AI-Enabled Acceleration of Spectroscopic Research and Materials Modelling

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of Applied Sciences titled “AI-Enabled Acceleration of Spectroscopic Research and Materials Modelling”.

Artificial intelligence (AI) is reshaping how scientific data are generated, analyzed, and translated into actionable knowledge across many experimental disciplines, including chemistry, physics, materials science, and the life sciences. AI-driven methods, ranging from classical machine learning to deep learning and large language models, enable closed-loop experimentation, advanced data interpretation, and automation of complex analytical workflows.

This Special Issue aims to bring together contributions that demonstrate how AI accelerates the full research pipeline—from data acquisition and processing to modeling, interpretation, and decision-making—in real-world scientific environments, including partially or fully autonomous laboratories.

The scope of the Special Issue includes, but is not limited to, the following topics:

• Machine learning and deep learning for scientific data analysis
  Supervised, unsupervised, and reinforcement learning methods for processing high-dimensional or multimodal experimental data.

• Large language models and agentic AI in scientific discovery
  LLM- and agent-based systems for hypothesis generation, literature mining, protocol design, and scientific knowledge extraction.

• Autonomous and self-driving laboratories
  AI-controlled experimental platforms, closed-loop optimization, robotic manipulation, and integrated sensing for accelerated discovery.

• AI-accelerated spectroscopic research and automated spectral interpretation
  Methods and systems that speed up spectroscopic measurements (e.g., vibrational, electronic, NMR, and MS) and automate peak assignment, feature extraction, and quantitative analysis.

• AI-enabled workflows for accelerated analytical and interpretative pipelines
  Design and deployment of end-to-end workflows that integrate data acquisition, preprocessing, modelling, uncertainty estimation, and reporting to reduce time to insight.

• AI-assisted structural, physical, and chemical characterization
  Applications of AI to diffraction, scattering, microscopy, imaging, and other structural or physical–chemical techniques, including structure–property relationships and inverse design.

• Physics-informed and knowledge-guided AI models
  Integration of domain knowledge, physical constraints, and mechanistic models with data-driven approaches to improve robustness, interpretability, and extrapolation.

• AI for materials, catalysis, and drug discovery
  Data-driven design and optimization of materials, catalysts, formulations, and bioactive compounds.

• AI in computational chemistry and materials modelling
  Machine learning accelerates materials discovery workflows involving density functional theory (DFT) by providing efficient surrogate models that approximate DFT-level accuracy, enabling high-throughput virtual screening without exhaustive DFT computations, and leveraging transfer learning from extensive DFT databases to predict molecular and material properties.

• Reproducibility, reliability, and ethical aspects of AI-driven research
  Benchmarks, best practices, standards, and governance for trustworthy AI in scientific applications.

• Case studies and real-world deployments
  Demonstrations of AI-driven discovery in industrial, academic, or clinical laboratory settings, including lessons learned from implementation and scale-up.

Dr. Marek Doskocz
Dr. Jacek Kujawski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.