D³REAM: a framework for full-space inverse materials design with target properties via machine learning models and global optimization algorithms

Full-space inverse materials design (FSIMD) aims to identify materials with target properties by exploring the complete combinatorial space of possible elements, compositions, and structures, yet efficiently navigating the vast and high-dimensional materials space remains a fundamental challenge. Here, we present D³REAM (Data-Driven Design and Rapid Exploration for Advanced Materials), an open-source framework that unifies machine learning (ML) models and global optimization algorithms (OAs) for FSIMD with target properties. D³REAM integrates diverse universal ML interatomic potentials and universal property prediction models with multiple optimization strategies, including Bayesian optimization, swarm intelligence, and multi-objective OAs, to efficiently search for structures that meet specified target properties. Within D³REAM, a crystal structure is represented by three fundamental descriptors, i.e. atomic type (A), composition (C), and structural configuration (S). By adjusting the search domains of the (A, C, S) space, D³REAM can flexibly perform crystal structure prediction, variable-composition inverse materials design, and FSIMD. The framework combines ML with physics-informed search, and incorporates adaptive search-space pruning to construct a unified and efficient platform for accelerating materials discovery and optimization. Moreover, D³REAM is implemented in a modular and extensible architecture, enabling seamless integration with external simulation tools and OAs. In this paper, we focus on descriptions of the implementation and applications of the D³REAM framework.