Abstract
Soft matter embodies a wide range of materials, which all share the common characteristics of weak interaction energies determining their supramolecular structure. This complicates structure-property predictions and hampers the direct application of data-driven approaches to their modeling. We present several aspects in which these methods play a role in designing soft-matter materials: drug design as well as information-driven computer simulations, e.g., histogram reweighting. We also discuss recent examples of rational design of soft-matter materials fostered by physical insight and assisted by data-driven approaches. We foresee the combination of data-driven and physical approaches a promising strategy to move the field forward.
Highlights
Soft matter embodies a wide range of materials, which all share the common characteristics of weak interaction energies determining their supramolecular structure
We present several aspects in which these methods play a role in designing soft-matter materials: drug design as well as information-driven computer simulations, e.g., histogram reweighting
We highlight two sources of difficulties: (i) the accurate prediction of thermodynamic properties and (ii) the identification of clear descriptors for backward optimization. These difficulties are so severe that computational materials design has made relatively little progress in soft matter compared to other systems, e.g., thermoelectrics,[26] crystal-structure prediction,[27,28] or electrocatalytic materials.[29]
Summary
The fundamental laws of physics and chemistry provide us with constitutive laws and equations which can be used to predict material properties and to link them to the chemical composition and processing conditions. The correlations drawn call for a qualitative validation based on thorough physical and chemical understanding, thereby avoiding artifacts due to noisy or erroneous data We highlight two sources of difficulties: (i) the accurate prediction of thermodynamic properties and (ii) the identification of clear descriptors for backward optimization These difficulties are so severe that computational materials design has made relatively little progress in soft matter compared to other systems, e.g., thermoelectrics,[26] crystal-structure prediction,[27,28] or electrocatalytic materials.[29] The present review aims at bridging two largely disconnected fields, namely, computational materials design and soft matter, expected to rapidly grow closer with hardware and methodological developments. We conclude by proposing an outlook on data-driven methods in soft matter
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