Polish Research Infrastructure Network for science supported by artificial intelligence

The project is co-financed by the European Union under Measure 2.4 of the European Funds for a Modern Economy program, No. FENG.02.04-IP.04-0019/24.

The goal of the “Polish Network of Research Infrastructure for Artificial Intelligence-Assisted Science (PLAI4SCIENCE)” project is to create a unique research infrastructure to support the development of science, particularly physics and chemistry, using artificial intelligence (AI) and machine learning (ML). The main application of this infrastructure is to create a computational platform and measurement stations for the scientific community and business entities, providing tools for:

1. ML-assisted material simulations: study of the properties of molecules and nanostructures; study of the properties of low-dimensional optoelectronic systems; development and use of AI/ML-assisted quantum chemical and simulation methods to reduce the cost of theoretical calculations and enable simulation of large systems that are difficult to process with currently available quantum chemical methods. Commercial applications: predicting the properties of multi-electron systems, computational chemistry, spectroscopic calculations, materials engineering, molecular dynamics, drug design, material identification for the photovoltaic, spintronics and organic electronics industries.

2. Molecular spectroscopy and photonic metrology: use of optical resonant cavities, ultraprecision spectroscopy, optical frequency combs to measure material properties and ultrafast processes and validate spectroscopic models calculated using AI methods and ML models, “smart” light sources. Commercial applications: characterization of materials for the semiconductor and optoelectronics sectors, generation of reference data for atmospheric monitoring and trace detection systems, process monitoring, biomedical diagnostics, precision characterization of laser systems.

3. Measurements using spatial-spectral imaging: hyperspectral imaging with ML models for detection, segmentation and classification of spectra, and dedicated computer vision models. Commercial applications: environmental and phenomenon monitoring, quality control (e.g., food), non-contact substance detection and identification, medical diagnostics.

4. Uses of explainable AI and ML methods in the sciences: specialized algorithms and models, both classical and deep neural network architectures, e.g. graph networks and language models, as well as tools for model learning and reinforcement learning. A component of the infrastructure is an advanced computing environment with high-powered clusters and corresponding software.

The project involves the purchase or development of the following components:

- Computing cluster - the basis of the platform to be launched
- Software platform for AI and ML-supported scientific computing
- A software package for simulating the dynamics of the interaction of atomic systems with light
- Application package for accurate ab initio and KS-DFT quantum-chemical calculations supported by AI methods
- Testbed for testing and development of electro-optical laser light modulation systems
- Optical system for precise measurements of human eye dynamics
- High-performance workstation for mass processing of imaging biomedical and structural data
- Prototyping and fabrication workstation for mechanical systems
- Methods for acquiring, analyzing and synthesizing biomedical data provided by the optical system for precise measurements of human eye dynamics
- License for a software environment to control the measurement bench
- CAD/CAM software package
- Dual-beam spectrometer for the mid-infrared range based on Cr:ZnS/Se lasers
- System for multi-parametric optimization of AI-based laser pulse generation and propagation process
- Broadband spectrometer for THz and far-infrared wavelength range (λ>25 µm) in dual-wavelength configuration
- AI computing node
- AI tool development server
- Testbed for research and development of hyperspectral image processing methods
- Platform for spatial-spectral processing and classification of hyperspectral images
- Explanatory artificial intelligence platform for scalable data processing and analysis
- Infrastructure for materials simulation research based on machine learning
- Artificial intelligence methods for data analysis in science and life sciences
- An interface for integrating the project's data platform with EOSC and OpenAire
- A repository for artificial intelligence data and models
- Software platform for AI and ML-supported scientific computing

The results of the project are aimed at scientists conducting research in physics, chemistry and other fields requiring analysis and processing of large data sets.

IITIS PAN is the coordinator of the project, which also involves Nicolaus Copernicus University in Torun, Wroclaw University of Technology and the Institute of Bioorganic Chemistry of the Polish Academy of Sciences, Poznan Supercomputing and Networking Center.

The total cost of the Project is PLN 92,635,716.67, with funding of PLN 69,709,425.54.

#EuropeanFunds #EUFunds

 

 

  

 

Numer projektu: 

FENG.02.04-IP.04-0019/24

Termin: 

01/01/2025 to 31/12/2029

Typ projektu: 

Inne

Kierownik projektu: 

Krzysztof Grochla, DSc

Wykonawcy projektu: 

Michał Romaszewski, PhD
Przemysław Głomb, PhD

Historia zmian

Data aktualizacji: 08/01/2025 - 08:39; autor zmian: mgr inż. Ewelina Szweda (eszweda@iitis.pl)

The project is co-financed by the European Union under Measure 2.4 of the European Funds for a Modern Economy program, No. FENG.02.04-IP.04-0019/24.

The goal of the “Polish Network of Research Infrastructure for Artificial Intelligence-Assisted Science (PLAI4SCIENCE)” project is to create a unique research infrastructure to support the development of science, particularly physics and chemistry, using artificial intelligence (AI) and machine learning (ML). The main application of this infrastructure is to create a computational platform and measurement stations for the scientific community and business entities, providing tools for:

1. ML-assisted material simulations: study of the properties of molecules and nanostructures; study of the properties of low-dimensional optoelectronic systems; development and use of AI/ML-assisted quantum chemical and simulation methods to reduce the cost of theoretical calculations and enable simulation of large systems that are difficult to process with currently available quantum chemical methods. Commercial applications: predicting the properties of multi-electron systems, computational chemistry, spectroscopic calculations, materials engineering, molecular dynamics, drug design, material identification for the photovoltaic, spintronics and organic electronics industries.

2. Molecular spectroscopy and photonic metrology: use of optical resonant cavities, ultraprecision spectroscopy, optical frequency combs to measure material properties and ultrafast processes and validate spectroscopic models calculated using AI methods and ML models, “smart” light sources. Commercial applications: characterization of materials for the semiconductor and optoelectronics sectors, generation of reference data for atmospheric monitoring and trace detection systems, process monitoring, biomedical diagnostics, precision characterization of laser systems.

3. Measurements using spatial-spectral imaging: hyperspectral imaging with ML models for detection, segmentation and classification of spectra, and dedicated computer vision models. Commercial applications: environmental and phenomenon monitoring, quality control (e.g., food), non-contact substance detection and identification, medical diagnostics.

4. Uses of explainable AI and ML methods in the sciences: specialized algorithms and models, both classical and deep neural network architectures, e.g. graph networks and language models, as well as tools for model learning and reinforcement learning. A component of the infrastructure is an advanced computing environment with high-powered clusters and corresponding software.

The project involves the purchase or development of the following components:

- Computing cluster - the basis of the platform to be launched
- Software platform for AI and ML-supported scientific computing
- A software package for simulating the dynamics of the interaction of atomic systems with light
- Application package for accurate ab initio and KS-DFT quantum-chemical calculations supported by AI methods
- Testbed for testing and development of electro-optical laser light modulation systems
- Optical system for precise measurements of human eye dynamics
- High-performance workstation for mass processing of imaging biomedical and structural data
- Prototyping and fabrication workstation for mechanical systems
- Methods for acquiring, analyzing and synthesizing biomedical data provided by the optical system for precise measurements of human eye dynamics
- License for a software environment to control the measurement bench
- CAD/CAM software package
- Dual-beam spectrometer for the mid-infrared range based on Cr:ZnS/Se lasers
- System for multi-parametric optimization of AI-based laser pulse generation and propagation process
- Broadband spectrometer for THz and far-infrared wavelength range (λ>25 µm) in dual-wavelength configuration
- AI computing node
- AI tool development server
- Testbed for research and development of hyperspectral image processing methods
- Platform for spatial-spectral processing and classification of hyperspectral images
- Explanatory artificial intelligence platform for scalable data processing and analysis
- Infrastructure for materials simulation research based on machine learning
- Artificial intelligence methods for data analysis in science and life sciences
- An interface for integrating the project's data platform with EOSC and OpenAire
- A repository for artificial intelligence data and models
- Software platform for AI and ML-supported scientific computing

The results of the project are aimed at scientists conducting research in physics, chemistry and other fields requiring analysis and processing of large data sets.

IITIS PAN is the coordinator of the project, which also involves Nicolaus Copernicus University in Torun, Wroclaw University of Technology and the Institute of Bioorganic Chemistry of the Polish Academy of Sciences, Poznan Supercomputing and Networking Center.

The total cost of the Project is PLN 92,635,716.67, with funding of PLN 69,709,425.54.

#EuropeanFunds #EUFunds