | INTRODUCTION Many efforts in computational catalysis are focused on screening materials for innovative catalysts that promote high chemical activity. 1-3 Descriptors for catalyst activity, for example, an adsorbate binding

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Alchemical perturbation density functional theory

Cited by 43 publications

References 54 publications

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Recent developments in the PySCF program package

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

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