理論計算、機器學習加速的能源材料探索系列報告之一
報告題目：Computational quantum mechanics – basics: theory and software
報告人：Prof. Chandra Veer Singh, University of Toronto
主持人（邀請人）：蔣青（楊春成）
報告時間：2022年12月20日9:00 AM---10:00 AM
線上騰訊會議ID：909025938
主辦單位：汽車材料教育部重點實驗室，伟德bv1946
報告摘要：
Density functional theory (DFT) is a quantum-mechanical atomistic simulation method to compute a wide variety of properties of almost any kind of atomic system: molecules, crystals, surfaces, and even electronic devices. DFT belongs to the family of first principles (ab initio) methods, so named because they can predict material properties for unknown systems without any experimental input. The DFT approach is widely applied in organic and inorganic chemistry, materials sciences like metallurgy or ceramics, and electronic materials, to just name a few areas. In this lecture, Prof. Singh will briefly introduce DFT, what problems DFT solves, how to apply DFT in various fields, and some DFT softwares.
報告人簡介：
Prof. Chandra Veer Singh is the Erwin Edward Hart Endowed Professor and Associate Chair of Research in the Department of Materials Science and Engineering (MSE) at the University of Toronto, Canada. Dr. Singh received his Ph.D. in Aerospace Engineering in 2008 from Texas A&M University. Subsequently, he worked as a postdoctoral fellow at Cornell University. He obtained industrial experience as a Design Engineer at General Electric Aircraft Engines. His research is currently focused on atomistic modeling and machine learning-enabled development of new materials for applications in electrochemical energy conversion and storage, including catalysts and metal-ion batteries. He has published 200+ papers in international peer-reviewed journals with 6700+ citations and an h-index of 47 in Google Scholar.
理論計算、機器學習加速的能源材料探索系列報告之二
報告題目：Machine learning – basics: theory and implementation
報告人：Prof. Chandra Veer Singh, University of Toronto
主持人（邀請人）：蔣青（楊春成）
報告時間：2022年12月20日10:00 AM---11:00 AM
線上騰訊會議ID：909025938
主辦單位：汽車材料教育部重點實驗室，伟德bv1946
報告摘要：
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they can independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. In this lecture, Prof. Singh will show machine learning approaches with applications examples for materials design. He will tell us the types of machine learning, how machine learning works, and the role of machine learning in material science.
報告人簡介：
Prof. Chandra Veer Singh is the Erwin Edward Hart Endowed Professor and Associate Chair of Research in the Department of Materials Science and Engineering (MSE) at the University of Toronto, Canada. Dr. Singh received his Ph.D. in Aerospace Engineering in 2008 from Texas A&M University. Subsequently, he worked as a postdoctoral fellow at Cornell University. He obtained industrial experience as a Design Engineer at General Electric Aircraft Engines. His research is currently focused on atomistic modeling and machine learning-enabled development of new materials for applications in electrochemical energy conversion and storage, including catalysts and metal-ion batteries. He has published 200+ papers in international peer-reviewed journals with 6700+ citations and an h-index of 47 in Google Scholar.
理論計算、機器學習加速的能源材料探索系列報告之三
報告題目：Computational quantum mechanics – applications: materials for energy conversion
報告人：Prof. Chandra Veer Singh, University of Toronto
主持人（邀請人）：蔣青（楊春成）
報告時間：2022年12月21日9:00 AM---10:00 AM
線上騰訊會議ID：146001955
主辦單位：汽車材料教育部重點實驗室，伟德bv1946
報告摘要：
With the development of society and human progress, the demand for energy is increasing day by day. Thus, inevitably, two big problems erupted, which are the energy crisis and environmental pollution, including the depletion of fossil fuels and the greenhouse effect. These problems have brought challenges to our life and even survival. To deal with these issues, green energy conversion should be the most important project. For energy conversion, the design of catalysts is the key. Chemical reactions include the breakage of chemical bonds in the reactants and the formation of new chemical bonds in the products, which are essentially a process of electronic redistribution. The role of the catalyst is to guide the redistribution of electrons purposefully. Density functional theory (DFT) calculations present great advantages in elaborating the electronic structures and thus are considered an indispensable tool for catalyst design. In this lecture, Prof. Singh will show the key questions in designing catalysts. To address these questions, Prof. Singh will summarize their works on the application of DFT calculations in designing catalysts, including 2D-materials-based catalysts, Si nanoparticle-based photocatalysts, and In2O3-xOHy nanocatalysts.
報告人簡介：
Prof. Chandra Veer Singh is the Erwin Edward Hart Endowed Professor and Associate Chair of Research in the Department of Materials Science and Engineering (MSE) at the University of Toronto, Canada. Dr. Singh received his Ph.D. in Aerospace Engineering in 2008 from Texas A&M University. Subsequently, he worked as a postdoctoral fellow at Cornell University. He obtained industrial experience as a Design Engineer at General Electric Aircraft Engines. His research is currently focused on atomistic modeling and machine learning-enabled development of new materials for applications in electrochemical energy conversion and storage, including catalysts and metal-ion batteries. He has published 200+ papers in international peer-reviewed journals with 6700+ citations and an h-index of 47 in Google Scholar.
理論計算、機器學習加速的能源材料探索系列報告之四
報告題目：Computational quantum mechanics – applications: materials for energy storage
報告人：Prof. Chandra Veer Singh, University of Toronto
主持人（邀請人）：蔣青（楊春成）
報告時間：2022年12月21日10:00 AM---11:00 AM
線上騰訊會議ID：146001955
主辦單位：汽車材料教育部重點實驗室，伟德bv1946
報告摘要：
Between 2010 and 2018, annual battery demand grew by 30%, reaching a total of 180 Gwh in 2018. Conservatively, the growth rate is expected to be maintained at an estimated 25%, culminating in demand reaching 2600 Gwh in 2030. In addition, cost reductions are expected to further increase the demand to as much as 3562 Gwh. Important reasons for this high rate of growth of the electric battery industry include the electrification of transport, and large-scale deployment in electricity grids, supported by anthropogenic climate change-driven moves away from fossil-fuel-combusted energy sources to cleaner, renewable sources, and more stringent emission regimes. Therefore, the design of battery materials is imperative. In this lecture, Prof. Singh will show the computational design of battery materials, including Li-S batteries, Li-ion batteries, solid-state electrolytes, and hydrogen storage.
報告人簡介：
Prof. Chandra Veer Singh is the Erwin Edward Hart Endowed Professor and Associate Chair of Research in the Department of Materials Science and Engineering (MSE) at the University of Toronto, Canada. Dr. Singh received his Ph.D. in Aerospace Engineering in 2008 from Texas A&M University. Subsequently, he worked as a postdoctoral fellow at Cornell University. He obtained industrial experience as a Design Engineer at General Electric Aircraft Engines. His research is currently focused on atomistic modeling and machine learning-enabled development of new materials for applications in electrochemical energy conversion and storage, including catalysts and metal-ion batteries. He has published 200+ papers in international peer-reviewed journals with 6700+ citations and an h-index of 47 in Google Scholar.
理論計算、機器學習加速的能源材料探索系列報告之五
報告題目：Machine learning – applications for energy materials, challenges, and opportunities
報告人：Prof. Chandra Veer Singh, University of Toronto
主持人（邀請人）：蔣青（楊春成）
報告時間：2022年12月22日9:00 AM---10:00 AM
線上騰訊會議ID：528064453
主辦單位：汽車材料教育部重點實驗室，伟德bv1946
報告摘要：
Both single-atom catalysts and high entropy alloy catalysts are the most popular catalysts due to their novel physical and chemical properties. The complex coordination environments of single-atom catalysts and the tanglesome active centers of high entropy alloy catalysts make the corresponding exploration quite difficult for experimental researchers as well as DFT researchers. In this lecture, Prof. Singh applied machine learning to study the stability, catalytic activity, and selectivity of single-atom catalysts and high entropy alloy catalysts. Based on the machine learning model, they proposed a new descriptor and bidirectional activation mechanism for nitrogen reduction reactions, got insights into the catalytic properties of high entropy alloy catalysts, and demonstrated how high entropy alloy catalysts circumvent the scaling relation for the CO2 reduction reaction. And finally, Prof. Singh points out the challenges of machine learning in energy materials and the corresponding potential solutions.
報告人簡介：
Prof. Chandra Veer Singh is the Erwin Edward Hart Endowed Professor and Associate Chair of Research in the Department of Materials Science and Engineering (MSE) at the University of Toronto, Canada. Dr. Singh received his Ph.D. in Aerospace Engineering in 2008 from Texas A&M University. Subsequently, he worked as a postdoctoral fellow at Cornell University. He obtained industrial experience as a Design Engineer at General Electric Aircraft Engines. His research is currently focused on atomistic modeling and machine learning-enabled development of new materials for applications in electrochemical energy conversion and storage, including catalysts and metal-ion batteries. He has published 200+ papers in international peer-reviewed journals with 6700+ citations and an h-index of 47 in Google Scholar.
