Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

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Machine learning for molecular and materials science.

A method is presented based on graph theory for categorizing hydrogen-bond motifs in such a way that complex hydrogen-bond patterns can be disentangled, or decoded, systematically and consistently. This method is based on viewing hydrogen-bond patterns topologically as if they were intertwined nets with molecules as the nodes and hydrogen bonds as the lines. Surprisingly, very few parameters are needed to define the hydrogen-bond motifs comprising these networks. The methods for making these assignments, and examples of their chemical utility are given.

Graph-set analysis of hydrogen-bond patterns in organic crystals

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a s...

https://arxiv.org/pdf/cond-mat/0505185.pdf

Characterization of complex networks: A survey of measurements

The task of quantizing general relativity raises serious questions about the meaning of the present formulation and interpretation of quantum mechanics when applied to so fundamental a structure as the space-time geometry itself. This paper seeks to clarify the foundations of quantum mechanics. It presents a reformulation of quantum theory in a form believed suitable for application to general relativity. The aim is not to deny or contradict the conventional formulation of quantum theory, which has demonstrated its usefulness in an overwhelming variety of problems, but rather to supply a new, more general and complete formulation, from which the conventional interpretation can be deduced. The relationship of this new formulation to the older formulation is therefore that of a metatheory to a theory, that is, it is an underlying theory in which the nature and consistency, as well as the realm of applicability, of the older theory can be investigated and clarified.

"relative State" Formulation of Quantum Mechanics

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.

The Origins of Chemical Graph Theory Elements of Graph Theory for Chemists Nomenclature of Chemical Compounds Polynomials in Graph Theory Enumeration of Isomers Graph Theory and Molecular Orbitals

Chemical Group Theory: Introduction and Fundamentals

On the Complexity of Fullerenes and Nanotubes.- Complexity and Self-Organization in Biological Development and Evolution.- The Circle That Never Ends: Can Complexity be Made Simple?.- Graphs as Models of Large-Scale Biochemical Organization.- Quantitative Measures of Network Complexity.- Cellular Automata Models of Complex Biochemical Systems.- The Complex Nature of Ecodynamics.

Complexity in chemistry, biology, and ecology

Graph Theory and Topology in Chemistry

The outcome of an American Chemical Society Conference Symposium held in Los Angeles, Sept. 1988. Chemical graph theory is a branch of mathematical chemistry that has concerned itself with the mathematical description and characterization of molecular species, notational schemes and the enumeration

Computational chemical graph theory

A class of graph invariants known today as topological indices are being increasingly realized by chemists and others to be powerful tools in the description of chemical phenomena. Topological indices generally characterize both the size and shape of chemical species; in recent years a number of such indices have been put forward that sensitively reflect the amount of branching present in molecules. Chemists are thus able to model accurately the chemical behavior of an extensive range of chemical substances in all three thermodynamic states. In discussing the manifold applications of topological indices to the description of physicochemical properties, we present a survey of the progress to date in this area and point out some of the advantages and drawbacks of using topological indices.

Dennis H. Rouvray

Papers

Chemical Group Theory: Introduction and Fundamentals

Complexity in chemistry, biology, and ecology

Graph Theory and Topology in Chemistry

Computational chemical graph theory

The modeling of chemical phenomena using topological indices