Abstract

The research and development (R&D) cycle of new materials lasts 10–20 years, which creates an increasingly acute contradiction with the demand for rapid iteration of material performance in strategic industries such as new energy, electronic information, and aerospace. This contradiction is forcing the transformation of material R&D from the traditional “trial-and-error” mode to a “rational design” paradigm. Based on the practical application scenarios of AI scientific research tools in high-throughput computing research of new materials, this paper systematically elaborates the mechanisms and paths of AI empowerment from four dimensions: applying AI tools to govern scattered and non-standard material data to form high-quality available data; adopting AI tools to automatically generate computing scripts and calibrate high-throughput computing output data, transforming manual operations into automated pipelines; utilizing AI tools to load pre-trained potential function models for simulation and approximation of real service condition performances; and employing AI tools to extract key features from high-throughput computing results, screen candidate materials, generate supporting reasons, and convert massive data into actionable R&D decisions. On this basis, this paper verifies the effectiveness of the paradigm through typical cases including the ALKEMIE platform of Beihang University, the high-dielectric material screening research of The Hong Kong Polytechnic University, and the “metal material data factory” developed by China Iron & Steel Research Institute Group. It also points out that crossing key barriers such as data barriers, computing resources, and interdisciplinary talents is essential for upgrading from single-point technological breakthroughs to systematic capability building. This paper argues that the paradigm transformation driven by AI and high-throughput computing has become an inevitable trend in material science development, and its realization relies on the collaborative construction of a trinity ecosystem consisting of data infrastructure, intelligent computing platforms, and independent experimental verification.

Keywords

Materials Genome; High-Throughput Computing; Artificial Intelligence; Paradigm Transformation; Rational Design

I. Introduction: Contradiction Between New Material R&D Efficiency and Industrial Demand

New materials serve as the core support for strategic industries including aerospace, electronic information, new energy, and biomedicine. In the new energy sector, for example, the ionic conductivity requirements for electrolyte materials of solid-state batteries are approaching physical limits, reaching 10⁻² S/cm at room temperature; thermal protection materials for aerospace applications need to withstand temperatures above 2000℃ while maintaining structural integrity; ultra-large-scale integrated circuits continue to raise precision requirements for dielectric materials. These performance demands cannot be met by optimizing existing material systems, but require the discovery of brand-new material systems.

Nevertheless, a severe “timescale mismatch” exists between the R&D pace of new materials and industrial iteration demands. The traditional trial-and-error material R&D paradigm relies on experience-driven repeated experimental optimization, with a typical cycle of 10 to 20 years from laboratory discovery to engineering application. In other words, the decade-scale material R&D system is confronted with annual or even monthly industrial iteration demands, leading to a continuously expanding supply-demand gap.

This contradiction has been strategically recognized by major economies worldwide. In 2011, the White House Office of Science and Technology Policy of the United States launched the Materials Genome Initiative (MGI), aiming to halve the time and cost required for new material development and application, with cumulative investment exceeding 1 billion US dollars over the years. Inspired by this initiative, China launched the national key R&D special project “Key Technologies and Supporting Platforms for Material Genome Engineering” in 2015, followed by similar programs launched by the European Union, Japan and other economies. The synchronous strategic actions of major economies indicate that this is not an individual academic judgment, but a collective consensus driven by industrial competition.

Against this background, this paper focuses on the core research questions: how are AI tools practically applied in high-throughput computing research? What specific research problems do AI tools solve in four dimensions including data governance, computing automation, simulation, and feature extraction? Are these applications empirically validated? What barriers need to be overcome to achieve paradigm transformation from single-point technological progress? The remainder of this paper is structured as follows: Chapter 2 reviews the historical evolution of material R&D paradigms and the theoretical connotation of rational design; Chapter 3 systematically elaborates the practical application modes of AI tools in high-throughput computing from four perspectives; Chapter 4 conducts empirical verification through representative cases; Chapter 5 discusses key issues in transforming from single-point breakthroughs to systematic capability building; Chapter 6 analyzes current challenges and future development trends; Chapter 7 draws the conclusions.

II. Paradigm Transformation of Material R&D: From Trial and Error to Rational Design

2.1 Historical Evolution of Material R&D Paradigms

The methodology of material R&D has evolved into four stages. The first stage is the empirical trial-and-error paradigm, represented by Edison’s testing of thousands of materials for filament application. This paradigm relies on repeated experiments and intuitive judgment, featuring low efficiency and dominating material research for centuries. The second stage is the theory-driven paradigm. With the maturity of solid-state physics, quantum chemistry and other theoretical disciplines, researchers began to guide experimental design based on theoretical understanding, realizing a shift from blind attempts to directional exploration. The third stage is the computational simulation paradigm. In the late 20th century, the combination of first-principles calculation methods such as density functional theory and high-performance computing enabled the computational prediction of material properties, greatly narrowing the scope of experimental screening.

Currently, material science is stepping into the fourth stage: the data-driven intelligent paradigm. Guided by the material genome concept and supported by high-throughput computing and AI tools, this paradigm aims to fundamentally transform material research from performance-oriented screening to performance-oriented design.

2.2 Material Genome Concept and Rational Design Paradigm

Inspired by the Human Genome Project, the Materials Genome Initiative regards materials as systems encoded by “material genes”, namely microscopic characteristic units that determine macroscopic material properties. Its core methodological innovation lies in the transformation from the traditional forward process of “preparation-test-analysis” to the reverse design path of “performance requirement → structural prediction → computational verification → experimental synthesis”.

The physical foundation of this transformation lies in the fact that the macroscopic properties of substances are essentially determined by the interaction between atomic nuclei and electrons, and the Schrödinger equation has theoretically defined the fundamental properties of all substances. First-principles calculations enable the prediction of electronic structures, mechanical properties, thermodynamic stability and other material properties by solving quantum mechanical equations without physical experiments. However, the accurate solution of macroscopic systems containing hundreds of millions of electrons is computationally infeasible. Density functional theory simplifies the complex electronic interaction into a function of electron density through ingenious mathematical approximation, making large-scale material simulation achievable.

2.3 High-Throughput Computing: The Technical Foundation of Rational Design

The rational design paradigm relies on large-scale material data as fundamental resources. The core value of high-throughput computing technology lies in completing simulation calculations of thousands of candidate material structures in a short time through automated processes and parallel computing architectures. Taking the ALKEMIE platform developed by the team of Professor Sun Zhimei from Beihang University as an example, the platform supports over 10⁴ concurrent high-throughput automatic computing simulations for a single user, realizing full-process automation from modeling and computing to data analysis.

Nevertheless, high-throughput computing faces three major bottlenecks in practical application. First, the material design space is extremely vast, making it difficult for researchers to identify valuable structures for calculation. Second, first-principles calculations are conducted under ideal conditions (0 K, perfect crystals), which cannot directly reflect material behaviors under real service conditions. Third, high-throughput computing generates massive datasets, making it hard for researchers to identify verification-worthy candidates and dominant performance mechanisms from numerous data entries. Although high-throughput computing solves the quantitative problem of material screening, AI tools are required to address the qualitative problems of efficient spatial search, real-condition approximation, and knowledge extraction from massive data.

III. Four Application Scenarios of AI Tools Empowering High-Throughput Computing

The core of AI empowering high-throughput computing research is not model training, but the application of mature AI tools by material researchers to solve practical R&D problems based on accumulated massive data. Four practical application scenarios in the research workflow are elaborated as follows.

3.1 AI-Based Data Governance: Activating Dispersed Material Data

Problems Faced by Researchers

Sufficient and reliable material data is the prerequisite for high-throughput computing and AI-assisted analysis. Research data is mainly derived from three sources: public material databases (e.g., the Materials Project containing structural and performance data of more than 150,000 materials), published academic papers (millions of literature works accumulated over decades containing massive experimental and computational results), and long-term stock data of research groups.

However, more than 300,000 papers were published in the field of material science in 2024 alone, making traditional manual literature research inefficient and time-consuming. Professional AI academic tools such as the UniResearch platform can help researchers quickly grasp overall research progress through structured intelligent interpretation, conduct in-depth contextual questioning on research details, extract core knowledge points via knowledge graphs, analyze literature citation and co-occurrence relationships, and visualize academic contexts. These functions enable researchers to clarify key literature, disciplinary knowledge structures and research gaps before formal data governance and high-throughput computing.

Direct application of multi-source data in high-throughput computing faces multiple obstacles. First, inconsistent data formats: different databases organize data by elemental composition, crystal structure or performance type, with unified standards lacking for field naming, unit systems and precision representation. For instance, the band gap data provided by the Materials Project is calculated via the PBE functional, while literature band gap data may be experimental measurements or HSE functional calculation results. Despite carrying the same name, the two types of data can differ by over 50%, leading to erroneous conclusions after direct integration. Second, inconsistent field alignment: different databases adopt different naming conventions for thermophysical parameters, such as “formation energy” and “enthalpy of formation”, which have subtle thermodynamic differences and will be misidentified as identical parameters without professional recognition.

AI Tool Application Methods

After importing multi-source data into AI data governance tools, the system implements three core operations:

First, automatic field mapping. The tool parses field names, units, value ranges and physical connotations of multi-source data via natural language processing, establishes cross-database field correspondence, and unifies diverse expressions such as “Eg”, “band_gap”, and “band gap (eV)” into a standardized band gap field, while retaining label information of calculation and experimental conditions.

Second, automatic data alignment. Based on the similarity of material composition and crystal structure, the tool automatically merges scattered data of the same material from different sources, forming material-centered datasets that integrate structural parameters, performance values and test/calculation conditions instead of simple data stacking.

Third, automatic conditional normalization. Guided by prior physical knowledge of materials, the tool standardizes performance data obtained under different test conditions. For example, electrical conductivity data measured at different temperatures is normalized to a unified reference temperature via the Arrhenius equation, ensuring the comparability of multi-source data.

Output Results

Researchers obtain high-quality standardized datasets with unified field definitions, deduplicated and integrated material entries, and normalized performance values. The datasets can be directly applied to subsequent computational screening, model training and feature analysis, saving months of manual data cleaning work.

Subsequent Research Applications

The governed high-quality datasets serve as the fundamental basis for all subsequent research. They can be imported into high-throughput computing tools as initial data or adopted for AI feature extraction analysis. Without standardized data governance, all subsequent calculations and analyses will be based on dirty data, resulting in unreliable research conclusions.

3.2 AI-Driven Script Generation and Data Calibration: Automating High-Throughput Computing Pipelines

Problems Faced by Researchers

The core of high-throughput computing is first-principles calculation of thousands of candidate material structures. Traditional manual workflows require repeated operations for each structure, including compiling crystal structure files containing atomic coordinates, lattice constants and symmetry information, configuring dozens of computational parameters such as functional type, cutoff energy, k-point grid density and convergence criteria, submitting tasks to high-performance computing clusters, monitoring operating status, handling computational interruptions and errors, extracting core data such as energy, band structure and density of states from output files, and associating extracted data with material structural and compositional information.

A high-throughput computing task covering 1,000 candidate materials requires 1,000 cycles of repeated manual operations. This workflow is labor-intensive and error-prone. Minor mistakes including parameter configuration errors, file naming confusion and data omission will lead to invalid batch calculation results and repeated work.

AI Tool Application Methods

After inputting the compositional range and structural constraints of target material systems into AI tools, the system automatically completes full-process operations:

First, automatic structure generation. The tool enumerates all feasible atomic occupancy configurations within the given compositional range, identifies and removes equivalent structures via symmetry analysis, and outputs a unique list of candidate structures without manual file compilation.

Second, automatic script generation. The tool generates standardized computational input files for each candidate structure, including format conversion of structural files and adaptive matching of computational parameters (functional, cutoff energy, k-point density) based on material characteristics. Researchers only need to set precision levels (high/standard/fast) and resource limits, and the system will automatically match corresponding parameter combinations.

Third, automatic task scheduling and monitoring. The tool automatically submits all computing tasks to clusters, monitors real-time task status, and intelligently handles interruptions and errors, such as automatic retry upon resource shortage and parameter adjustment for unreasonable configurations.

Fourth, automatic data calibration. Upon task completion, the tool automatically extracts core data including total energy, atomic energy, band structure, density of states and elastic constants from output files, labels detailed metadata including calculation methods, functional types, cutoff energy and k-point grid density, and stores standardized data associated with material composition and structure in databases without manual script parsing.

Output Results

Researchers obtain complete, traceable and reproducible calibrated high-throughput computing datasets, covering material composition, crystal structure, full computational parameters, performance outputs and metadata annotations.

Subsequent Research Applications

The calibrated datasets can be directly applied to simulation modeling, feature extraction analysis and iterative high-throughput computing. The automation workflow eliminates manual script writing and data extraction work, allowing researchers to focus on data analysis and experimental design.

3.3 AI-Based Simulation: Bridging the Gap Between Ideal Calculation and Real Service Conditions

Problems Faced by Researchers

Although density functional theory calculations achieve high precision, they only correspond to ground-state properties of perfect crystals at 0 K. In practical service scenarios, materials operate under complex conditions including room or high temperature, various defects (vacancies, interstitial atoms, dislocations and grain boundaries), external stress and strain, and interfacial or surface states. Taking solid-state battery electrolytes as an example, practical research focuses on lithium-ion diffusion coefficients of materials at 300 K with grain boundaries, rather than the band structure of perfect crystals at 0 K.

Direct simulation of real service conditions via density functional theory is computationally infeasible. Defective and interfacial systems contain hundreds to thousands of atoms, and the computational cost of density functional theory increases cubically with atomic number, reaching the limit of computing capability for large systems. Molecular dynamics simulation can handle thousands of atoms and set temperature and pressure conditions, yet its accuracy is entirely dependent on the reliability of interatomic potential functions. Traditional artificial potential function development relies on physical intuition and parameter fitting, requiring 3 to 5 years for a single material system, which cannot match the iteration speed of modern material R&D.

AI Tool Application Methods

Researchers directly load pre-trained AI potential function models, input material composition and initial atomic configurations, set simulation parameters including temperature, pressure, strain rate, defect type and concentration, and conduct molecular dynamics simulations to obtain material performance data under real service conditions.

The core advantage lies in the application of mature pre-trained models rather than independent model training. Taking the Deep Potential toolkit as an example, the simulation workflow includes three steps:

First, model loading. Researchers select pre-trained potential function models corresponding to target material systems. Public mature models are directly applicable for standard material systems; for new material systems, the tool automatically generates potential function models based on existing first-principles energy and force data, requiring no in-depth understanding of neural network architectures.

Second, condition configuration. Researchers set physical simulation parameters including temperature, pressure, strain loading mode, defect concentration and simulation duration through visual interfaces, without professional machine learning knowledge.

Third, simulation execution and result acquisition. The tool conducts GPU-accelerated molecular dynamics simulations (at least five orders of magnitude faster than density functional theory calculations) and outputs multi-dimensional data including energy variation, atomic trajectories, stress-strain curves, diffusion coefficients and thermal conductivity.

Output Results

Researchers acquire material performance data that incorporates real influencing factors including temperature effects, defect effects and interfacial effects, rather than ideal zero-temperature crystal data.

Subsequent Research Applications

Simulation results guide targeted experimental design and engineering decision-making. For example, if simulations show that a candidate electrolyte material achieves ideal lithium-ion diffusion coefficients at 300 K but suffers an order-of-magnitude performance degradation due to grain boundaries, researchers can propose targeted grain boundary engineering optimization schemes instead of blind experimental synthesis.

3.4 AI-Driven Feature Extraction: Converting Massive Data into Actionable R&D Decisions

Problems Faced by Researchers

Multi-stage research including data governance, automated high-throughput computing and real-condition simulation accumulates massive datasets. A typical high-throughput computing task covering 10,000 candidate structures with dozens of performance indicators per structure generates hundreds of thousands of data points.

Researchers need to clarify core practical questions from high-dimensional data: which candidate materials have the best application potential for experimental verification? What compositional and structural features dominate material performance? What is the optimal direction for subsequent material design? These pattern recognition problems exceed the processing capacity of human cognition.

AI Tool Application Methods

Researchers import calibrated datasets into AI feature extraction tools and set target performance variables (e.g., band gap value, lithium-ion diffusion coefficient). The tool implements two core analytical functions:

First, feature importance analysis. The tool quantitatively calculates the correlation strength between dozens of material structural descriptors (elemental ratio, atomic radius difference, electronegativity difference, coordination number, bond length distribution, lattice symmetry, etc.) and target performances, generating a ranked list of key features to clarify dominant influencing factors of material properties.

Second, intelligent candidate screening and recommendation. The tool sorts and filters all candidate materials according to performance constraints and outputs a shortlist of high-potential candidates. Differing from traditional screening methods, the tool provides traceable and verifiable recommendation reasons for each candidate by analyzing structural similarity with known high-performance materials and quantitative performance indicators.

Output Results

Three types of actionable outputs are obtained:

1. A feature importance ranking list for clarifying the dominant mechanism of material performance;

2. A optimized shortlist of candidate materials (reduced from thousands to 20–30 candidates);

3. Detailed recommendation rationales for each candidate material.

Subsequent Research Applications

First, experimental verification is only implemented for shortlisted candidates, greatly saving experimental time and cost. Second, the feature importance ranking clarifies core performance-determining factors, guiding targeted structural optimization for subsequent material design. Third, AI-generated recommendation rationales provide theoretical support for experimental scheme design, realizing the upgrade from blind trial-and-error to evidence-based directional exploration.

3.5 Collaborative Workflow of the Four Application Scenarios

The four AI application scenarios form a complete and iterative material R&D workflow, with the output of each scenario serving as the input of the next:

Accumulated Multi-source Data (public databases, literature data, group stock data)

→ Imported into AI data governance tools

→ Output: Standardized high-quality datasets with unified fields, aligned entries and normalized values

High-quality standardized datasets

→ Imported into AI script generation and data calibration tools

→ Output: Complete calibrated high-throughput computing datasets

Calibrated computing datasets

→ Imported into AI potential function simulation tools

→ Output: Material performance simulation results under real service conditions

Real-condition simulation results + High-throughput computing results

→ Imported into AI feature extraction and screening tools

→ Output: Feature importance ranking + Optimized candidate shortlist + Screening rationales

Final R&D decisions: Experimental verification arrangement, structural optimization design and subsequent research scheme formulation.

IV. Application Verification: Typical Scenarios and Practical Effects

4.1 Case 1: ALKEMIE Platform – Representative Practice of Domestic High-Throughput Computing

The ALKEMIE platform, independently developed by the Sun Zhimei team of Beihang University under the support of national key R&D programs, is a landmark domestic intelligent platform for visualized multi-scale integrated high-throughput automatic computing and material data management. Together with international mainstream platforms including Materials Project and AFLOW, it has been included in the 10-year achievement report of the Global Materials Genome Initiative compiled by the U.S. National Academies of Sciences, Engineering, and Medicine.

The ALKEMIE platform consists of four core modules: the high-throughput automatic computing simulation module (Matter Studio), the material database and data management module (Matter DB), the AI and machine learning-based material data mining module (Matter AI), and the potential function module (Potential Mind). The integrated modules form a closed-loop R&D system of “computation-data-mining”, supporting efficient data-driven new material development.

In practical applications, the ALKEMIE platform has achieved systematic research results in multiple fields including phase-change storage materials, heterojunction materials, two-dimensional materials and energy materials, proving that China has reached international advanced levels in independent high-throughput computing platform development.

4.2 Case 2: The Hong Kong Polytechnic University – Accelerated Dielectric Material Discovery via High-Throughput Screening

The research team led by Professor Yang Ming from The Hong Kong Polytechnic University achieved breakthroughs in the development of high-dielectric constant materials for next-generation two-dimensional electronic devices by combining high-throughput first-principles computing and AI technology. Starting from more than 140,000 known substances, the team conducted high-throughput screening based on key performance indicators including band gap and dielectric constant, locking 1,000 potential candidates and further screening down to 20 high-performance dielectric materials via semi-automatic simulation.

The integrated workflow improves R&D efficiency by approximately four times compared with traditional methods. This case verifies the practical value of the “high-throughput computing + AI screening” paradigm in solving industrial demands for advanced electronic device materials, realizing rapid convergence from massive design space to verifiable experimental candidates.

Another core innovation of the research lies in physics-embedded AI modeling. Short-range interaction information of diverse materials is integrated into graph neural networks, improving the efficiency and interpretability of models in predicting complex material properties such as adsorption characteristics and defect behaviors.

4.3 Case 3: China Iron & Steel Research Institute Group – AI-Driven Metal Material Data Factory

The independently developed “AI metal material data factory” of China Iron & Steel Research Institute Group represents the transformation of AI-enabled material R&D from academic exploration to industrial practice. Based on high-throughput data generation and iterative AI optimization, the platform is equipped with in-situ alloying high-throughput sample preparation systems and fully automatic experimental data acquisition systems to realize unmanned experimental data collection for metal materials.

Through AI-based massive data processing and analysis, combined with intelligent material optimization systems and laboratory cloud scheduling systems, the platform realizes multi-round iteration of high-throughput experiments, greatly shortening the R&D cycle and reducing the cost of high-performance alloy materials. This case indicates that the value chain of AI-enabled high-throughput computing has extended from computational screening to experimental verification, forming an initial closed-loop R&D mode integrating computation and experiment.

V. From Single-Point Breakthrough to Systematic Capability: Construction of Intelligent R&D Ecosystem

The above cases verify the technical feasibility of AI-enabled high-throughput computing at the single-point level. However, isolated technological breakthroughs cannot complete fundamental paradigm transformation. The upgrade from technical feasibility to systematic industrial capability relies on the collaborative construction of three core infrastructures.

5.1 Data Infrastructure: Database Construction and Standardization

Data is the fundamental resource for AI-enabled high-throughput computing research. Although AI tools can govern scattered heterogeneous data, low quality, insufficient consistency and incompleteness of original data will restrict the performance of AI algorithms. At present, global material database construction has achieved initial scale: the Materials Project contains over 150,000 material datasets, AFLOW covers more than 450,000 quaternary mixtures, and NOMAD stores over 120 TB of material data (as of 2025). China has also built a number of specialized material databases covering crystal structures, metal materials and energy materials.

Nevertheless, the lack of unified data standards remains the core bottleneck. Inconsistent experimental specifications and uneven data quality across research teams hinder cross-platform and cross-team data fusion and subsequent AI analysis. The solution lies in formulating unified material data specification standards and establishing systematic data quality certification and application norms.

5.2 Computing Platform: Deep Integration of High-Throughput Computing and AI Tools

The closed-loop intelligent material R&D system requires deep integration of high-throughput computing platforms and AI tools. On the one hand, high-throughput computing provides massive, high-quality training data for AI models; on the other hand, AI tools guide high-throughput computing directions via feature extraction and intelligent screening, reducing invalid computing resource consumption. The positive feedback loop of “computation generating data, data analyzed by AI, and AI guiding computation” constitutes the core driving force of the intelligent material R&D paradigm.

Preliminary progress has been made in the collaborative development of multi-scale high-throughput computing methods and AI tools in China. Experts propose that further coupling of first-principles calculation, molecular dynamics simulation, phase-field simulation and other multi-scale methods with AI tools is required to realize cross-scale collaborative material design from atomic to device levels.

5.3 Experimental Verification: Autonomous Experiment and Closed-Loop Iteration

The ultimate goal of material science research is experimental synthesis and engineering application rather than theoretical prediction. As pointed out by Professor Liu Yuzhou from Beihang University, the predictive capability of AI in material research is overestimated, while the “synthesis gap” between theoretical prediction and practical preparation is underestimated. The gap from conceptual verification to pilot test, scale-up and industrial production may still last for years or even decades.

The construction of autonomous laboratories is the key to bridging the synthesis gap. The integration of AI technology and robotic systems enables the construction of unmanned “dark laboratories”, realizing automatic closed-loop iteration of material synthesis, characterization and testing guided by AI experimental design. Beijing has issued strategic layouts for autonomous laboratory construction focusing on solid-state batteries, functional fibers and other key fields. The integration of computational prediction, data analysis and autonomous experiments realizes full-chain acceleration of material R&D beyond single-point technical optimization.

VI. Challenges, Countermeasures and Future Development Trends

6.1 Core Current Challenges

Despite broad development prospects, the paradigm transformation driven by AI and high-throughput computing faces multiple obstacles.

Data Barriers: High-quality experimental data is scarce, and the absence of unified data standards hinders cross-domain data fusion. Meanwhile, the black-box nature of AI models leads to poor interpretability, making it difficult for researchers to judge the rationality of model predictions and restricting practical application.

Computing Power and Energy Consumption Constraints: The training and fine-tuning of large AI models consume massive computing resources, increasing the energy cost of AI-driven material design. In addition, the shortage of interdisciplinary talents integrating material science, computational science and data science cannot meet industrial development demands.

Disconnected R&D and Industrialization Chains: Current new material research mainly focuses on laboratory performance breakthroughs, with insufficient connection with downstream product design and manufacturing links, resulting in low conversion efficiency of laboratory achievements to industrial practical applications.

6.2 Countermeasure Strategies

Targeted countermeasures are proposed to address the above challenges: first, formulate unified material data standards, build high-quality big data platforms, and break data island barriers; second, develop interpretable AI models embedded with physical prior knowledge to improve the reliability and credibility of predictive results; third, optimize computing strategies, adopt lightweight efficient models to reduce computing power consumption and avoid unnecessary large-scale model calculation; fourth, improve the achievement transformation system, open up the transformation channel between universities, research institutes and enterprises, and build a full-chain innovation platform covering basic research, pilot incubation and industrial mass production.

6.3 Future Development Trends

AI-enabled material R&D presents three major development trends. First, low-cost R&D: AI technology will reduce the frequency of repeated and high-cost experiments, greatly shortening the overall R&D cycle of new materials. Second, intelligent material manufacturing: Autonomous laboratories with self-perception and self-optimization capabilities will become the mainstream form of material R&D. Third, autonomous scientific discovery: Future AI technology will break through the constraints of existing data and models to discover unknown scientific laws, solving challenging scientific problems that cannot be addressed by human cognition alone.

The deep integration of large models and material science, the collaboration of embodied intelligence and autonomous laboratories, and the integration of scientific supercomputing and AI computing architectures will be the key research directions in the next stage.

VII. Conclusion

This paper systematically demonstrates the inevitability of the paradigm transformation from trial-and-error to rational design in material R&D, which originates from the fundamental contradiction between the dual industrial demands for higher performance and faster iteration of new materials and the efficiency bottleneck of traditional R&D modes. AI-enabled high-throughput computing is the core approach to bridge this contradiction.

This paper redefines the connotation of AI empowerment in material research: empowerment does not refer to independent AI model training by researchers, but the practical application of mature AI tools in full research links, including literature research and knowledge integration to clarify research foundations and gaps, standardized governance of scattered non-standard data to form available research resources, automated script generation and data calibration to realize industrialized high-throughput computing pipelines, pre-trained potential function model loading for real-condition simulation, and intelligent feature extraction to convert massive data into targeted R&D decisions. The five practical application scenarios constitute a complete theoretical and practical framework for AI empowering high-throughput computing research.

Empirical analysis based on typical cases including the ALKEMIE platform, dielectric material screening of The Hong Kong Polytechnic University, and the metal material data factory of China Iron & Steel Research Institute Group verifies that AI-enabled high-throughput computing can improve material R&D efficiency by several times or even an order of magnitude across multiple material systems and application scenarios.

Nevertheless, the complete realization of paradigm transformation still requires breakthroughs in core barriers including data barriers, model interpretability defects, insufficient interdisciplinary talents and unsmooth industrialization chains. At present, Beijing and other regions have launched special policies for “AI + new materials”, focusing on the layout of model software, data infrastructure and autonomous laboratories. This strategic layout will largely determine China’s competitive position in the global new material industry in the next decade.

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