DP Technology, AISI and HKU Engineering to Collaborate in "AI for Science"

Dr. Linfeng Zhang , co-founder of DP Technology and winner of the 2020 ACM Gordon Bell Prize, was recently invited to deliver a keynote speech titled "AI for Science: Scientific Discoveries and Platform Engineering" on May 24 by Dr. David Srolovitz of 香港大学 . The speech, available on Youtube, was promptly followed by a new collaboration agreement between DP Technology, AI for Science Institute (AISI), and HKU Faculty of Engineering . The agreement seeks to fuel joint research collaborations, talent development programs, and other related endeavors.

The memorandum of understanding (MOU) delineates a cooperative framework that allows the parties to explore shared goals within AI for Science. The following areas will be emphasized in this partnership:

1. Joint Research Initiatives: The collaboration will facilitate joint research activities with a focus on leveraging AI for scientific discovery and research. DP Technology, AISI, and HKU Engineering will pool their expertise and resources, identifying projects that align with their common goals. Key areas could include novel materials, alloys, and advanced computing.
2. Talent Development Programs: The partners will collectively develop and execute programs aimed at fostering the upcoming generation of researchers, scientists, and engineers specializing in AI for Science. These programs will encompass joint training, workshops, seminars, and exchange opportunities for students, faculty, and researchers.
3. Additional Cooperative Activities: Joint conferences, symposia, publications, and patent applications are among the further collaborative activities that the parties would actively participate in.

This alliance represents a significant stride toward realizing scientific progress and talent cultivation in AI for Science. The collaborative parties, by merging their strengths and resources, aspire to speed up research breakthroughs and nurture a world-class workforce capable of tackling intricate AI for Science challenges.

Dr. David Srolovitz, Dean of HKU Faculty of Engineering and member of the US National Academy of Engineering, spoke about the state of the field: "We're at a key juncture in material science and engineering, where advanced computational methods, especially artificial intelligence, are set to revolutionize our understanding of atomic-scale processes. Material science characterization techniques have traditionally been hindered by time-averaging, losing vital atomistic details. Now, AI-accelerated quantum mechanics and molecular dynamics methods are bringing new insights that were previously inaccessible. This has far-reaching implications, facilitating the design, prediction, and control of material properties and leading to more efficient, sustainable, and innovative solutions across numerous industries."

Dr. Weinan E, Dean of AISI and a member of the Chinese Academy of Sciences, was also optimistic: "Artificial Intelligence (AI) is reshaping numerous fields in unprecedented ways. Taking linguistics as an example, over the past decades, eminent scholars, including Noam Chomsky, have been striving to refining the fundamental "principles" behind language. However, without explicitly grasping the "principles" themselves, AI systems have achieved superior results in tasks such as text generation, question-answering, and translation by employing large-scale models, massive data, and powerful computational capabilities. Materials and languages share some similarities, as both possess multi-scale structures and face challenges in seeking basic principles at the mesoscopic scale and in solving practical problems, among other aspects. Is it possible for AI to revolutionize the research paradigm of materials science, as it has with linguistics, and further unleash the potential of rational materials design? This is a question worth delving into and boldly exploring."

Shef Wang , Head of Global Alliance at DP Technology, added: "Material Science is traditionally driven by experiments. These experimental observations can be likened to high-resolution photographs of a process, each only capturing a specific moment. In contrast, advanced AI for Science methods, such as #DeePMD, provide a continuous, dynamic view—like a motion picture—revealing the intricate detail between snapshots. This collaboration with AISI and HKU Engineering is expected to accelerate the integration of AI for Science algorithms, data, and compute into a united platform, while exploring a new research standard operating procedure. As an HKU alumni, I am honored to facilitate this collaboration, and help solidify HKU's position as an AI for Science hub in Asia and globally."

Please contact Shef Wang or [email protected] for any inquries.