From Data-Driven to Intelligent Discovery: Artificial Intelligence Accelerates Scientific Breakthroughs and Industry Transformation

　　With the explosion of data, the surge in computing power, and continuous algorithmic optimization, artificial intelligence (AI) is profoundly transforming the paradigm of scientific research—from a traditionally experience-driven approach toward data-driven, intelligent exploration. In scientific research, AI is not only an efficient tool for analyzing massive datasets but also enables the simulation of complex systems through deep learning and can even autonomously generate scientific hypotheses. Highly complex industries such as petrochemicals have become crucial testing grounds for AI’s practical application. AI permeates the entire chain—from exploration and production to environmental protection—empowering value creation in areas like resource development, process optimization, and equipment maintenance, thereby facilitating precise decision-making and boosting efficiency. However, challenges such as data silos, algorithmic biases, and energy consumption continue to constrain AI’s full research potential. This edition explores the current state, untapped potential, and remaining challenges of AI’s role in empowering scientific research—stay tuned!

　　□ Vice Dean, School of Information Science and Technology, Northwest University

　　Feng Jun

　　Artificial intelligence (AI) is a technology that enables machines—represented by computers—to simulate human capabilities such as learning, understanding, problem-solving, decision-making, and the expression of creativity and autonomy. Typically, the training process for AI requires large volumes of structured data to learn from. Therefore, researchers first need to collect, process, annotate, and analyze data, and then design AI algorithms to create valuable knowledge models that can serve humanity. Thus, data, computational power, and algorithms constitute the three essential components of AI. Among these, computational power primarily depends on the hardware devices—including computing units, storage systems, and control mechanisms—of computers. The construction of AI models generally involves two major stages: training and inference. Data and computational power form the foundation for model training, while algorithms provide the roadmap for realizing the model. In recent years, the rapid advancement of AI has largely been driven by the simultaneous progress in these three key elements.

　　Currently, artificial intelligence has become a first-level discipline, with subfields of research including machine learning, computer vision, and deep learning. Among these, machine learning is the science that studies how to use computers to simulate human learning activities; it is one of the most intelligent and cutting-edge research areas within artificial intelligence. Computer vision primarily aims to equip machines with the ability to "see," enabling them to understand, interpret, and process visual data and extract useful information from it. Deep learning, based on artificial neural networks—especially multilayer neural networks—automatically extracts multi-level features from raw data through multiple layers of nonlinear transformations, making it well-suited for handling complex, high-dimensional data such as images, speech, and text.

　　Upending Tradition: AI Becomes Scientists’ “Super Assistant”

　　At the heart of scientific research lies the exploration of the unknown, yet traditional methods have been constrained by human resources and tools. Today, AI—through the synergistic breakthroughs enabled by three key elements: data, computing power, and algorithms—is reshaping the scientific research process.

　　At the heart of scientific research lies the exploration of the unknown, the discovery of new phenomena, and the formulation of novel theories. Traditional scientific research methods rely on experiments, observations, and theoretical deductions—processes that are often time-consuming and constrained by the researcher’s experience and technical expertise. However, with the explosive growth in data volumes, the dramatic enhancement of computing power, and the continuous optimization of algorithms, the introduction of artificial intelligence technologies has opened up entirely new possibilities for scientific research.

　　At the data level, modern scientific research has accumulated vast amounts of experimental data through multiple sources, including experiments and the internet, laying a solid foundation for model training. In terms of computational power, advancements in hardware technologies such as cloud computing, edge computing, and GPUs have significantly enhanced the capacity to process large-scale data, enabling computation tasks that once took days or even months to be completed in a matter of hours. Meanwhile, continuous optimization of algorithms—such as deep learning and reinforcement learning—has allowed researchers to train more efficient models, extract hidden patterns from complex data, automatically generate hypotheses, and validate new scientific theories. The synergistic integration of algorithms, computational power, and data not only accelerates the pace of scientific discovery but also propels scientific research toward a new paradigm characterized by data-driven, intelligent exploration—shifting it away from an experience-driven approach.

　　By leveraging machine learning and deep learning algorithms, AI can automatically extract key insights from data, identify complex patterns, and detect anomalies. Through natural language processing (NLP) technologies, AI can perform automated content extraction and knowledge graph construction from scientific literature, laying a solid foundation for subsequent scientific discoveries. In fields such as astronomy, genomics, and climate change, AI can rapidly organize and analyze vast amounts of data, accelerating trend identification and decision support. For example, in genomic research, AI systems can analyze massive gene sequence datasets to identify patterns and swiftly uncover genetic variations associated with diseases, providing crucial data support for precision medicine and new drug development.

　　AI technology can also build high-precision mathematical models to simulate and predict complex physical, chemical, and biological processes. By leveraging deep neural networks and big data technologies, scientists can model the behavior of intricate systems and continuously refine model parameters through iterative optimization, thereby enhancing prediction accuracy. For example, recently, DeepMind’s AlphaFold has used deep learning to predict the three-dimensional structures of proteins with accuracy approaching that of experimental results obtained by researchers, providing a powerful tool for structural biology and drug discovery.

　　AI is not only a tool for data processing and prediction, but also a major driving force behind scientific innovation. In many scientific experiments, researchers first propose new research hypotheses, design experimental protocols, and integrate interdisciplinary knowledge. During the reasoning process, AI can uncover issues that human experts might overlook. For example, a team from DeepMind and MIT used deep-learning models to discover a brand-new antibiotic called Halicin—a molecule whose structure is radically different from that of traditional antibiotics. This discovery highlights AI’s potential in expanding the chemical space for drug development and generating entirely new molecular structures.

　　Industry Insights: AI-Driven Precision and Efficiency in the Petrochemical Industry

　　As a representative of high-complexity industries, the petrochemical sector has become a prime testing ground for AI-driven transformation, with AI’s value permeating the entire chain—from exploration and production to environmental protection.

　　In the petrochemical industry, resource exploration and development have always been critical stages. Traditional geological exploration typically relies on extensive seismic data acquisition, well logging, and on-site field surveys—processes that are both time-consuming and costly. However, the introduction of artificial intelligence technologies has enabled these stages to become automated and intelligent, significantly enhancing the efficiency and accuracy of resource exploration. AI can integrate and analyze multi-source data—including seismic, geomagnetic, gravity, remote sensing, and well-log data—using deep-learning algorithms to precisely model and predict complex subsurface structures. By creating digital twin models—a type of digital replica used to simulate real-world equipment or systems, which can be employed for monitoring, analysis, simulation, and control of physical assets—companies can build virtual models of underground oil and gas reservoirs, enabling them to assess reservoir characteristics and exploitation potential in advance and thereby guiding drilling and development decisions. Moreover, AI can also be applied to real-time data monitoring and predictive analytics, optimizing drilling operations, reducing risks, and improving development efficiency.

　　The petrochemical production process is complex, involving multi-step reactions and stringent control of process parameters. AI technologies can leverage historical process data to build predictive models and optimize production processes. By employing deep learning and reinforcement learning algorithms, the system can dynamically adjust reaction conditions—such as temperature, pressure, and catalyst dosage—in real time, thereby maximizing yield and achieving optimal product quality. Moreover, data-driven optimization approaches can uncover subtle correlations that have previously gone unnoticed in conventional processes, thus driving process reengineering. Enterprises can adopt AI platforms that, through real-time data collection and model predictions, automatically regulate parameters within reaction vessels, thereby enhancing reaction conversion rates and product purity.

　　In petrochemical production, the stable operation of equipment is crucial. Artificial intelligence can monitor sensor data—such as temperature, vibration, and pressure—in real time and use deep learning to identify fault patterns, thereby enabling equipment condition monitoring and predictive maintenance. By providing early warnings of equipment abnormalities, companies can schedule targeted maintenance plans, reduce downtime and maintenance costs, and ensure production safety and continuity.

　　Environmental protection and energy conservation and emission reduction are challenges that petrochemical enterprises must confront. AI can integrate multi-source data from sensors, satellite remote sensing, online monitoring devices, and more, enabling real-time monitoring and analysis of pollutants such as exhaust gases, wastewater, and noise. Through predictive models, enterprises can identify environmental risks in advance, optimize energy use, and reduce emissions. Meanwhile, intelligent control systems can dynamically adjust process parameters to minimize energy consumption, thereby promoting green production.

　　Petrochemical enterprises face safety risks such as fires, explosions, and leaks. The application of AI technology in safety management includes real-time monitoring of hazardous indicators, risk early warning, and decision support for emergency response. By integrating data from multiple sensors, AI systems can swiftly identify safety hazards and, through simulation-based predictions, recommend optimal emergency response plans to minimize accident-related losses. Moreover, training systems based on virtual reality (VR) and simulation technologies can enhance employees' emergency response capabilities and safety awareness. Petrochemical enterprises can deploy AI-powered safety monitoring platforms that, by analyzing data such as temperature, pressure, and gas concentrations in real time, can detect equipment abnormalities in advance and promptly activate emergency response plans, thereby preventing accidents.

　　In the field of new materials development, artificial intelligence—through virtual screening, molecular simulations, and machine-learning algorithms—helps researchers identify novel materials with specific properties from an enormous pool of molecular structures. AI can predict the performance of catalysts, polymers, or high-performance alloys, guide laboratory synthesis, shorten the new materials development cycle, and reduce R&D costs. By leveraging generative models, AI can also design entirely new molecular structures, exploring chemical spaces that are difficult to access using conventional methods.

　　Digital transformation is a key development direction for the petrochemical industry. By building digital platforms, companies can integrate data from various aspects—including production, equipment, environment, and safety—and leverage AI technologies to achieve real-time monitoring and optimized decision-making across the entire process. Digital platforms not only provide management with visualized operational dashboards but also support data-driven intelligent decision-making, helping companies optimize production schedules, reduce costs, and enhance overall competitiveness. With the aid of intelligent decision-support systems, enterprises can respond swiftly to market fluctuations or unexpected events, thereby achieving efficient operations.

　　Hidden Concerns and Strategic Maneuvering: The AI Research Journey Is Packed with Both Opportunities and Challenges

　　Despite the promising outlook, the integration of AI and scientific research still faces deep-seated conflicts related to data, ethics, and effectiveness.

　　In recent years, the rapid development of smart devices has generated vast amounts of data, and breakthroughs in computing chips such as GPUs have significantly boosted computational power. As a result, data-driven deep-learning algorithms have become the cornerstone of artificial intelligence. New network architectures—including Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformers—have been widely adopted for tasks such as image generation, natural language processing, and speech synthesis. Among these, Transformer-based models (such as GPT and BERT) have taken natural language processing technology to a whole new level, sparking a wave of next-generation intelligent question-answering products led by ChatGPT and DeepSeek.

　　Overall, artificial intelligence technology is currently undergoing rapid development. It is gradually evolving from the traditional “data-driven” model toward more efficient, smarter, and more creative approaches to reasoning and question-answering—approaches that are increasingly aligned with human intelligence. These cutting-edge technologies are not only driving intelligent transformations across various industries but also giving rise to new challenges, such as AI security and AI ethics.

　　Artificial intelligence has demonstrated tremendous potential in scientific research, yet it also faces numerous challenges—such as issues related to data quality and accessibility. Currently, most AI models rely heavily on high-quality datasets; however, in many research fields, data often suffer from problems like incompleteness, high levels of noise, or inconsistent formats, which severely limit the effectiveness of model training. Moreover, data across different disciplines exhibit significant variations. How to efficiently integrate data across diverse domains is now a pressing challenge that all enterprises must address swiftly. As we all know, many deep-learning models operate as “black boxes,” with their internal decision-making processes difficult to interpret. This undermines the transparency and credibility of scientific findings. Existing explainable AI technologies are still far from fully mature, making it challenging to provide sufficient theoretical support for complex scientific problems and thus affecting researchers’ confidence in the results.

　　Moreover, the robustness and generalization capability of AI models are also crucial criteria for evaluating their performance. Some models excel on specific datasets but tend to suffer from overfitting when applied to cross-domain or complex systems, making it difficult for them to adapt to scientific phenomena involving multiple variables and multiple scales. At the same time, in the processes of experimental design, outcome prediction, and data analysis, AI often relies on substantial computational resources, which not only leads to high computational costs but also raises concerns about energy consumption and environmental impact. From an ethical and societal perspective, issues such as academic integrity, peer review of research outcomes, and intellectual property rights arising from automated scientific research are receiving increasing attention. How to ensure both the efficiency of scientific discovery and the maintenance of academic fairness and transparency is currently a significant challenge facing researchers.

　　Expert Perspective: Digital and Intelligent Technologies Revitalize Traditional Energy Sources

　　□ Li Junjun, Vice Chairman and Secretary-General of the China Petroleum Society

　　In recent years, a new generation of digital and intelligent technologies—represented by artificial intelligence, large-scale models, and generative AI—has been rapidly developing worldwide, yielding a steady stream of groundbreaking achievements. The underlying logic of technological innovation and industrial transformation is undergoing profound restructuring, presenting rare opportunities for traditional industries such as oil and petrochemicals to undergo transformative upgrades and cultivate new forms of productive forces.

　　General Secretary Xi Jinping emphasized that accelerating the construction of Digital China means adapting to the new historical position of China’s development, fully implementing the new development philosophy, nurturing new drivers of growth through informatization, leveraging these new drivers to propel new development, and achieving new brilliance through this new development. The “Overall Layout Plan for Building Digital China,” issued by the CPC Central Committee and the State Council, clearly states that the construction of Digital China will be laid out according to a comprehensive framework of “2522”—that is, consolidating the “two foundations” of digital infrastructure and data resource systems, promoting the deep integration of digital technologies with economic, political, cultural, social, and ecological civilization development in a “five-in-one” approach, strengthening the “two capabilities” of digital technology innovation systems and digital security barriers, and optimizing the “two environments”—domestic and international—for digital development. In its “Guiding Opinions on Energy Work for 2025,” the National Energy Administration stressed the need to actively harness digital and green technologies to advance the building of a modern energy industry system. Promoting the deep integration of digital technologies with the real economy and empowering the digital and intelligent transformation and upgrading of traditional industries represent strategic choices for seizing the new opportunities presented by the new round of scientific and technological revolution and industrial transformation.

　　The 2024 Central Economic Work Conference emphasized that we should leverage scientific and technological innovation to drive the development of new-quality productivity, build a modern industrial system, and actively employ digital and green technologies to transform and upgrade traditional industries. In China’s petroleum and petrochemical sector, taking the implementation of the strategy to build a cyber power and a Digital China as our key focus, we are vigorously carrying out the “Artificial Intelligence Plus” initiative. We are comprehensively promoting digital and intelligent transformation across production, operations, and research fields, in response to the demand for a green and low-carbon energy transition, thereby injecting new momentum into the high-quality development of the industry.

　　As the core engine of today’s technological revolution, digital intelligence technologies are powerfully reshaping global political game rules, redefining the structure of the world economic landscape, and rewriting the balance of international competitive forces. They have become the key variable and driving force propelling human civilization toward a new era. Currently, driven primarily by artificial intelligence technologies and synergizing next-generation information technology clusters—including 5G high-speed communications, big data deep mining, and digital twin precision mapping—entire oil and petrochemical industry ecosystems are undergoing comprehensive transformation. State-owned energy enterprises such as China National Petroleum Corporation, Sinopec, CNOOC, National Pipeline Network, and Sinochem have made substantial progress in digital transformation, enhancing operational efficiency, innovating product and service offerings, and upgrading business models. In the exploration and development phase, geological data transmitted in real time via 5G is analyzed using artificial intelligence algorithms and combined with digital twin technology to construct three-dimensional underground models, enabling accurate and dynamic prediction of oil and gas reservoirs. In the refining and chemical processing sector, the deployment of 5G plus industrial IoT integrates massive data from the entire production process, leveraging AI optimization models to intelligently adjust process parameters and using digital twin systems for virtual simulation verification, thereby efficiently boosting production efficiency. In pipeline transportation, leveraging 5G’s low-latency characteristics, an integrated smart operation and maintenance system featuring “big data monitoring—AI-based early warning—digital twin-driven simulations” has been established, significantly improving the speed of leak detection and response. At the product sales end, the integration of 5G+AI-powered intelligent customer service systems with big-data-driven user profile analysis, coupled with digital twin-simulated consumption scenarios, has dramatically enhanced marketing precision. The deep integration across various technological fields is accelerating the intelligent and digital transformation of the oil and petrochemical industry, injecting digital DNA into this traditional energy sector. While elevating intrinsic safety levels, this transformation is creating a win-win situation of high-quality development and industrial upgrading, strongly propelling China’s oil and petrochemical industry into a new stage of “digital-intelligence convergence.”