AI-supported quantum multiscale modeling and nanostructured material design for artificial photosynthesis systems

Abstract

A system (100) for modeling and optimizing artificial photosynthesis for the development of materials and devices for converting solar energy into fuel energy, comprising: • a quantum multiscale modeling module (110) configured to simulate the electronic and catalytic properties of photoactive materials; • a photoelectrode design module (120) configured to design and optimize photoanodes and photocathodes for photoelectrochemical reactions; • a module (130) for the fabrication of nanostructured material architectures designed for improved light absorption and catalytic performance; • a hybrid organic-inorganic interface module (140) configured to optimize charge transfer between organic molecules and inorganic semiconductor materials; • an AI optimization engine (150) configured to analyze simulation data and identify optimal material configurations; and • a simulation module (160) for converting solar energy into fuels, configured to simulate the operation of artificial photosynthesis systems for hydrogen production or carbon dioxide reduction.

Description

Application area of ​​the invention

[0001] The present invention relates generally to the field of renewable energy systems and computer-aided materials development. In particular, it relates to an integrated artificial photosynthesis system for the modeling, simulation, and optimization of materials used in solar energy conversion. Specifically, it is an intelligent computer system that integrates quantum simulation modules at various scales, components for the development of nanostructured materials, hybrid organic-inorganic interfaces, and AI optimization modules to develop and optimize materials for artificial photosynthesis devices.

[0002] The system is configured to simulate and analyze critical processes of artificial photosynthesis. These include light absorption, electron-hole pair generation, charge separation and transport, and catalytic reactions such as water splitting and carbon dioxide reduction. By combining advanced modeling techniques with AI-supported optimization, the system enables the development and design of highly efficient photoanodes, photocathodes, and photocatalytic materials for solar fuel production systems. The invention thus relates to a renewable energy system for the intelligent development and optimization of materials and devices for artificial photosynthesis, enabling the scalable and efficient production of clean chemical fuels such as hydrogen and hydrocarbons from solar energy. Background of the invention

[0003] In light of rising energy consumption, climate change, and the depletion of fossil fuel reserves, the global demand for sustainable, renewable, and environmentally friendly energy sources has increased significantly. Conventional energy systems based on fossil fuels such as coal, oil, and natural gas are the primary sources of greenhouse gas emissions and pollutants. Therefore, there is a growing international effort to develop alternative energy technologies to produce clean fuels while simultaneously reducing carbon emissions. One promising approach is the further development of artificial photosynthesis systems. These systems aim to convert solar energy into chemical fuels, similar to the photosynthesis process in plants and microorganisms.

[0004] Artificial photosynthesis uses sunlight to control chemical reactions, producing energy molecules such as hydrogen and hydrocarbons. By converting solar energy into chemical bonds, artificial photosynthesis creates a much-needed space for energy storage. The processing and production of fuels makes it possible to store, transport, and use energy when needed, eliminating the need for complex storage facilities. Therefore, artificial photosynthesis is considered a promising technology for sustainable, zero-emission fuel production.

[0005] The basic principle of artificial photosynthesis is largely based on its function in living organisms such as plants, algae, and some bacteria. In these biological systems, photons from sunlight are absorbed by pigment molecules like chlorophyll. There, they trigger complex photochemical reactions that generate electron-hole pairs. These act as charge carriers and stimulate a series of redox reactions to convert water and carbon dioxide into oxygen and energy-rich organic molecules such as glucose. Natural photosynthesis provides a compelling biological model. However, when considering energy consumption, the overall efficiency of photosynthesis is only about one to two percent. This has been the main reason for favoring artificial systems to increase energy conversion efficiency.

[0006] The study of photosynthetic reactions received a significant boost in the early 1970s with the first demonstration of photoelectrochemical water splitting using semiconductor electrodes. This discovery, known as the Honda-Fujishima effect, showed that titanium dioxide (TiO2) electrodes can split water by reacting to ultraviolet (UV) radiation with the half-reactions of hydrogen and oxygen evolution. This groundbreaking demonstration of harnessing solar energy through a semiconductor material to control a technologically important chemical reaction—namely, fuel production—was revolutionary. Since then, intensive research has been conducted in photocatalysis, photoelectrochemistry, and semiconductor materials science to improve the efficiency and practicality of these artificial photosynthesis systems.

[0007] In the context of advances in artificial photosynthesis, researchers became familiar with a variety of materials for device fabrication in the following years: Si, CdS, TiO2, and BiVO4. These were investigated for their ability to absorb sunlight and generate charge carriers sufficient for catalytic reactions. Many catalysts were developed to support the three fundamental chemical processes: water splitting, proton reduction for hydrogen production, and CO2 reduction to hydrocarbons or alcohols. These central catalytic processes determine the efficiency and selectivity of solar fuel production in artificial photosynthesis systems.

[0008] Despite numerous advances in this field, significant technical challenges still hinder the large-scale application of artificial photosynthesis. A crucial factor is the low efficiency of many photoelectrochemical systems. Insufficient light absorption, rapid recombination of electron-hole pairs, and limited charge transport capacities impede high energy conversion into chemical fuels. Furthermore, many expensive or rare materials, primarily platinum, iridium, and ruthenium, impede the large-scale commercialization of high-performance and robust catalyst materials and drive up the associated costs.

[0009] A key challenge in ensuring the stability of photosynthetic materials in the environment is monitoring their stability over extended periods within devices. Photocatalytic materials are frequently exposed to corrosive operating conditions such as intense sunlight, moderate vapor concentrations, and reactive chemical intermediates—conditions that ultimately lead to material degradation, thereby reducing efficiency and limiting the device's lifespan. Furthermore, combining fundamental components like photoanodes, redox catalysts, and electrolytes into a stable and efficient system architecture remains a demanding engineering challenge.

[0010] Advances in nanotechnology have led to the development of various strategies to overcome these limitations. Nanostructured materials such as nanowires, nanoparticles, and quantum dots can be designed to optimize their electronic, optical, and catalytic properties, thereby achieving a new level of performance in artificial photosynthesis. For example, nanomaterials with a high surface-to-volume ratio achieve or surpass the catalytic activity of conventional materials due to their reactive surface atoms and electrons. Furthermore, the enhanced local electric fields in plasmonic gold and silver nanostructures contribute to improved light absorption or the photogeneration of charges in a photocatalytic structure.Hybrid systems based on organic molecules and inorganic semiconductors aim to increase the efficiency of electron transfer and catalysis.

[0011] However, given the progress made in developing better-prepared catalysts and semiconductors, envisioning a process for producing solar fuels is no longer so simple: it remains a highly technical and time-consuming undertaking. Efficient materials must first be identified before further optimizations can be made, requiring additional time, computing resources, and laboratory experiments.

[0012] Recent advances in computer-aided modeling—a suite of cutting-edge quantum simulation tools—provide a powerful mechanism for gaining a fundamental understanding of the electronic structure, surface binding, and catalysis of materials down to the atomic level. Density functional theory or molecular dynamics (MD) simulations enable the analysis of photoabsorption processes, charge carrier dynamics, and catalytic pathways, whose relevance is well established through the traditional study of experimental materials. However, they also form the basis for simulations that bridge the gap in predictive materials development—from photocatalysts to semiconductor materials.

[0013] The advent of artificial intelligence (AI) and machine learning (ML) has created promising conditions for faster and more effective dynamic modeling of the discovery and development of new materials. By analyzing extensive quantum simulations and experimentally acquired datasets, machine learning algorithms uncover patterns and predict potentially advantageous material compositions and configuration structures. Furthermore, machine learning and AI can optimize models to evaluate various parameters such as bandgap energy, catalytic activity, and material stability, thereby identifying the highest-performing materials for artificial photosynthesis systems.

[0014] Despite the successes achieved so far, there is still a need for a more holistic approach and a system that combines nanotechnology solutions and AI methods with quantum simulations. This comprehensive approach would advance artificial photosynthesis and thus further accelerate improvements in the efficiency of solar energy-to-fuel conversion – an area that has received too little attention to date.

[0015] Therefore, there is an integrated, multifaceted system for modeling and optimizing artificial photosynthesis that combines quantum mechanical multiscale simulations, the development of nanostructured materials, hybrid material interfaces, and artificial intelligence algorithms to create advanced materials with the necessary device architectures, efficient catalysts, and contaminant-free internal interfaces for the production of clean chemical fuels using solar energy. Summary of the invention

[0016] The integrated artificial photosynthesis system of the present invention enables researchers to create models and perform simulations while exploring and developing materials for systems that convert solar energy into fuels. The system combines quantum mechanical multiscale simulations with nanostructured material development and hybrid material interfaces, as well as AI-based optimization techniques, to optimize artificial photosynthesis technologies through improved efficiency and scalability, as well as reduced operating costs.

[0017] The invention provides a computer-aided design system that enables users to understand key photoelectrochemical processes that control artificial photosynthesis systems. The system models all essential processes that occur through light absorption and generate electron-hole pairs before these migrate through the system to complete catalytic processes such as water splitting and carbon dioxide reduction. Through precise process modeling, the system facilitates material development and thus contributes to higher efficiency in solar power-to-fuel conversion.

[0018] The invention utilizes semiconductor materials with nanostructures such as nanowires, quantum dots, nanoparticles, and thin films to improve light absorption, catalytic surface area, and charge transport in photoelectrochemical systems. The nanostructured architecture, which absorbs more solar radiation while minimizing the loss of photogenerated charge carriers through recombination, results in higher system performance.

[0019] The invention improves electron transfer efficiency and catalytic activity through the use of hybrid organic-inorganic material interfaces. These hybrid systems make it possible to tailor the optical and chemical properties of organic molecules while maintaining the stability and conductivity of inorganic semiconductor materials. The interfaces enable efficient charge transfer from photoactive materials to catalytic centers, resulting in faster chemical reactions and higher selectivity. By reducing reliance on conventional trial-and-error experiments, the system significantly accelerates the development of high-performance artificial photosynthesis devices.

[0020] In some embodiments, the system is configured to develop and optimize photoanodes and photocathodes for photoelectrochemical cells used for hydrogen production or carbon dioxide reduction. The optimized materials enable improved catalytic activity, increased stability under operating conditions, and reduced reliance on expensive or rare metals.

[0021] The advantage of the invention is that the integrated system provided in the present invention enables the development of efficient, scalable, and economically viable artificial photosynthesis technologies capable of producing clean chemical fuels directly from sunlight. The invention thus contributes to the advancement of renewable energy technologies and supports global efforts toward sustainable energy production and the manufacture of climate-neutral fuels. Detailed description of the invention

[0022] The present invention relates to an integrated system (100) for modeling and optimizing artificial photosynthesis. This system is used for the simulation, design, and optimization of materials and components for technologies that convert solar energy into fuels. The system (100) integrates quantum mechanical multiscale simulation techniques, the development of nanostructured materials, hybrid organic-inorganic material interfaces, and optimization modules with artificial intelligence. The aim is to develop highly efficient artificial photosynthesis devices capable of converting solar energy into chemical fuels such as hydrogen or hydrocarbons. The system addresses key challenges in artificial photosynthesis, including low energy conversion efficiency, material instability, and high catalyst costs.

[0023] The system for modeling and optimizing artificial photosynthesis (100) consists of several interconnected modules that together simulate and optimize the physical and chemical processes of artificial photosynthesis. These modules include a quantum mechanical multiscale modeling module (110), a photoelectrode development module (120), a nanostructured materials development module (130), a hybrid organic-inorganic interface module (140), an AI-supported optimization engine (150), and a simulation module for converting solar energy into fuel (160).

[0024] The module for quantum mechanical multiscale modeling (110) is used to model the electronic structure, optical properties, and catalytic performance of materials that researchers select for testing in artificial photosynthesis systems. The module employs advanced computational methods such as density functional theory (DFT), time-dependent density functional theory (TD-DFT), and molecular dynamics simulations to investigate the electronic band structure, charge density distribution, exciton generation, and catalytic reaction pathways of semiconductor materials. Using these simulations, the module predicts material behavior under solar irradiation and assesses its potential as components in artificial photosynthesis devices.

[0025] The system (100) comprises a photoelectrode design module (120) that enables the development and improvement of photoelectrochemical electrodes for artificial photosynthesis systems. The photoelectrode design module (120) consists of a photoanode and a photocathode. The photoanode enables water splitting reactions, producing oxygen, protons, and electrons. The photocathode enables reduction reactions, including hydrogen production through proton reduction and hydrocarbon production through carbon dioxide reduction. The photoelectrode design module optimizes semiconductor materials and catalytic surfaces to achieve maximum photon absorption, charge separation efficiency, and catalytic reaction performance.

[0026] The system (100) includes a module (130) for the development of nanostructured materials, which designs and evaluates materials with nanoscale structures for improved artificial photosynthesis. The module generates or analyzes materials such as semiconductor nanowires, quantum dots, plasmonic nanoparticles, and nanostructured thin films. The nanostructured materials offer superior optical absorption properties, an increased catalytic surface area, and improved charge transport properties. By employing nanostructured architectures in photoelectrodes, the system achieves improved photon yield and reduced recombination losses of electron-hole pairs.

[0027] In some embodiments, the system (100) further comprises a hybrid organic-inorganic interface module (140) that integrates organic molecules with inorganic semiconductor materials to form hybrid interfaces. The hybrid interface module can contain organic dye sensitizers, molecular catalysts, and inorganic semiconductor substrates to enhance charge transfer efficiency and catalytic activity. Organic molecules provide tunable optical properties and selective catalytic behavior, while inorganic semiconductors ensure structural stability and efficient charge transport pathways. The hybrid interface module optimizes the electronic coupling between organic and inorganic components to enable efficient transfer of photogenerated electrons from light-absorbing materials to catalytic reaction centers.

[0028] The system (100) also includes an AI optimization engine (150) that accelerates the discovery and optimization of materials for artificial photosynthesis. The AI ​​engine analyzes large datasets from quantum simulations and experimental studies to identify optimal material compositions and device architectures. Machine learning algorithms enable high-throughput material screening, multi-criteria optimization of material properties, and the prediction of new materials with improved photocatalytic performance. The AI ​​optimization engine can also control automated experimental processes by recommending optimal synthesis conditions and operating parameters.

[0029] The system also includes a simulation module (160) for converting solar energy into fuel, which simulates the operation of artificial photosynthesis plants. The simulation module models the sequence of physical and chemical processes that occur during solar fuel production.

[0030] The processes begin with semiconductor materials that absorb photons and generate electron-hole pairs through photon excitation. The simulation results enable the module to evaluate system performance while simultaneously identifying optimal material configurations for the development of solar-based fuel technologies. The Artificial Photosynthesis Modeling and Optimization System (100) processes incoming candidate material structures along with device architectures. The Quantum Mechanical Multiscale Modeling Module (110) analyzes the electronic and catalytic properties of the candidate materials. The Photoelectrode Development Module (120) generates optimized photoanode and photocathode structures, while the Nanostructured Materials Development Module (130) introduces nanostructures to enhance light absorption and catalytic activity.The module for hybrid organic-inorganic interfaces (140) optimizes material interfaces for efficient charge transfer. The AI ​​optimization engine (150) analyzes the simulation results to identify the most promising material configurations. The simulation module for converting solar energy into fuel (160) evaluates the expected results of the optimized artificial photosynthesis system.

[0031] The invention creates a comprehensive system that combines quantum simulation with nanotechnology, hybrid materials development, and artificial intelligence to develop advanced artificial photosynthesis devices that produce clean chemical fuels using solar energy. The system improves the conversion efficiency of solar energy into fuel, reduces reliance on expensive catalysts, accelerates materials development, and enables scalable technologies for the production of renewable fuels.

Claims

[1] A system (100) for modeling and optimizing artificial photosynthesis for the development of materials and devices for converting solar energy into fuel energy, comprising: • a quantum multiscale modeling module (110) configured to simulate the electronic and catalytic properties of photoactive materials; • a photoelectrode design module (120) configured to design and optimize photoanodes and photocathodes for photoelectrochemical reactions; • a module (130) for the fabrication of nanostructured material architectures designed for improved light absorption and catalytic performance; • a hybrid organic-inorganic interface module (140) configured to optimize charge transfer between organic molecules and inorganic semiconductor materials; • an AI optimization engine (150) configured to analyze simulation data and identify optimal material configurations; and • a simulation module (160) for converting solar energy into fuels, configured to simulate the operation of artificial photosynthesis systems for hydrogen production or carbon dioxide reduction. [2] System according to claim 1, wherein the quantum multiscale modeling module (110) uses computational methods including density functional theory (DFT), time-dependent density functional theory (TD-DFT) and molecular dynamics simulations to analyze electronic structures and charge transfer mechanisms. [3] System according to claim 1, wherein the photoelectrode design module (120) comprises a photoanode configured for water splitting reactions and a photocathode configured for hydrogen evolution or carbon dioxide reduction reactions. [4] System according to claim 1, wherein the module for producing nanostructured materials (130) produces or evaluates nanostructured materials selected from semiconductor nanowires, quantum dots, plasmonic nanoparticles and nanostructured thin films. [5] System according to claim 1, wherein the hybrid organic-inorganic interface module (140) integrates organic dye sensitizers or molecular catalysts with inorganic semiconductor substrates to improve charge transfer efficiency and catalytic activity. [6] System according to claim 1, wherein the artificial intelligence optimization engine (150) applies machine learning algorithms to perform high-throughput material screening and multi-criteria optimization of materials for artificial photosynthesis. [7] System according to claim 1, wherein the solar-to-fuel conversion simulation module (160) models photon absorption, electron-hole pair generation, charge separation and charge transport, as well as the catalytic reactions occurring at the photoelectrodes. [8] System according to claim 1, wherein the artificial intelligence optimization engine (150) predicts optimal material compositions and structural configurations that maximize the conversion efficiency of solar energy into fuel. [9] System according to claim 1, wherein the artificial photosynthesis system is configured to produce hydrogen as fuel by photocatalytic water splitting reactions. [10] System according to claim 1, wherein the artificial photosynthesis system is configured to convert carbon dioxide into hydrocarbon fuels by photocatalytic reduction reactions, thereby enabling climate-neutral energy production.

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