cs.LGMar 31, 2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning

Authors: Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, 

Organizations: Institute for Materials Science and Max Bergmann Center of Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany · Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway · Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-University Bochum, 44780 Bochum, Germany · Ray Dolby Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0US. United Kingdom · 

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Abstract

Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.

Figures & tables

Figure 1: Scheme illustrating the sustainability topics discussed at the SusML workshop (Dresden, Germany) and in this Perspective, spanning the AI-driven discovery pipeline from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows.
Figure 2: Standard workflow for developing predictive models of molecular and materials properties, combining quantum-inspired representations with machine-learning (ML) techniques ( e.g. , neural networks, kernel methods, and tree-based models). Certain ML models can be made interpretable through additional explainability techniques, such as symbolic regression, attention mechanisms, and SHAP (SHapley Additive exPlanations) analysis..
Figure 3: Data-driven discovery of traditional vs . emerging 2D materials. ( a ) 2D materials such as graphene are traditionally exfoliated from naturally layered vdW compounds such as graphite. Large computational materials databases ( b ) have already fueled the discovery of several thousand such systems. For the emerging class of non-vdW 2D materials derived from non-layered crystals such as hematite ( α\alpha -Fe 2 O 3 ) ( c ), data-driven research and ML based modelling is still at an early stage. Colors: C brown, Fe gray, and O red.
Figure 4: General scheme of structure generation with generative models. Generative models are trained to learn the underlying distribution of a dataset of molecules or materials encoded in a representation, together with their conditioning label on desired properties (gray arrows). From a trained model, new data with the desired properties can be sampled from noise, along with the respective conditioning (blue arrows). The included histogram is reproduced from Ref. [ [ 401 ] ].
Figure 5: Classical and AI-powered approaches along the PSPP relationship [ 111 ] : Classical approaches (i) to connect the different parts range from quantum theory and molecular dynamics approaches up to continuum models. The AI-powered PSPP-chain reasoning process (ii) offers an alternative approach, where every connection is represented by a data-driven model. Models can also leapfrog steps, such as Processing-Property models [ 426 ] .
Figure 6: Conceptual overview of sustainable extractive language models for domain-specific applications in materials science: (a) examples of published domain-specific BERT models derived from structured datasets across different domains (e.g., batteries, photocatalysis, thermoelectrics, photovoltaics, and stress–strain properties); (b) conventional training process for a domain-specific language (BERT) model; (c) resource-efficient creation of domain-specific models via knowledge distillation; (d) integration of small, domain-specific BERT models as agents within an agentic AI workflow for data-driven materials discovery.
Figure 7: Data efficiency for machine-learned interatomic potential (MLIP) models. This schematic overview summarises two aspects, building upon a talk at the SusML workshop. The first aspect ( yellow ) is being efficient in the selection of training data; this comprises ( a ) the exploration of configurational space with evolving MLIP models and ( b ) the use of automated workflows. The second aspect ( blue ) is being efficient in the way that the models are fitted. The pre-training of graph-based models, and their subsequent fine-tuning on much smaller amounts of data (panel c ), is particularly timely in the age of large general-purpose (also referred to as ‘foundational’) MLIPs. Furthermore, evaluation speed at runtime can be improved by model distillation (panel d ): the sketch on the right illustrates a data-driven teacher–student approach [ 287 ] which uses an accurate, but slow model to create training data and fit a more specialised, much faster model to those. The drawing at the top is similar to one in a recent Comment article, first published in Ref. [ [ 40 ] ] by Springer Nature. Panels a and b are adapted from Ref. [ [ 260 ] ], which is published under a CC BY licence (http://creativecommons.org/licenses/by/4.0/).
Figure 8: Scheme illustrating the desired workflow for sustainable exploration of chemical space in the discovery of technological materials and therapeutics. The process begins with the definition of a scientific question, followed by the navigation of existing big data repositories and the reuse of computational workflows and general-purpose models through collaborative partnerships. When implemented correctly, this approach can culminate in the development of self-driving laboratories capable of addressing the original question at larger scales.
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