Digital Discovery
A journal for new thinking on machine learning, robotics and AI.
Open Access: Gold
Digital Discovery is an open access journal that publishes both theoretical and experimental research at the intersection of chemistry, materials science and biotechnology. We focus on the development and application of machine learning, AI and automation tools to unravel scientific problems, and we put data first to ensure reproducibility and faster progress for everyone. Chemists, biologists, engineers, physicists – if your work is driving digital transformation, you are home.
ISSN: 2635-098X
Indexed in: Web of Science: Emerging Sources Citation Index, Scopus, Directory of Open Access Journals (DOAJ)
Journal Impact factor
5.6 (2024)
First decision time (all)
37 days
First decision time (peer reviewed)
45 days
Scope
Digital Discovery welcomes both experimental and computational work on all topics related to the acceleration of discovery such as screening, robotics, databases and advanced data analytics, broadly defined, but anchored in chemistry.
The journal publishes research related to chemical, materials, biochemical, biomedical, or biophysical sciences and specific topics include:
Artificial intelligence and other high throughput computational methodologies for molecular, materials and formulation design:
- Computer-assisted retrosynthesis
- Generative models for scientific design
- Machine learning classification and regression models
- Quantum algorithms for quantum simulation and data science as applied to molecular and materials discovery
- Modern molecular, materials, and biological representations
- Molecular, Materials and Chemo- and Bio-informatics
- Methods for Bayesian optimization and design of experiments
- Advances and applications of interpretable models
- Image recognition
- Natural language processing
- Literature mining tools
Advanced data workflows:
- Databases
- Data provenance tools
- Computational workflow engines
- Experimental control software
- Ontologies for science
- Advances in robotics for science
Novel experimental automation:
- New robotic setups
- New automated sensors and analytical workflows
- Novel synthetic methodologies and workflows that enable higher throughput
- High-throughput computational science
- Studies where large families of electronic structure or molecular simulations are analyzed for use in experimental and automated applications
Papers at the interface of chemistry and other sciences that involve the following topics:
- Directed or accelerated evolution
- DNA Encoded Library Technology or novel chemical library technologies
- Cryptochemistry
- Blockchain-enabled science
Papers that will not be considered are in the areas of low-throughput structural or mechanistic studies using computational chemistry, QM/MM studies of biochemical mechanisms at low throughput, traditional analysis of molecular dynamics trajectory simulations to understand biological conformations, reports or comparisons of electronic structure methods that do not involve machine learning, interpretations of chemical bonding models, and quantum dynamics and spectroscopy simulations at low throughput.
The "digital transformation" of the chemical industry is a huge driver for the twenty-first century and we want this journal to be the premier venue for papers related to this topic.
Alán Aspuru-Guzik, Editor-in-Chief
Information for authors
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Meet the team
Read this journal
Paper
Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion batteries
Paper
A digital laboratory with a modular measurement system and standardized data format
Communication
BoTier: multi-objective Bayesian optimization with tiered objective structures
Paper
Natural language processing for automated workflow and knowledge graph generation in self-driving labs
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