ChemInform Abstract: THE PRINCIPLE OF MINIMUM CHEMICAL DISTANCE (PMCD)

AbstractThe principle of minimum chemical distance (PMCD) is derived from the general theory of the BE- and R-matrices, and makes precise the vague classical “principle of minimum structure change”. In fact, the PMCD may be seen as the principle of minimum structure change in mathematical terms. It provides a quantitative measure of chemical similarity of isomeric molecular systems. Its applications lie in the fields of correlations of substructures, elucidation of reaction mechanisms, and evaluation of synthetic pathways as is illustrated by examples. The mathematical foundations of the computer assisted application of the PMCD are presented.

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Spatial chemical distance based on atomic property fields

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-009-9316-x ◽

2010 ◽

Vol 24 (3) ◽

pp. 173-182 ◽

Cited By ~ 12

Author(s):

A. V. Grigoryan ◽

I. Kufareva ◽

M. Totrov ◽

R. A. Abagyan

Keyword(s):

Atomic Property ◽

Chemical Distance

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Asymptotic shape for the chemical distance and first-passage percolation on the infinite Bernoulli cluster

ESAIM Probability and Statistics ◽

10.1051/ps:2004009 ◽

2004 ◽

Vol 8 ◽

pp. 169-199 ◽

Cited By ~ 10

Author(s):

Olivier Garet ◽

Régine Marchand

Keyword(s):

First Passage Percolation ◽

First Passage ◽

Asymptotic Shape ◽

Chemical Distance

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DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

10.33774/chemrxiv-2021-5l2f8 ◽

2021 ◽

Author(s):

Sterling Baird ◽

Tran Diep ◽

Taylor Sparks

Keyword(s):

High Performance ◽

Pareto Front ◽

Screening Tool ◽

Chemical Space ◽

Chemical Compositions ◽

Trade Off ◽

High Performing ◽

Chemical Distance ◽

Weighted Score ◽

Unique Chemical

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

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