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Title: First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

Journal Article · · International Journal of Quantum Chemistry
DOI:https://doi.org/10.1002/qua.24375· OSTI ID:1395172
 [1]
  1. Univ. of Basel, Basel (Switzerland); Argonne National Lab. (ANL), Argonne, IL (United States)

A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we give an introduction to an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of compound pairs, related to each other via “alchemical” interpolations involving fractional nuclear charges in the electronic Hamiltonian. We address Taylor expansions in CCS, property nonlinearity, improved predictions using reference compound pairs, and the ounce-of-gold prize challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. Here, these relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schrödinger equation.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Argonne National Laboratory, Argonne Leadership Computing Facility; USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1395172
Journal Information:
International Journal of Quantum Chemistry, Vol. 113, Issue 12; ISSN 0020-7608
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 98 works
Citation information provided by
Web of Science

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  • von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24912
journal April 2015
Crystal structure representations for machine learning models of formation energies journal April 2015
Machine learning for quantum mechanics in a nutshell journal July 2015
Exploring chemical space with alchemical derivatives: alchemical transformations of H through Ar and their ions as a proof of concept journal January 2019
Rapid and accurate molecular deprotonation energies from quantum alchemy journal January 2020
Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase journal January 2020
Simulated evolution of fluorophores for light emitting diodes journal March 2015
A fingerprint based metric for measuring similarities of crystalline structures journal January 2016
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity journal October 2016
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels journal June 2017
Alchemical and structural distribution based representation for universal quantum machine learning journal June 2018
Machine learning model for non-equilibrium structures and energies of simple molecules journal January 2019
Chemical transferability of functional groups follows from the nearsightedness of electronic matter journal October 2017
Representing molecular and materials data for unsupervised machine learning journal April 2018
Inverse molecular design using machine learning: Generative models for matter engineering journal July 2018
Mapping and classifying molecules from a high-throughput structural database journal February 2017
Conceptual density functional theory: status, prospects, issues journal January 2020
Quantum-chemical insights from deep tensor neural networks journal January 2017
Conceptual DFT: chemistry from the linear response function journal January 2014
Operators in quantum machine learning: Response properties in chemical space journal February 2019
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Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: Separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase text January 2020
Electronic spectra from TDDFT and machine learning in chemical space text January 2015
Crystal structure representations for machine learning models of formation energies text January 2015
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties text January 2015
Machine learning of molecular electronic properties in chemical compound space text January 2013
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