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Mohammad Ghazi Vakili on Muck Rack

Mohammad Ghazi Vakili

As seen in: Nature
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Articles

PEO-sheathed liquid jets increase sample delivery stability for serial femtosecond X-ray crystallography

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By Mohammad Ghazi Vakili, Saša Bajt, Johan Bielecki, Sabine Botha, Henry Chapman, Carsten Deiter, Raphael de Wijn, Katerina Dorner, Petra Fromme, Raimund Fromme, Alfonso Calvo, Huijong Han, Chan Kim, Marco Kloos, Romain Letrun, José M. Montanero, Tomas Popelar, Alejandro Gonzalez Verified, Alejandro González Verified, Christina Schmidt, Sunidhi Sharma, Egor Sobolev, Oleksii Turkot, Richard Bean, Claire Cerniglia, Faisal H. M. Koua, José Domingo Meza-Aguilar, Katherine Morin, Jui-Tung Tseng, Agnieszka Wrona, Jay-How Yang, Gayathri Yogaganeshan
| Nature Verified
Abstract Viscoelastic jets can be generated by the polyethylene oxide (PEO) sheathing of an aqueous solution using double-flow focusing nozzles (DFFNs) and represent an efficient method to deliver samples that are dispersed in low and medium-viscosity liquids for X-ray diffractive imaging experiments. Due to their micrometre diameter and millimetre length, such jets can be used for pump–probe serial femtosecond crystallography (SFX) in order to access a timescale of a few tens of microseconds.
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Minimized sample consumption for time-resolved serial crystallography applied to the redox cycle of human NQO1 - Communications Chemistry

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By Diandra Doppler, Abhik Manna, Hung Le, Hùng Lê, Mukul Sonker, Gihan Ketawala, Romain Letrun, Katerina Dorner, Raphael de Wijn, Johan Bielecki, Huijong Han, Chan Kim, Adam Round, Abhisakh Sarma, Tokushi Sato, Christina Schmidt, Mohammad Ghazi Vakili, Richard Bean, Joachim Schulz, Raimund Fromme, Milagros Medina, Thomas Grant, Petra Fromme, Richard A. Kirian, Sabine Botha, José Martín-García, Sayantee Sen
| Nature Verified
Abstract Sample consumption for serial femtosecond crystallography with X-ray free electron lasers remains a major limitation preventing broader use in macromolecular crystallography. This drawback is exacerbated in time-resolved (TR) experiments, where the amount of sample required per reaction time point is multiplied by the number of time points investigated.
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Enhancing combinatorial optimization with classical and quantum generative models - Nature Communications

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By Mohammad Ghazi Vakili, Can B. Kalayci
| Nature Verified
Abstract Devising an efficient exploration of the search space is one of the key challenges in the design of combinatorial optimization algorithms. Here, we introduce the Generator-Enhanced Optimization (GEO) strategy: a framework that leverages any generative model (classical, quantum, or quantum-inspired) to solve optimization problems. We focus on a quantum-inspired version of GEO relying on tensor-network Born machines, and referred to hereafter as TN-GEO.
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