SUSTECH

Accelerating SUStainable TECHnological trajectories with computational chemistry and machine learning

Overview

Modern innovation systems generate substantial societal benefits, yet they are also associated with significant and persistent harms. Exposure to hazardous chemicals alone is estimated to cause around 2 million deaths and 53 million disability-adjusted life years annually worldwide, while the economic burden of related health damages reaches a substantial share of GDP in advanced economies. These outcomes are not random: they reflect systematic patterns in how technological trajectories are selected and stabilized.

The ERC Synergy project SUSTECH investigates how technological trajectories emerge and why particular alternatives are selected over others. It addresses a central limitation in current research on innovation: most empirical approaches rely on indirect representations of technologies, such as patent classifications, citations, or text, rather than on the technologies themselves.

Scientific Approach

SUSTECH develops a new measurement framework that shifts attention from representations to the technological artifact itself. It combines computational chemistry and machine learning with the economics of innovation to derive detailed information on the properties of inventions directly from their underlying structure.

Using chemical inventions as an empirical setting, technologies can be represented through their molecular composition. This allows us to characterize inventions in terms of multiple intrinsic attributes, such as functional performance, environmental impact, or potential risks, rather than relying on coarse or one-dimensional classifications. 

By embedding these characteristics in a multidimensional space, the project enables a systematic analysis of technological trade-offs and of how different alternatives compare at early stages of development.

Research Focus

The analysis concentrates on the early phases of innovation, where key decisions shape long-term trajectories. It examines how scientists, firms, and institutions select among competing alternatives, and how these choices lead to persistent patterns of technological development.

Principal Investigators

The project is conducted by an interdisciplinary team:

 

 

Perspective

By linking molecular-level information with economic behavior, the project establishes a new empirical basis for studying technological change. It contributes to a more granular understanding of how innovation unfolds and opens new avenues for research on the directionality of technological development.