本會自2021年起建置一個平台能讓會員們多了解年輕學者的研究方向，促進相互了解。「理論與計算分子科學論壇」採線上演講方式，時間訂於每月最後一週的週四晚上6點開始。

演講影片觀看

*觀看資格: 有效會籍會員 (2022/08/01-2023/07/31)

*連結登入: https://www.t2comsa.tw/2021seminar/

日期	主講人/題目
2024-01-19	主講人：Dr. Jheng-Wei Li (CEA Centre de Grenoble) 題目：Tensor networks for solving the Hubbard model and function fitting 時間：113年1月19日(五)10:30 地點：4F Lecture Hall, Cosmology Hall, NTU 報告語言：英文 Online Link (Webex)：https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my-tc/j.php?MTID=mabcf9d00b3c3ec8799cb6163b3386419 Meeting number：2552 536 8285 Password：cbXHqvxF662（22947893 from phones and video systems） 大綱: Tensor network theory, at the interface of physics and mathematics, is an exciting field of research. In the first part of the talk, I will discuss our recent progress in using two-dimensional (2D) tensor networks to simulate Hubbard-type models. The 2D Hubbard model is the standard model to describe correlated elections in high-$T_c$ copper oxide materials. Despite the model’s simplicity, solving its ground state close to half-filling is not an easy task. Particularly, the question concerning $d_{x^2-y^2}$ superconductivity at this regime has remained a major controversy for years. Various magnetic orders can compete and mask the emergence of superconducting order. Via 2D tensor networks, we uncover the role of spin symmetry in these competing orders. This finding helps us to understand high-$T_c$ superconductivity through simple physics. In the second part, I will talk about how to use one-dimensional tensor networks to fit continuous functions. The main idea, pioneered by mathematicians in 2010, is to map continuous functions onto quantum states that can be efficiently parameterized by tensor networks. By that, the power of tensor networks is extended to solve a wide range of problems in physics. As a demonstration, I will show how we can apply tensor networks to global optimization problems and model superconducting circuits.
2024-01-05	主講人：Prof. Hsing-Ta (Theta) Chen (Department of Chemistry and Biochemistry, University of Notre Dame) 題目：Collective optical effects and local electron dynamics: Superradiance, Rabi splitting, and Marcus rates 時間：113年1月5日(五)10:30 地點：4F Lecture Hall, Cosmology Hall, NTU 報告語言：英文 Online Link (Webex)：https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=m878855a185b91d45904a195d596ecdd4 Meeting number: 2552 213 4773 Password: mKE8U6aQMh6 (65388627 from phones and video systems) 大綱: Strong light-matter interactions between quantum excitations and confined electromagnetic fields open up new possibilities to impact chemical reactivity and charge transport. When electronic or vibrational excitations are strongly coupled with a photon mode (for example near a plasmonic nanoparticle or in a microcavity), collective excitations lead to intriguing phenomena that are fundamentally different from conventional photoexcitation. Despite recent developments, understanding collective excitation remains challenging from theory and simulation perspectives. In this talk, I will present our recent efforts in modeling superradiance and cavity effects in a disordered system and our future endeavors of developing a theoretical toolbox for simulating collective excitation in materials.
2023-06-29	主講人：Prof. Ka Un Lao (Department of Chemistry, Virginia Commonwealth University) 題目：Accelerating Quantum Chemistry Calculations Using Grassmannians 時間：112年6月29日(五)14:00 地點：Rm. 504, Institute of Atomic and Molecular Sciences, Academia Sinica 報告語言：英文
2023-06-16	主講人：Prof. Akbar Salam (Department of Chemistry, Wake Forest University, USA)  題目：A QED Theory of Mediated RET Between a Pair of Chiral Molecules 時間：112年6月16日(四)14:00 地點：4F Lecture Hall, Cosmology Hall NTU (Hybrid, Webex)  報告語言：英文
2023-05-26	主講人：Prof. Chern Chuang (Department of Chemistry and Biochemistry, University of Nevada, Las Vegas, USA) 題目：Spectroscopic signatures of short- and long-range excitonic interactions in organic photovoltaic materials 時間：112年5月26日(五)10:30 地點：4F Lecture Hall, Cosmology Hall NTU (Hybrid, Webex) 報告語言：英文
2023-04-21	主講人：Prof. Henryk Witek (Department of Applied Chemistry, National Yang Ming Chiao Tung University) 題目：Toward quantitative Raman spectroscopy 時間：112年4月21日(五)11:00 地點：4F Lecture Hall, Cosmology Hall NTU (Hybrid, Webex) 報告語言：英文
2023-03-03	主講人： Prof. Seogjoo J. Jang (Department of Chemistry and Biochemistry, Queens College, City University of New York (CUNY) & Chemistry and Physics PhD Programs, Graduate Center, CUNY) 題目：Quantum Master Equation and Quantum Fokker-Planck Equation for Open System Quantum Dynamics 時間：112年3月3日(五)10:30 地點：4F Lecture Hall, Cosmology Hall NTU (Hybrid, Webex) 報告語言：英文
2022-12-29	主講人：邱政超教授 (國立中山大學化學系) 題目：A Bit about the Theory and Application of the kinetic Monte Carlo Method 時間:：111年12月29日(四) 18:00 線上報名：https://forms.gle/jtSrSW1mGXiGALUK8 報名截止時間 ：111年12月29日中午12點止 報告語言：中文
2022-11-24	主講人：Prof. Tzu-Hsiung Yang (Department of Chemistry, National Tsing Hua University)   題目：Deep Generative Models: A New Method for Molecular Discovery 時間：111年11月24日(四) 18:00 線上報名： https://forms.gle/9XQzYREPA6svrocz8 報名截止時間 ：111年11月24日中午12點止 報告語言: 英文 大綱: Deep generative models have emerged as a promising way for molecular discovery. They have shown great potential in designing molecules such as organic photovoltaics and drug-like molecules. In this talk, I will first go through a review on the developments of machine learning and deep learning for chemical applications, with the emphasis on their involvement in molecular generation via deep generative models (DGMs). I will then review state-of-the-art DGMs such as variational autoencoder (VAE), generative adversarial network (GAN), recurrent neural network (RNN), and flow. Lastly, I will go through our recent work on applying VAE models to discover optoelectronic materials.
2022-10-27	主講人：李奕霈教授 (國立臺灣大學化學工程系) 題目：The Good and the Bad of Deep Learning for Molecular Property Predictions 時間：111年10月27日(四) 18:00 線上報名： https://forms.gle/UEgrPpGXDCH8xdbJ8 報名截止時間 ：111年10月27日中午12點止 報告語言: 中文 大綱: Recent advances in deep neural networks have led to the development of models with remarkable accuracy for molecular property predictions and have successfully facilitated the development process in various fields, including retrosynthesis, drug design, and reaction engineering. Although deep learning has been shown to perform well in many applications, its accuracy and applicability strongly depend on the quality and quantity of available data. Since most of the research is carried out with a clear goal, data reported in the literature usually focus on certain regions of the chemical space. Therefore, in the field of chemistry, the accuracy of data-driven models is not always satisfactory, especially in new research areas where data are scarce. For this reason, assessing when and to what extent a prediction can be considered reliable is an important task for applying deep learning to the prediction of molecular properties. In this presentation, we will take the prediction of molecular thermochemistry as an example to discuss how one can deliberately design the architecture of deep neural networks to improve model generalizability and leverage active and transfer learning strategies to reduce the amount of data required. The prospect of combining deep learning models with scalable uncertainty quantification methods to derive explainable and calibrated uncertainty for molecular property predictions will also be discussed. References 1.   Li, Y.-P.; Han, K.; Grambow, C. A.; Green, W. H. Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry. J. Phys. Chem. A 2019, 123 (10), 2142– 2152. https://doi.org/10.1021/acs.jpca.8b10789. 2.   Scalia, G.; Grambow, C. A.; Pernici, B.; Li, Y.-P.; Green, W. H. Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction. J. Chem. Inf.  Model. 2020, 60 (6), 2697–2717. https://doi.org/10.1021/acs.jcim.9b00975. 3.   Chen, L.-Y.; Hsu, T.-W.; Hsiung, T.-C.; Li, Y.-P. Deep Learning-Based Increment Theory for Formation Enthalpy Predictions. 2022. https://doi.org/10.26434/chemrxiv-2022-lc84d. 4.  Yang, C.-I.; Li, Y.-P. Explainable Uncertainty Quantifications for Deep Learning-Based Molecular Property Prediction. 2022. https://doi.org/10.26434/chemrxiv-2022-qt49t.
2022-09-29	主講人：Prof. Lee-Wei Yang (Institute of Bioinformatics and Structural Biology, National Tsing Hua University) 題目：Drugging the dancing target by leveraging beneficial systemic effects 時間：111年9月29日(四) 18:00                             報名網址：https://forms.gle/pTivCJ6jTv7VDFfKA 報名截止時間 ：111年9月29日中午12點止 報告語言: 英語 大綱: For decades, drug discovery has been associated with high failure rate, huge capital injections and consequential high medical costs that are not always covered by health insurance. Still, one out of every five drugs newly entering the market caused serious reactions. Medication has been the number 4 leading cause, commensurate with stroke, of death in the hospital. Accuracy in drug design is a must but still falls short to ensure successful clinical trials on both safety or efficacy. The talk hopes to invite discussions with presenting philosophy, technological strategy and experimental evidence on a new possibility to lower the developmental cost and enhance the safety and synthesizability of new drugs. I will start with drug discovery stories where a MD->docking->AI->MD pipeline helped accurately select “safe” compounds with high molecular affinity, followed by re-integrating fragments of these compounds with feedback obtained from simple cellular experiments. All three newly synthesized compounds showed nanomolar affinity with hundreds nanomolar EC50 in suppressing breast cancer cell lines including TNBC. Cellular and Animal experiments recently demonstrated dose dependent efficacy with low toxicity. The talk advocates against equating affinity to specificity, which most pharma philosophically suffer from and is arguably the reason for the high failure rate. With acknowledging inevitable off-target effects, affinity is re-defined as the overlapped area of off-target effects from 2 or 3 drugs targeting the same protein on different sites. Autophagic protein ATG4B was introduced as a cancer target to illustrate the concept; multiple site drugging strategy was computationally designed with biochem, cellular, animal and NMR validations. Shall time allow, a combined data-driven and first principle strategy will be introduced on the design of new antimicrobial peptides. Reference https://www.nvidia.com/zh-tw/on-demand/session/gtcspring22-s41686/ https://www.thno.org/v08p0830.htm https://www.nature.com/articles/s41467-021-27655-0
2022-08-25	主講人：Prof. Masato Kobayashi (Department of Chemistry, Hokkaido University) 題目：Large-scale quantum chemical calculations based on divide-and-conquer (DC) method: Recent developments and future prospects 時間：111年8月25日(四) 18:00                             報名網址： https://forms.gle/PyynQugak4dqb5N38 報名截止時間 ：111年8月25日中午12點止 報告語言: 英語 大綱: The structure, reactivity, response to electromagnetic fields, and all other properties of molecules and molecular assemblies are determined by the quantum behavior of constituting electrons and nuclei. The goal of quantum chemical calculations is to reveal this behavior by numerically solving the Schrödinger equation for these systems. However, ordinary quantum chemical calculations require diagonalization of the Hamiltonian matrix, which requires a computational time proportional to at least O(N^3) for N atomic system. O(N) quantum chemical calculation methods that overcome this problem have been gradually proposed since the 1990s. We have contributed to this field by developing the divide-and-conquer (DC) method, which was originally proposed by Yang and coworkers. In this lecture, I will first review our recent developments in the DC methods. Then, I will give my personal view on the role that large-scale quantum chemical computation should play and the issues that need to be addressed in the era of data science native and the era of quantum computers.
2022-07-28	主講人：蔡明剛教授 (國立臺灣師範大學化學系) 題目：The perspectives on modeling CO2 electrocatalytic reduction by Cu-based materials 時間：111年7月28日(四) 18:00 報名網址： https://forms.gle/RsjWhvSLmggEcuHQ8 報名截止時間 ：111年7月28日中午12點止 報告語言: 中文 (PowerPoint will be prepared in English.) 大綱: 本次線上分享會針對銅電極(相關材料）在電化學催化CO2還原反應之理論計算模擬，進行部分相關論文的介紹。CO2電催化還原成可用的碳氫化合物，被視為解決永續能源發展的一個重要手段，其中理論計算在這個問題可扮演重要的角色。不論是在材料設計、反應選擇性機制的推理、中間體的光譜學探索，甚至電催化反應動力學模擬，都是理論計算研究者可以深入研究的面向。然而，電化學介面的複雜性，導致在理論計算的模擬方法手段上，尚有可精進的地方。這個線上分享會藉由個人有限的研究經驗，向有對此命題感興趣的聽眾，介紹未來有趣的相關研究方向。
2022-06-30	主講人： Dr. Chun-Wei Pao (Research Fellow, Research Center for Applied Sciences, Academia Sinica) 題目：Machine Learning-enabled Force Fields: solution toward complex material puzzles with atomistic resolution? 時間：111年6月30日(四) 18:00                             報名網址：   https://forms.gle/PNUTuopCLzBW82HT9 報名截止時間 ：111年6月30日中午12點止 大綱: Complex materials are gaining increasing attentions from both industrial and academic community. Nevertheless, owing to the combinatorially complicate chemical space spanned, these complex materials have imposed grand challenge to both experimental and theoretical teams. In this talk, I will present our recent efforts in utilizing machine learning-enabled force fields to tackle the problems of revealing the microstructures of complex materials. I will firstly present our extensive Monte Carlo simulations to provide insights into the process-structure-property of complex perovskite using artificial neural network scheme; then, I will present our large-scale molecular dynamics simulations of the plastic deformation of ultraelastic complex alloy using the spectral neighbor analysis potential. I hope that these examples of applications could provide useful insights into both the pros and cons of the machine learning-enabled models brought to theoreticians for the problem of complex materials.
2022-05-26	主講人：Prof. David Srolovitz (Dept. of Mechanical Engineering, The University of Hong Kong) 題目：Atom Size Dispersion Effects in the Thermodynamics of High Entropy Alloys 時間：111年5月26日(四) 18:00                             報名網址：https://forms.gle/7R24LHrXr1YEzEoX6    報名截止時間 ：111年5月26日中午12點止 演講語言: 英文 (Language: English) 大綱: High entropy alloys (HEAs) (solid-solution, nearly equi-atomic, multicomponent alloys) are stabilised by the configurational entropy associated with randomly distributing the elemental constituents amongst lattice sites. In this talk, I will examine the role of differing atomic size on stabilising these alloys and controlling their structural, mechanical and thermodynamic properties. This is done through statistical mechanics analysis and molecular dynamics simulations of simple 1D and 2D systems and a 3D crystal in a model where all bonds are harmonic and atomic sizes vary. This approach rigorously captures the effects of this atomic size dispersion and temperature on universal thermodynamic, structural, and elastic features through a single, effective temperature. Atomic size disorder greatly stabilises HEAs by raising the effective temperature; this increases the stability of a solid solution relative to phase separation or ordering. Atomic size disorder can also be manipulated in HEAs to create a material with a zero coefficient of thermal expansion (the Invar effect). We quantify these effects by application to a wide range of high entropy alloys.
2022-04-28	主講人： Prof. Daisuke Yokogawa (Department of Basic Science, The University of Tokyo) 題目：RISM-SCF-cSED: a hybrid method between quantum mechanical and solvation theories 時間：111年4月28日(四) 18:00                             報名網址：   https://forms.gle/QrkvBGyDTojeGuP1 報名截止時間 ：111年4月28日中午12點止 演講語言: 英文 (Language: English) 大綱: In most of the quantum mechanical (QM) calculations, solvation effect seems to be considered as minor effect. However, the solute and solvent interaction sometimes has great effect on the electronic structure of the solute molecule. For example, the solvent polarity is one of the important factors to determine the absorption and emission energies of imaging dyes. Only with the QM calculations in gas phase, it is impossible to discuss the color change of the dyes in solution. In this seminar, I’ll introduce the Reference Interaction Site Model (RISM) and the hybrid method between the RISM and QM approaches. Although RISM is a minor theory compared to polarizable continuum model (PCM), it has many interesting characters. By showing the advantages and weak points of RISM and PCM, I want to show how to select a proper solvation theory in the QM calculations with solvation effect.
2022-03-31	主講人：Prof. Nongnuch Artrith (Debye Institute for Nanomaterials Science, Utrecht University) 題目: Development of efficient and accurate machine-learning potentials for the simulation of complex molecules and materials 時間：111年3月31日(四) 18:00 線上報名: https://forms.gle/xGPEQFg8VnsXMRkZ8 報名期限: 111年3月31日中午12點止 演講語言: 英文 (Language: English) 大綱: The properties of materials for energy applications, such as heterogeneous catalysts and battery materials, often depend on complicated chemical compositions and complex structural features including defects and disorder. This complexity makes the direct modelling with first principles methods challenging. Machine-learning (ML) potentials trained on first principles reference data enable linear-scaling atomistic simulations with an accuracy that is close to the reference method at a fraction of the computational cost. ML models can also be trained to predict the outcome of simulations or experiments, bypassing explicit atomistic modelling altogether. Here, I will give an overview of our contributions to the development of ML potentials based on artificial neural networks (ANNs) and applications of the method to challenging materials classes including metal and oxide nanoparticles, amorphous phases, and interfaces. Further, I will show how large computational and small experimental data sets can be integrated for the ML-guided discovery of catalyst materials. These examples show that the combination of first-principles calculations and ML models is a useful tool for the modelling of nanomaterials and for materials discovery. All data and models are made publicly available. To promote Open Science, we also formulated guidelines for the publication of ML models for chemistry that aim at transparency and reproducibility.
2022-02-24	主講人：Prof. Kuo-Wei Huang (Physical Science and Engineering Division, King Abdullah University of Science and Technology) 題目: Fueling the Future 時間：111年2月24日(四) 18:00 報名對象：本會會員    線上報名: https://forms.gle/xGPEQFg8VnsXMRkZ8 報名期限: 111年2月24日中午12點止 演講語言: 英文 (Language: English) 大綱: (Abstract here https://reurl.cc/ZrZyMa) In 2021, the estimated world population of 7.0 billion people consumed ~14 Gtoe of energy (at an average rate of 19.0 TW). Globally, burning of carbon-based fossil fuels supplies over 81% of the energy demand, and hence the prospering industrial societies are responsible for the observed increase in carbon dioxide levels form preindustrial 280 ppm to over 412.5 ppm measured last year. The constantly increasing atmospheric CO2 concentration is highly likely to result in global warming, sea level rise and ocean acidification. To reduce the environmental footprint of modern societies and address the limitations of fossil recourses, the projected increase in global energy demand must go along with the implementation of low-carbon energy production and carrier systems. In this presentation, the current energy status and future options will be discussed and compared. It will then be concluded by introducing our research efforts in utilizing formic acid as a NET-ZERO hydrogen/energy carrier and e-fuel.
2021-12-30	主講人：郭哲來研究員 (中央研究院原子與分子科學研究所) 題目：Linking Potential Energy Surface and Vibrational Spectroscopy via Ab Initio Anharmonic Algorithms 時間：110年12月30日(四) 18:00       線上報名：https://forms.gle/obqaFYEsQT9Wk6Wh6 報名截止時間 ：110年12月30日中午12點止 演講語言: 英文 大綱: The potential energy surface (PES) of molecular systems plays a central role in chemistry. Vibrational spectroscopy methods traditionally serve as one of the major channels for Chemists to obtain information on the curvature of PES near a given stable minimum. Realistic molecular systems, however, are intrinsically anharmonic due to the existence of high-order terms in the PES. For example, vibrational (IR and Raman) spectra of many organic systems often reveal complex vibrational features near 3 μm which cannot be accounted for by harmonic approximation. Furthermore, these high-order terms of PES give rise to many important phenomena such as intramolecular vibrational energy redistribution (IVR). Vibrational modes associated with CH, NH, and OH bonds pose larger amplitude motions; the anharmonic effects thus can be quite substantial. In this perspective, we will focus on recent developments and applications of ab initio anharmonic algorithms, which not only help us understand vibrational spectra of important functional groups containing CH, NH, and OH but also provide a new window to see through PES beyond harmonic approximations. Through these theoretical analyses, we can link PES that theoreticians can compute directly to experimental observables collected via various vibrational spectroscopies. The examples discussed here were drawn mainly from recent gas-phase studies, yet the same notions can be applied to macro-molecular systems and condensed phases as well.
2021-11-25	主講人：朱智瑋教授  (國立陽明交通大學生物科技學系) 題目：Molecular Simulation, Machine Learning, and Artificial Intelligence in Studying Biomolecular Systems 時間：110年11月25日(四) 18:00       線上報名： https://forms.gle/BEkHjHpKENx24ebj7 報名截止時間 ：110年11月25日中午12點止 演講語言: 中文 大綱: With advancement in theory, algorithms, and computer hardware, the applications of molecular simulations on biological systems are experiencing revolutionary changes. This talk provides historical, current, and future perspectives of this field. An overview of the methodology development and problem formulation will be introduced for important classes of topics. The objective is to develop insight about the kind of problems that can benefit from the modern developments in this field.
2021-10-28	主講人：林倫年主任/研究員 (台灣大學凝態科學研究中心) 題目：What does “quantum materials” mean for chemists like us? 時間：110年10月28日(四) 18:00                             線上報名： https://forms.gle/Ae3TLFcgFaaAzokWA 報名截止時間 ：110年10月28日中午12點止 演講語言: 英文 大綱: Perhaps many of you may have already heard people whispering about “quantum materials”. It would not be trivial for chemists like us to understand what this term means? However, there have been many review papers explaining this term in physics, chemistry, material science, etc. It seems like a promising land is ahead of us. One of the quite recent issues of chemical review is a special issue focusing on the quantum materials [1~4], introducing them to chemists. The issue [1] says “for chemists, … the simplest of definitions is that a quantum material is one whose electronic or magnetic properties are best described as having a nontrivial quantum mechanical origin…. materials where classical particles or calculations that do not take into account the full character of the system do not adequately describe the electronic or magnetic properties displayed.” It would be interesting to see how “quantum materials” have been interpreted in chemistry so far. To this end, I will briefly review the relationship between molecules (atoms) and crystalline systems by making, in part, a bridge between the physics and chemistry of crystal. Hopefully, I attempt to provide you with some ideas of quantum materials where the interactions of four fundamental degrees of freedom, lattice, charge, orbital, and spin, are dynamically intertwined. For theoretical/computational chemists, you may be curious about how these freedoms are theoretically treated in crystal chemistry [4,5]. References [1] Robert Cava, Nathalie de Leon, Weiwei Xie Chem. Rev. 121, 2777−2779, 2021. [2] Nitesh Kumar, Satya N. Guin, Kaustuv Manna, Chandra Shekhar, Claudia Felser, Chem. Rev. 121, 2780-2815, 2021. [3] Daniel I. Khomskii and Sergey V. Streltsov, Chem. Rev. 212, 2992-3030, 2021. [4] Alex Zunger* and Oleksandr I. Malyi Chem. Rev. 212, 3031-3060 2021. [5] Alexander J. Browne, Aleksandra Krajewska Alexandra S. Gibbs, J. Mater. Chem. C 9, 11640-11654, 2021.
2021-09-30	主講人：吳台偉所長 (中央研究院化學研究所) 題目：Reaction Coordinate Identification and Rates of  Methane Hydrate  Nucleation from Molecular Dynamics Simulations 時間：110年9月30日(四) 18:00                             線上報名： https://forms.gle/Q3P5gq8qjkQk98gg7 報名截止時間 ：110年9月30日中午12點止 演講語言: 英文 大綱: Understanding the molecular mechanism of clathrate hydrate formation has been a challenging and fundamental research topic for many years. Recently, molecular simulations of clathrate hydrates have significantly advanced our understanding of the kinetics and pathway for hydrate nucleation from solution. The simulations reveal the formation of clathrate structures from multi-phase systems. Here we extract mechanistic details to define a reaction coordinate associated with specific molecular events that precede nucleation, reach a critical size, and result in growth. After defining this reaction coordinate for methane hydrate, advanced sampling methods are used to calculate a kinetically meaningful free energy pathway. Specifically, we will discuss the “Mutually Coordinated Guest” order parameter, its usage to estimate a hydrate nucleus critical size, testing of the order parameter via pB histogram shooting, and equilibrium path sampling calculations to determine the reaction coordinate and free energy change along the reaction coordinate. Optimization and use of a reaction coordinate is essential for the quantification of clathrate hydrate nucleation and growth. With a proven RC in hand for methane hydrate, a path forward is available for work involving heterogeneous nucleation, ethane hydrate nucleation, or inhibited systems at moderate conditions.
2021-08-26	主講人：游靜惠教授 (國立清華大學化學系) 題目：討論計算化學在未來化學實驗室自動化可能的功能 時間：110年8月26日(四) 18:00 線上報名：https://forms.gle/RCHcNNsVr6Cxb7g87 報名截止時間 ：110年8月26日中午12點止 演講語言: 中文 大綱: 合成化學實驗室未來可能藉由連結人工智慧程式與自動操作反應的機器而達到全面自動化，這類機器被視為有潛力成為研發新藥與開發新材料的主要工具，已經有先導工廠運作生產，市面上也有自動合成機器出售供研究實驗室使用。文獻上看來化學合成的人工智慧程式的效能經得起資深高成就有機會化學家的檢驗，而自動操作反應的機器的品管能減免人為疏失與對操作者的危險，自動化合成實驗室看起來將是為來的主流如果不是唯一的模式。這個未來的合成化學家是「鍵盤化學家」的藍圖也許會比我們預料的更早到來，C&E News今年3月28日的相關主題報導標題聳動「The lab of the future is now」。處於貌似相關領域的計算化學在此趨勢中具有的功能與如何發揮而給與最大助力，值得我們在趨勢初發萌芽時加以探討。
2021-07-29	主講人：高橋開人副研究員 (中央研究院原子與分子科學研究所) 題目：What are quantum chemists doing now? 時間：110年7月29日(四) 18:00 線上報名：https://forms.gle/D5yjaqYKrTzEUnfP8 報名截止時間 ：110年7月29日中午12點止 演講語言: 英文 大綱: In this talk, I will not talk about my research. Instead, I will survey some of the interesting (to me) research that is being performed in the field of quantum chemistry recently. First, I will briefly review the present state of quantum chemistry calculations in terms of accuracy by looking at some recent benchmark reports. Then we will ask if machine learning can help us develop better density functionals to be used to calculate unknown molecules. After looking at some pitfalls of the current use of density functional theory, we will end by asking if quantum chemistry calculation really helps us understand chemical concepts or not.
2021-06-24	主講人：王家蓁副教授/主任 (國立中山大學化學系/ 氣膠科學研究中心) 題目：病毒氣膠傳播的科學基礎及防治策略 (Airborne Transmission of Virus-laden Aerosols and Its Precautionary Measures) 時間：110年6月24日(四) 18:00 線上報名：https://forms.gle/tZg2uqoKsJYeyyoV7 報名截止時間 ：110年6月24日中午12點止 演講語言: 中文 大綱: 自COVID-19疫情爆發以來，其傳播途徑一直是全球各國公衛醫療體系及學術界密切討論的焦點之一。隨著疫情於全球持續延燒，許多證據直指氣膠傳播(又稱氣溶膠傳播或空氣傳播)為主要傳播途徑，促使WHO在4/30修改防疫準則，將氣膠傳播列為導致新冠肺炎短距離及長距離傳播的主要途徑，美國CDC亦隨即於5/7跟進修改其防疫準則。實際上, 不只新冠病毒, 病原體以氣膠型態於空氣中傳播是許多呼吸道傳染疾病共同的傳播模式。本次演講中, 我將分享病毒氣膠如何產生、於環境及人體中如何傳輸流佈，最終導致感染, 以及各種COVID-19經由氣膠傳播的證據，包括超級傳播事件、高比例無症狀或症狀前傳播、與通風排氣系統的高度相關性，以及國外多起飛沫及接觸防範措施完備情況下卻還是導致醫護人員院內感染及防疫旅館內感染的事件。我也將分享與此議題相關的未來可能研究議題, 包括氣膠於各種環境狀態下之氣動力學理論計算及模擬。
2021-05-27	主講人：江志強教授 (台灣科技大學化學工程系) 題目：The role of computational chemistry in the development of ion batteries 線上報名：https://forms.gle/3tes7PhMj7HEZu21A 報名截止時間 ：110年5月27日中午12點止 演講語言: 中文 大綱: 溫室氣體對地球環境的危害逐年加重，節能減碳是當今的重要課題。除了尋求乾淨能源外，儲能設備的發展也是亳不容緩。為了落實減碳，當今主要國家紛紛訂下在2030 ~ 2040年禁止燃油車的販售，禁售的政策能否落實，則決定於離子電池的開發。在我的演講中，我將介紹離子電池的現狀，及其發展的瓶頸，並以鋰離子電池為例，說明計算化學可以扮演的角色。
2021-04-29	主講人：牟中原院士 (台灣大學化學系) 題目：Plasmonic photo-catalysis 線上報名：https://forms.gle/Qnx37nR4WAnJ3k2o8 報名截止時間 ：110年4月29日中午12點止 演講語言: 中文 大綱： The last two decades have seen a vigorous growth of exploration in plasmonics in the interaction between light and free carriers in metals. The phenomenon includes SERS, plasmonic photocatalysis and plasmonic photonics. When plasmonic excited electron can be ejected from the metal nanoparticle, the hot electron, to affect some catalytic action, one observed the so-called “plasmonic photo-catalysis”. The field has shown many very promising experimental results in giving excellent catalysis. I will begin in this presentation with showing a few experimental systems from my lab that give plasmonic catalytic transformations. This includes, (1) Photocatalytic H2O2 Production with Au/TiO2 catalysts (using hot electron to reduce oxygen to H2O2) (2) Ordered Mesoporous Au/TiO2 Nanospheres for Solvent-Free Visible-Light-Driven Plasmonic Oxidative Coupling Reactions of Amines (using hot hole) However, theoretical understanding of the catalytic action is rather poor at present. I will then discuss the theoretical issues in plasmonic photocatalysis.
2021-03-25	主講人：許昭萍研究員 (中央研究院化學研究所) 題目：The dynamics of charge transport: how can Machine Learning help? 線上報名：https://forms.gle/Fwy3pGWx3STtZCiP6 報名截止時間 ： 110年3月25日中午12點止 演講語言: 中文 大綱: 以理論模型描述電荷傳輸，重組能和光譜密度函數描述了原子核運動對傳輸電子的影響。我們通常可以使用各種理論方法獲得光譜密度：例如從介電響應模型獲得或通過各種理論模型直接計算。隨著現代計算技術的進步，現在已經可以獲得振動耦合的許多細節。 在本次演講中，我將先根據一些早期文獻，提供大家對振動耦合本質的了解。我也將進一步討論與非對角線的、非絕熱耦合矩陣元素的振動耦合。通過機器學習，可以減少大量的計算工作，並且可以模擬許多非對角的電子-聲子耦合的性質。演講中會討論這樣的結果，並討論它們在電荷傳輸中的作用。
2021-02-25	主講人： 許良彥助研究員 (中央研究院原子與分子科學研究所) 題目：The Alchemy of Vacuum: Effects of Quantum Electrodynamics in Chemistry 線上報名：https://forms.gle/dZtGCdJQ791rbYFp9 報名截止時間 ： 110年2月25日中午12點止 演講語言: 中文 大綱: 量子電動力學被費曼譽為「物理學的瑰寶」以及「化學背後的原理」， 但近代化學卻極少見到量子電動力學的蹤跡。近幾年來，人們已經可以利用「真空電磁場 (電磁場的量子漲落)」控制化學反應的速率與產物。換句話說，真空電磁場可以被用來當作催化劑或抑制劑，這個新興的領域被稱作電磁極化子化學(polariton chemistry)或量子電動力學化學(QED chemistry)。在這場演講中，我將與大家分享這個領域目前的進展。由於這個領域充滿許多未知，我希望能夠透過這場活動， 一同討論台灣在這項前沿研究上未來可能的機會。
2021-01-28	主講人： 鄭原忠教授 (國立臺灣大學化學系) 題目：Quantum Chemistry on Quantum Computers: Explained by a Theoretical Chemist 線上報名：https://forms.gle/VTSyFf2R6JtYeSBT9 報名截止時間 ： 110年1月28日中午12點止 演講語言: 中文 大綱: 量子計算在近幾年已經成為媒體與學界共同的熱門話題，而且常聽到的消息或量子電腦的最新突破常常與量子化學計算相關。在這場演講中，我將與大家分享目前量子計算對於處理化學領域相關研究問題的各項模型以及預期的影響，透過比較主觀與批判性的視角，來探討量子電腦未來處理化學、物理相關問題的可能性。希望透過這場活動提升大家對於量子計算相關議題的了解，也一起討論台灣在這項科學前沿研究上可能的機會。