Congratulations to Michael van Stipdonk, Theodore Corcovilos, and their research group for recently publishing their paper, "Intrinsic reactivity of [OUCH]+: Apparent synthesis of [OUS]+ by reaction with CS2" in Rapid Communications in Mass Spectrometry. Read the abstract below!
Ken Jordan served as a guest editor for a special issue of the Journal of Chemical Physics titled “Frontiers of stochastic electronic structure calculations.” Read part of the abstract below.
Abstract:
In recent years there has been a rapid growth in the development and application of new stochastic methods in electronic structure. These methods are quite diverse, from many-body wave function techniques in real space or determinant space to being used to sum perturbative expansions. This growth has been spurred by the more favorable scaling with the number of electrons and often better parallelization over large numbers of central processing unit (CPU) cores or graphical processing units (GPUs) than for high-end non-stochastic wave function based methods. This special issue of the Journal of Chemical Physics includes 33 papers that describe recent developments and applications in this area. As seen from the articles in the issue, stochastic electronic structure methods are applicable to both molecules and solids and can accurately describe systems with strong electron correlation.
The special issue tackles six subtopics: auxiliary-field quantum monte carlo, real-space methods, perturbation theory, quantum chemistry methods, application to finite systems, and application to infinite systems. Read more here.
Metal-Organic Frameworks: Outlooks and Opportunities
Register and learn more at connect.acspubs.org/JACSInSessionWith
As Metal-Organic Frameworks enter their third decade, new fundamental insights are still being uncovered even as applications spread through every branch of chemistry and commercial products hit the market.
JACS in...
Wei Xiong and Maysam Chamanzar were both recently awarded NSF CAREER grants for their outstanding work.
Wei Xiong’s project will study the tradeoff between strength and ductility of alloy components created by additive manufacturing. His team aims to find a way to overcome the problem that the stronger a material is, the less ductile it becomes, and they will design new alloys that can be additively manufactured. This could help reduce the number of alloy powders needed for 3D printing, save the cost of alloy powder production for various engineering purposes, and provide recipes to recycle and reuse existing metal powders.
Maysam Chamanzar’s research will present a new type of neural probe that uses graphene to change brain signals to electromagnetic waves. This will increase the number of recording channels without increasing the size of the probe since a large probe could cause brain damage. The research could provide insight into treatments for brain disorders like epilepsy, Parkinson’s and Alzheimer’s.
Congrats Wei and Maysam!
Pure dephasing effects in superconducting flux qubits and classical simulation of entanglement
Zoom Link: https://pitt.zoom.us/j/92738959286
Abstract:
Advancements in the technology of quantum bits invoke more precise calculations of decoherence and dissipative effects. Such noise effects are a result of entanglement of qubits with their surrounding environment. It is interesting to see if the entanglement can be simulated by classical noises...
Congratulations to Hrvoje Petek and his team for their paper “Plasmonically assisted channels of photoemission from metals,” which was recently selected as an Editor’s Choice by APS Physics! Their research focuses on nonlinear photoemission spectra from Ag surfaces and single- and multiplasmon excitations. Check out the abstract here!
From the periodic table to new magnets: climbing the inverse design mountain
Abstract: The development of novel materials is a fundamental enabler for any technology, to the point that often technology and materials innovation cannot be separated. Unfortunately the process of finding new materials, optimal for a given application, is lengthy, often unpredictable, and has a low throughput. Here I will describe a systematic pathway to the discovery of novel compounds, which demonstrates an unprecedented throughput and discovery speed. The method can...
Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation Using Meta-VQE
Zoom Link: https://cmu.zoom.us/j/99244798052pwd=dTlCYkpHK3kzdStEd3FuWWU5amJ4dz09
Website: https://www.cmu.edu/aced/sciML.html
Abstract: Alba will present the meta-VQE, an algorithm capable to learn the ground state energy profile...
Understanding Nanoscale and Molecular Processes in Emulsions Systems
See CMU Chemistry departmental email or contact the host, Olexandr Isayev, at olexandr@cmu.edu for zoom link.
Abstract: Emulsions are dynamic and complex systems with several physical mechanisms possibly leading to the formation, destruction and transformation of their interfacial and bulk structures. Simultaneously, emulsions are increasingly being used in consumer products (creams, cosmetics), for the synthesis of...
Reinforcement Learning With Quantum Neural Networks
Zoom link: https://cmu.zoom.us/j/99244798052?pwd=dTlCYkpHK3kzdStEd3FuWWU5amJ4dz09
Website: https://www.cmu.edu/aced/sciML.html
Abstract: Quantum machine learning has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. Research in this area has focused primarily on variational quantum algorithms, and several proposals to enhance supervised, unsupervised and reinforcement learning algorithms with quantum computing have been put forward. Out of the three, RL is the least studied and it is still an open question whether near-term quantum algorithms can be competitive with state-of-the-art classical approaches based on neural networks even on simple benchmark tasks. In this talk, I will introduce a variational quantum algorithm for deep Q-learning and explain which architectural choices of the quantum model are crucial to make it competitive with its classical counterpart on a benchmark learning task.