去重讲座条目：按最新提交保留其一，并同步更新索引

# 《科学大讲堂 第229期》马丁 院士：Pt/CeO2界面催化剂用于H2和CO竞争氧化

* 题目: Pt/CeO2界面催化剂用于H2和CO竞争氧化

* 主讲人：马丁 院士 @ 北京大学

* 时间：2026年3月11日 19:00-20:50

* 地点：理学院一楼化学系C1038报告厅

## 主讲人简介

Ding Ma, professor at the College of Chemistry and Molecular Engineering, Peking University. Focusing on the optimization of energy and resource utilization in China's society, his research mainly covers hydrogen production and transportation, waste plastic conversion, C1 chemistry, and the study of catalytic reaction mechanisms. He won the first prize in the 2013 Peking University Young Teacher Teaching Competition and was named one of the top ten mentors at Peking University in 2018. He serves as an associate editor and editorial board member for ACS Catalysis, Journal of Catalysis, and Catalysis Science & Technology, and is a member of the Academic Committee of the Cultural Heritage and Inheritance Laboratory of the Palace Museum.

## 讲座简介

Understanding the interfacial catalytic behavior through in-depth mechanism research is crucial for applying artificial intelligence and bigdata methods to design efficient catalysts. The application of artificial intelligence (AI) and big data methods to heterogeneous catalyst design resents both unique opportunities and challenges. These difficulties mainly arise from the challenge of accurate data labeling—a critical step in training AI models. Unlike in fields such as Go game playing or protein structure prediction where clear labeling and training targets can be provided through definite game outcomes or known protein structures), catalyst design, especially heterogeneous catalyst design, involves complex and often ambiguous precise structures and interactions at the atomic or molecular level. Particularly, catalyst structures often exhibit a distribution, and their possible restructuring under reaction conditions further increases the difficulty of applying AI and big-data methods to catalyst design. 

In application areas such as Go game playing or protein structure prediction, AI systems like AlphaGo and AlphaFold have achieved remarkable success. AlphaGo learns to predict game outcomes by studying numerous games with clear win/loss criteria, whereas the accuracy of AlphaFold in protein structure prediction benefits from the well-defined nature of three-dimensional protein structures. In these cases, AI systems are trained on massive, high-quality, unambiguous data, enabling highly precise and deterministic learning. In contrast, heterogeneous catalyst design, especially under actual operating conditions, faces a completely different scenario. Take the widely studied Cu-Zn-Al catalyst for methanol synthesis as an example. Despite extensive literature, there are still gaps in understanding the structure of active sites under real high-temperature and high-pressure reaction conditions. Although most catalyst systems have been thoroughly studied, the actual active structures and their dynamic behaviors during the reaction process remain unknown at the atomic level. Yet the training of algorithms capable of accurately predicting catalytic behavior and performance relies on precisely labeled datasets, and this knowledge gap severely limits the effectiveness of AI methods in catalyst design. In this report, we use the catalytic oxidation of H2 and CO over Pt/CeO2 catalysts as an example to illustrate the complexity of heterogeneous catalysis and the importance of mechanistic studies.

## 海报链接

![](https://gtimg.liziwl.cn/post-img/2026-03-11T19-00-00_%E9%A9%AC%E4%B8%81.jpg)

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# 王成先生 TCL科技集团首席执行官 李晓雨老师 南科大科创MBA项目运营中心主任 孔德远先生 腾讯IWorkBuddy 产品及运营专家：领航·无界共生

* 题目: 领航·无界共生

* 主讲人：王成先生 TCL科技集团首席执行官 李晓雨老师 南科大科创MBA项目运营中心主任 孔德远先生 腾讯IWorkBuddy 产品及运营专家 @ TCL科技集团、南科大科创MBA项目、腾讯IWorkBuddy

* 时间：2026年3月29日 14:00-17:30

* 地点：深圳·南方科技大学商学院101

## 主讲人简介

TCL全球化实战分享、启动三端融合成长、玩转腾讯版小龙虾

## 讲座简介

TCL全球化实战分享、启动三端融合成长、玩转腾讯版小龙虾

## 海报链接

![](https://gtimg.liziwl.cn/post-img/2026-03-29T14-00-00_%E7%8E%8B%E6%88%90%E3%80%81%E6%9D%8E%E6%99%93%E9%9B%A8%E3%80%81%E5%AD%94%E5%BE%B7%E8%BF%9C.jpg)

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# 《科学大讲堂系列 第241期》Prof. Ralf I. Kaiser：冷化学，热影响：星际空间中的分子复杂性

> 以下内容根据公开信息整理，并经大模型处理生成，可能存在疏漏或误差，请以实际信息为准。

* 题目: 冷化学，热影响：星际空间中的分子复杂性

* 主讲人：Prof. Ralf I. Kaiser @ 夏威夷大学

* 时间：2026年5月26日 10:30

* 地点：理学院一楼化学系C1038报告厅

## 主讲人简介

Ralf I. Kaiser received his Ph.D. in Chemistry from the University of Münster and Nuclear Research Center Jülich (Germany) in 1994 and conducted postdoctoral work at UC Berkeley (Department of Chemistry). During 1997-2000, he performed his Habilitation at the Department of Physics (University Chemnitz, Germany) and Institute of Atomic and Molecular Sciences (Academia Sinica, Taiwan). He joined the Department of Chemistry at the University of Hawai'i in 2002, where he is currently Professor of Physical Chemistry and Director of the W. M. Keck Research Laboratory in Astrochemistry. His research focusses on Reaction Dynamics & Materials in Extreme Environments spanning Astrochemistry & Astrobiology, Combustion & Energy, Material Sciences, Reaction Dynamics & Kinetics along with Planetary Sciences. These findings have been published so far in 670 peer-refereed publications with an overall H-index of 78 and some 22,000 citations. For his research achievements, Ralf I. Kaiser was elected Fellow of the Royal Astronomical Society (UK), the Royal Society of Chemistry (UK), the Institute of Physics (UK), the American Physical Society (APS), the American Association for the Advancement of Science (AAAS), and of the American Chemical Society (ACS).

## 讲座简介

Over the past decade, astrochemistry has undergone a paradigm shift: complex organic molecules can form efficiently in the coldest regions of space. Ice-coated interstellar grains are now understood as molecular factories that synthesize biorelevant species within cold molecular clouds and star-forming regions—environments once thought chemically inert. Understanding this reactivity requires bridging gas-phase and condensed-phase chemistry. In this talk, we present molecular beam and surface science experiments that resolve the underlying reaction dynamics and kinetics. In the gas phase, we identify pathways to polycyclic aromatic hydrocarbons (PAHs), key building blocks of carbonaceous nanoparticles. On icy grains, we demonstrate the formation of complex organics—including amino acids, sugars, and polyalcohols—under conditions that challenge conventional assumptions about low-temperature chemistry. Our approach combines isomer-selective vacuum ultraviolet (VUV) photoionization with reflectron time-of-flight mass spectrometry (ReTOF-MS) and infrared (FTIR) spectroscopy, enabled by advanced VUV light sources. These techniques provide molecular-level insight into reaction mechanisms. The results reveal efficient low-temperature routes to aromatic and complex organic molecules via non-classical pathways involving radical–radical reactions, submerged barriers, and excited-state dynamics. Together, these findings redefine how chemical complexity emerges in space and reshape our understanding of molecular evolution in the universe.

## 海报链接

![](https://gtimg.liziwl.cn/post-img/2026-05-26T10-30-00_Ralf_I._Kaiser.jpg)

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# 《《物质科学名师讲坛》系列》潘世烈 教授：真空紫外光电功能晶体创制

> 以下内容根据公开信息整理，并经大模型处理生成，可能存在疏漏或误差，请以实际信息为准。

* 题目: 真空紫外光电功能晶体创制

* 主讲人：潘世烈 教授 @ 中国科学院新疆理化技术研究所

* 时间：2026年6月12日 9:00-11:10

* 地点：工学院北楼一楼协作中心

## 主讲人简介

潘世烈，博士生导师，国家杰出青年科学基金获得者，国家万人计划领军人才获得者，科技部中青年科技创新领军人才获得者，英国皇家化学学会会士。中国科学院新疆理化技术研究所所长，中国科学院“特殊环境功能材料与器件”重点实验室主任。在Nature、Nat. Photonics、Nat. Commun.等刊物上发表SCI论文700余篇。担任《人工晶体学报》、《激光与光电子学进展》副主编，《The Innovation》、《Science China Materials》、《eScience、Idea》等国际编委和《Chinese Journal of Structural Chemistry》、《硅酸盐学报》和《化学进展》等期刊编委。长期从事光电功能晶体材料的基础和应用基础研究，在多波段、多功能光电功能晶体材料创制等方面做出了开创性贡献，创制了ABF等高性能真空紫外非线性光学晶体。

## 讲座简介

非线性光学晶体作为激光技术的重要物质基础，是前沿科学研究等领域重要战略支撑。其制备的关键科学问题在于带隙、非线性光学系数、双折射率等关键性能参数之间的平衡。报告人将重点阐述突破关键晶体原始创新与生长技术，实现关键激光技术用光电功能晶体材料自主可控的研究结果，具体包括：发展响应电荷密度各向异性分布、全空间原子切割等方法，实现双折射率快速预测和量化功能基元的倍频效应；搭建非线性光学材料性质的机器学习回归模型，开发快速预测结构软件，为化学组分空间材料设计提供关键技术；创新性提出含[BO4-xFx]基团的氟化硼酸盐是探索紫外、深紫外非线性光学晶体材料的优选体系，成功设计合成出一系列综合性能优异的氟化无机酸盐非线性光学晶体ABF等，其中ABF晶体实现177.3 nm深紫外激光高效、稳定输出，其深紫外倍频器件将有望用于产生全固态深紫外光源，大幅提升相关领域创新能力。

## 海报链接

![](https://gtimg.liziwl.cn/post-img/2026-06-12T09-00-00_%E6%BD%98%E4%B8%96%E7%83%88.jpg)

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# 《科学大讲堂 第246期》王俊 教授：人工智能时代的抗病毒药物设计

> 以下内容根据公开信息整理，并经大模型处理生成，可能存在疏漏或误差，请以实际信息为准。

* 题目: 人工智能时代的抗病毒药物设计

* 主讲人：王俊 教授 @ 罗格斯大学

* 时间：2026年6月15日 10:00

* 地点：理学院一楼化学系C1038报告厅

## 主讲人简介

Dr. Jun Wang is a Professor of Medicinal Chemistry at the Ernest Mario School of Pharmacy, Rutgers University. His research focuses on the discovery and development of antiviral therapeutics, with particular emphasis on viral cysteine proteases, deubiquitinases, and other essential viral enzymes and proteins. His laboratory integrates structure-based drug design, covalent and noncovalent inhibitor discovery, biochemical and cellular assay development, antiviral validation, pharmacokinetic evaluation, and AI-enabled virtual screening to advance new antiviral drug candidates.

Dr. Wang’s group has made important contributions to the discovery of first-in-class inhibitors targeting coronavirus papain-like proteases, including SARS-CoV-2 PLpro, and has developed mechanistically validated antiviral series with defined target engagement, cellular activity, and in vivo efficacy. His broader research program also includes antiviral discovery against enteroviruses, coronaviruses, and other emerging or re-emerging viral pathogens.

In addition to experimental medicinal chemistry, Dr. Wang’s laboratory actively develops and applies computational and AI-assisted approaches for drug discovery, including ultralarge-scale virtual screening, active learning, molecular docking, protein–ligand modeling, and structure-guided lead optimization. These approaches are integrated with direct-to-biology synthesis and rigorous experimental validation to accelerate the identification of novel chemical matter.

Dr. Wang has received national and international recognition for his contributions to bioorganic and medicinal chemistry, including the 2026 Tetrahedron Young Investigator Award for Bioorganic and Medicinal Chemistry and the 2026 ACS Division of Medicinal Chemistry Robert M. Scarborough Award for Excellence in Medicinal Chemistry. His work aims to build broadly applicable antiviral discovery platforms that can respond rapidly to current and future viral threats.

## 讲座简介

Emerging and re-emerging viral infections continue to pose major threats to global health, yet the development of direct-acting antivirals remains slow, costly, and technically challenging. Artificial intelligence is beginning to reshape antiviral drug discovery by enabling rapid analysis of large chemical spaces, prioritization of novel chemical matter, and integration of structural, biochemical, cellular, and pharmacological data. In this presentation, I will discuss how AI-enabled approaches can be combined with medicinal chemistry, structure-based drug design, and rigorous experimental validation to accelerate the discovery of antiviral therapeutics.

Our laboratory focuses on viral cysteine proteases, deubiquitinases, and other essential viral targets, including coronavirus papain-like proteases and enterovirus proteins. We integrate ultralarge-scale virtual screening, active learning, molecular docking, protein–ligand modeling, and direct-to-biology synthesis with biochemical assays, cellular target engagement studies, antiviral assays, mechanism-of-action validation, and pharmacokinetic evaluation. This workflow allows rapid progression from computational hit identification to experimentally validated inhibitors with defined potency, selectivity, cellular activity, and translational potential.

I will highlight examples from our discovery of first-in-class inhibitors targeting SARS-CoV-2 papain-like protease and related antiviral programs. These studies illustrate both the opportunities and limitations of AI in drug discovery: AI can improve speed, scale, and prioritization, but high-impact antiviral discovery still depends on target biology, assay quality, medicinal chemistry judgment, and orthogonal validation. Together, these integrated platforms provide a practical framework for developing new antivirals and preparing for future viral outbreaks.

## 海报链接

![](https://gtimg.liziwl.cn/post-img/2026-06-15T10-00-00_%E7%8E%8B%E4%BF%8A.jpg)

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