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ICTP Condensed Matter and Statistical Physics
Приєднався 21 лис 2017
CMSP Lesson Series (Atomistic Simulation Seminar Series): Polarizable embedding QM/MM: (...)
CMSP Lesson Series (Atomistic Simulation Seminar Series): Polarizable embedding QM/MM: from properties to dynamics
Speker: Lorenzo Cupellini
(Dipartimento di Chimica e Chimica Industriale, Università di Pisa)
This lecture series will cover the following topics:
Introduction to Polarizable embedding QM/MM methods (QM/MMPol) as a technique to tackle complex systems.
Theory and Applications of integrating QM/MMPol to describe electronic excitations and its interface with studying dynamics using the polarizable AMOEBA force field.
Illustration of the use of OpenMMPol, a new open-source modular library that can be interfaced with QM codes to access polarizable QM/MM models.
Relevant references that will be presented during my lectures are included below:
[1] Bondanza et al., Phys. Chem. Chem. Phys. 2020; 22, 14433
[2] Nottoli et al., WIREs Comput Mol Sci. 2023; 13, e1674
[3] Bondanza et al., J. Chem. Phys. 2024; 160, 134106
Speker: Lorenzo Cupellini
(Dipartimento di Chimica e Chimica Industriale, Università di Pisa)
This lecture series will cover the following topics:
Introduction to Polarizable embedding QM/MM methods (QM/MMPol) as a technique to tackle complex systems.
Theory and Applications of integrating QM/MMPol to describe electronic excitations and its interface with studying dynamics using the polarizable AMOEBA force field.
Illustration of the use of OpenMMPol, a new open-source modular library that can be interfaced with QM codes to access polarizable QM/MM models.
Relevant references that will be presented during my lectures are included below:
[1] Bondanza et al., Phys. Chem. Chem. Phys. 2020; 22, 14433
[2] Nottoli et al., WIREs Comput Mol Sci. 2023; 13, e1674
[3] Bondanza et al., J. Chem. Phys. 2024; 160, 134106
Переглядів: 45
Відео
CMSP Seminar (Atomistic Simulation Seminar Series): How multiscale strategies uncover the (...)
Переглядів 593 місяці тому
CMSP Seminar (Atomistic Simulation Seminar Series): How multiscale strategies uncover the function of photoactive proteins Speaker: Lorenzo Cupellini (Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Italy) Abstract: Light-induced processes occurring in proteins are crucial for many biological functions, such as photosynthesis or biorhythm regulation. These processes are made ...
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 1
Переглядів 9153 місяці тому
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 1 - General introduction to algebraic topology on networks and simplicial complexes -Homology and boundary operators Speaker: Ginestra Bianconi (Queen Mary University of London) Higher-order networks [1] capture the many-body interactions present in complex systems and are dramatically changing our understandin...
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 3
Переглядів 3423 місяці тому
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 3- Topological Dirac equation and Discrete Network Geometry-Metric cohomology Speaker: Ginestra Bianconi (Queen Mary University of London) Higher-order networks [1] capture the many-body interactions present in complex systems and are dramatically changing our understanding of the interplay between topology of ...
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 2
Переглядів 3193 місяці тому
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 2- General introduction to algebraic topology on networks and simplicial complexes -Hodge Laplacians and Dirac operator Speaker: Ginestra Bianconi (Queen Mary University of London) Higher-order networks [1] capture the many-body interactions present in complex systems and are dramatically changing our understan...
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 4
Переглядів 2743 місяці тому
CMSP series of lectures on "Topology and dynamics of higher-order networks": lecture 4- Information theory of network geometry and gauge fields. Speaker: Ginestra Bianconi (Queen Mary University of London) Higher-order networks [1] capture the many-body interactions present in complex systems and are dramatically changing our understanding of the interplay between topology of and dynamics. In t...
CMSP Webinar (Atomistic Simulation Seminar Series): Challenges in Heterogeneous Catalyst Development
Переглядів 3093 місяці тому
CMSP Webinar (Atomistic Simulation Seminar Series): Challenges in Heterogeneous Catalyst Development: Advancements in High Throughput Simulation, Experiments, and Machine Learning Speaker: Sandip De (Global Scientific Discipline Lead, Inorganic Materials modelling QM, & BASF) Abstract: The development of efficient catalysts is crucial for achieving energy-efficient chemical transformations and ...
Unlocking Atomistic Simulation Potential: Tutorial on DeePMD-kit for Accurate Machine-Trained Models
Переглядів 9714 місяці тому
CMSP Atomistic Simulations Tutorial Speaker: Cesare Malosso (SISSA) Abstract: The last decade has seen the rise of machine-trained potentials, exemplified by deep-neural networks or Gaussian processes, as potent instruments for atomistic simulations. These potentials boast nearly quantum mechanical precision while incurring only marginally higher costs compared to classical force fields. This a...
Quantum gas microscopy of Hubbard systems out of equilibrium
Переглядів 4154 місяці тому
Quantum gas microscopy of Hubbard systems out of equilibrium Speaker: Johannes ZEIHER (Max Planck Institute of Quantum Optics)
Programmable Quantum Matter
Переглядів 4354 місяці тому
Programmable Quantum Matter Speaker: Kaden HAZZARD (University of Rice)
Thermopower properties of strongly correlated systems
Переглядів 1904 місяці тому
Thermopower properties of strongly correlated systems Speaker: Thereza PAIVA (Federal University of Rio de Janeiro)
Empowering deep neural quantum states through efficient optimization
Переглядів 2304 місяці тому
Empowering deep neural quantum states through efficient optimization Speaker: Ao CHEN (University of Augsburg)
Ab-initio variational wave functions for the time-(in)dependent many-electron Schrödinger equation
Переглядів 2524 місяці тому
Ab-initio variational wave functions for the time-(in)dependent many-electron Schrödinger equation Speaker: Jannes NYS (EPFL)
Transformer wave functions for quantum spin models
Переглядів 2004 місяці тому
Transformer wave functions for quantum spin models Speaker: Federico BECCA (University of Trieste)
Work statistics and Entanglement across the fermionic superfluid-insulator transition
Переглядів 2244 місяці тому
Work statistics and Entanglement across the fermionic superfluid-insulator transition Speaker: Vivian FRANCA (São Paulo State University)
Many-body entropies and entanglement from polynomially-many local measurements
Переглядів 1694 місяці тому
Many-body entropies and entanglement from polynomially-many local measurements
Provable quantum earning advantages for physics data
Переглядів 1294 місяці тому
Provable quantum earning advantages for physics data
Learning quantum systems: from physics-inspired models to Hamiltonian learning
Переглядів 2084 місяці тому
Learning quantum systems: from physics-inspired models to Hamiltonian learning
Optimal control and implementation of quantum algorithms
Переглядів 1724 місяці тому
Optimal control and implementation of quantum algorithms
Reinforcement Learning in Online Bayesian Estimation for Noise-Driven Coherent Rotation a Spin Qubit
Переглядів 1264 місяці тому
Reinforcement Learning in Online Bayesian Estimation for Noise-Driven Coherent Rotation a Spin Qubit
Neural network approach to quasiparticle dispersions in doped antiferromagnets
Переглядів 1064 місяці тому
Neural network approach to quasiparticle dispersions in doped antiferromagnets
Classical machine learning for quantum simulations: detection of phases, order parameters, and ...
Переглядів 1514 місяці тому
Classical machine learning for quantum simulations: detection of phases, order parameters, and ...
Deep Learning for Quantum Physics and Astronomy
Переглядів 2374 місяці тому
Deep Learning for Quantum Physics and Astronomy
Representation learning for quantum systems
Переглядів 2234 місяці тому
Representation learning for quantum systems
Domain wall dynamics of a two dimensional quantum Ising model using tree tensor networks
Переглядів 1724 місяці тому
Domain wall dynamics of a two dimensional quantum Ising model using tree tensor networks
Understanding Quantum Spin Liquids on Triangular Lattice withSymmetry Breaking and Machine Learning
Переглядів 1544 місяці тому
Understanding Quantum Spin Liquids on Triangular Lattice withSymmetry Breaking and Machine Learning
Reinforcement Learning to Disentangle Quantum States from Partial Observations
Переглядів 1204 місяці тому
Reinforcement Learning to Disentangle Quantum States from Partial Observations
Amorphous quantum magnets in a two-dimensional Rydberg atom array
Переглядів 754 місяці тому
Amorphous quantum magnets in a two-dimensional Rydberg atom array
Frontiers of quantum simulations with optical lattices
Переглядів 1394 місяці тому
Frontiers of quantum simulations with optical lattices
Simplifying the simulation of local Hamiltonian dynamics
Переглядів 1134 місяці тому
Simplifying the simulation of local Hamiltonian dynamics
Summary Professor Fabricio BA introduces ultracold atoms in quantum technologies, focusing on experimental methods and their applications in quantum physics. Highlights - 🔬 Introduction to ultracold atoms and their significance in quantum technologies. - 🎓 Emphasis on experimental perspectives and methodologies in ultracold atom research. - 📏 Overview of quantum degenerate states and their importance in the field. - ⚖ Discussion on precision measurements using ultracold atoms. - 🧪 Exploration of ultracold atoms in studying quantum chemistry. - 🌡 Explanation of reaching quantum degeneracy through laser and evaporative cooling techniques. - 🔭 Techniques for trapping and imaging ultracold atomic gases. Key Insights - 🔄 **Quantum Degeneracy**: Ultracold atoms allow exploration of quantum degeneracy, where quantum effects dominate over classical mechanics, essential for understanding many-body systems. - 🧬 **Precision Measurements**: Ultracold atoms are pivotal in precision measurements, aiding in advancements in timekeeping and gravitational force studies, crucial for quantum technologies. - 🔍 **Experimental Techniques**: The use of laser cooling and evaporative cooling is vital for reaching the nano-kelvin temperatures necessary for creating Bose-Einstein condensates and Fermi gases. - 🌌 **Quantum Chemistry**: Ultracold atoms serve as building blocks to investigate quantum-level interactions and chemical bonds, providing insights into complex molecular systems. - ⚡ **Trapping Mechanisms**: Optical dipole traps play a significant role in isolating ultracold atoms from external influences, enabling detailed experimental studies without environmental noise. - 📈 **Imaging Techniques**: Time-of-flight imaging provides crucial data about the momentum distribution of atoms, essential for characterizing their quantum states and behaviors. - 🎉 **Bose-Einstein Condensation**: The emergence of Bose-Einstein condensates marks a significant milestone in ultracold atom research, demonstrating collective quantum phenomena.
man got a stroke trying to answer the first quesion haha
33:20 very good point x2
Can models using quantum quench and entanglement predict if rockets repeatedly penetrating the atmosphere, damage the electromagnetic field and the stratosphere of earth?
Nice.
5:00 isn't exactly clear. If I wish to have 10^14 extrinsic carrier concentration, the Fermi level should to which level? What are the red and blue lines for? No image source either :(
The red and blue lines represent the values of Ef-Ei which are plotted against temperature for n and p type semiconductors respectively. Each individual line represents the doping concentration which is mentioned on the line. So for a given temperature, doping type and doping concentration, from the graph we can estimate the value of Ef-Ei and as a result electron and hole concentrations.
7:48
Can it be used to simulate the behvaiour of interface
脳とトポロジーと数理物理学
Is the handout still available somewhere?
9:38 🥶🥶
*Can it predict the simple FT-IR spectrum of H2O?*
beware of 36:14 headphone and earbud users, the sound explodes and i am now deaf
Thanks for this session.I learn a lot about the application of Classical Machine Learning for Quantum Physics.
10:08 what stock does that equation tell us to buy?
why does the french like the monsters more than a french film? perhaps someday when confronted by the future and past will be to the eyes that remembered truely.
Yay🎉 thanks for uploading the video ❤. It's amazing 👏👏👍
is it possible that fixed points in and out of equilibrium is a remainder to volume weight in [critical dimension -rate-] of expansion? in liquid nitrogen in a state of -out of equilibrium- the air inside the balloon become contour weight to volume capacity-volume weight to nitrogen in greater ratio (critical dimension). surface dimension is at a state of dystention to volume nitrogen.
reliant on the properties in nitrogen isn't a comprehension of science necessarily than isolating known material for attribution understanding.
simply put the volume of air in terms of critical dimension is in a state called surface dystension.
once surface dystention is reached to liquid nitrogen the critical dimension in total volume weight is abnormal as it is dismiliar in properties to equilibrium measured in volume weight, becoming a significant change to "fixed point" temperature in ratios.
why is this argument inclusive to temperature fluctuation and not surface dystention to equilibrium? if temperature fluctuations from advent gaseous material is said to increase then what does science say to volume weight to surface dystention?
why is chaos accepted if proverbiality could be incurred? disorder system... to inherent properties? if the known natural state was not to accept atmospheric condition to sodium metal then wouldn't porous material be a problem to equilibrium? what is disorder system than a system in proverbial dissimilar dimension?
is it possible that disorder system is substantiative to a system dimension not yet agreed upon by scientific philosophy?
Oh well thanks for your lecture. I actually applied for one year postgraduate programme in same condensed matter and statistical. I am from Pakistan, Quaid-i-Azam university islamabad. For 2024-2025 session. I hope i will be short listed...
Guessing 575563
Hi, can you please upload lectures of the following courses. It would be really helpful and beneficial for all. 1. Linux Basics (CMP-LB) M. Stella and C. Egan (5 lectures of 1.5 h each) 2. Scientific Python (CMP-SP) I. Davidenkova (20 lectures of 1.5 h each) Thank you very much.
Thank you for the discussion
22:11
Thank you.
Some French seeped through on the second slide: si=if Also, fois=times
4:48
Nice
Awesome content! Thanks a lot for sharing it!
Nice
This is great!
An excellent talk!
Excuse me for inquiring, could you please clarify if pythTB has the capability to handle irregular-shaped supercells, such as a hexagonal supercell?
This is very good. Thanks
Can someone 'explain', in layman's terms, why the quaternions created the 'symplectic' structures (No3 after reals, and complex; t=4634 and lecture 2 at t=344). All the wikipedia articles etc., start with the high-falutin deep maths definitions. I'd like to come at it from the growing out of the real, then complex, then quaternion stepping stones (i.e. quaternions as rotation and expansion of the sphere [while complex is in the Argand 2d plane], with maybe a bit of relativity for 'grounding in reality' ;-). Plus maybe a bit about 'self-dual quaternions' for the win. Coming via bi-complex ideas is also a possibility. (see XKCD Purity for the gap ;-)
Answering my own query: First we should take a step back and re-look at complex numbers as a method of rotating and expanding a 2d vector (or point relative to the 0,0 origin) and that there is a complementary rotation angle that will rotate the vector back to co-align with the original - which is the complex conjugate. Even better if the scaling is unity.. When we get to a layman's 3d space we can always rotate a 3d vector (or point relative to the 0,0,0 origin) about an 'axis' (rotating a globe) and scaling it, using a quaternion representation as an axis, rotation & scale, and we can rotate the vector/globe back to co-align with the original using that same 'axis', and we have the quaternion conjugate, or dual (with usual hand waving simplifications). So, it's about rotation symmetry, and consistency, in the relevant number of dimensions. Background reading "A Beginners Guide to Dual-Quaternions" by Ben Kenwright (I'd already seen that in '21!), and "Teaching Quaternions is not Complex" by J McDonald. Also worth looking (for those with an engineering bent) at using Quaternions in inertial navigation systems and how they were used in the Apollo missions, along with the CORDIC trig algorithms.
Lovely. Like the similarity with the radial Rayleigh distribution. Too much looking at optical spot point spread functions as a Gaussian!
Appreciated for sharing !!!!!!!!! Great content.
Woow....
Sorry can’t listen through this
Cover the Mike when you clear your throat please
Summary of “C. Wetterich, Phys. Lett. B 301 (1993) 90” I guess? But I wanna see author’s analysis : how k’^2=2k^2ln(\frac{ u}{k^2}+1) derived from constant solution h_a(x)=h\delta_a1 with (2 u -\mu^2 + \frac{1}{2}\lambda h^2)h = 2 u\phi, P511 in Nucl. Phys. B334 (1990) 506.
The Best!!
thank you so much for uploading this lecture. Very clear and helpful.
It makes it very clear why MERA of a few layers would be useful for language models. Because in a sentence the important correlations are not necessarily nearest neighbor, but maybe 10th neighbor. It is still short range. A few layers of MERA gets you the potential to capture such finite length cells structure.
The clearest introduction that I found
👍🏼
is the package compatible with spin polarization scf calculations
I understood everything until @0:01
The description seems incorrect; the speaker is Soonwon Choi from MIT. This also happens to a few other videos from this list.