scholarly journals Quantum algorithm for alchemical optimization in material design

2021 ◽  
Author(s):  
Panagiotis Kl. Barkoutsos ◽  
Fotios Gkritsis ◽  
Pauline J. Ollitrault ◽  
Igor O. Sokolov ◽  
Stefan Woerner ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Drug Discovery ◽  
Chemical Composition ◽  
Quantum Algorithm ◽  
Material Design ◽  
Quantum Mechanical ◽  
Near Future

‘Alchemical’ quantum algorithm for the simultaneous optimisation of chemical composition and electronic structure for material design. By exploiting quantum mechanical principles this approach will boost drug discovery in the near future.

Structural/Property Relationships for Optical Materials

1993 ◽  
Vol 329 ◽  
Author(s):  
Vivien D.
Keyword(s):  
Crystal Structure ◽  
Optical Properties ◽  
Electronic Structure ◽  
Chemical Composition ◽  
Field Strength ◽  
Optical Materials ◽  
Site Occupancy ◽  
Laser Materials ◽  
Crystal Field Strength ◽  
The Matrix

AbstractIn this paper the relationships between the crystal structure, chemical composition and electronic structure of laser materials, and their optical properties are discussed. A brief description is given of the different laser activators and of the influence of the matrix on laser characteristics in terms of crystal field strength, symmetry, covalency and phonon frequencies. The last part of the paper lays emphasis on the means to optimize the matrix-activator properties such as control of the oxidation state and site occupancy of the activator and influence of its concentration.

New Insights into the Nature of the Band Gap of CuGeO3 Nanoparticles: Synthesis, Electronic Structure, and Optical and Photocatalytic Properties

2019 ◽  
Author(s):  
Victor Y. Suzuki ◽  
Luís Henrique Cardozo Amorin ◽  
Natália H. de Paula ◽  
Anderson R. Albuquerque ◽  
Julio Ricardo Sambrano ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Band Gap ◽  
Material Design ◽  
Photocatalytic Properties ◽  
Critical Parameters ◽  
Synthetic Approach ◽  
Microwave Assisted ◽  
Theoretical Approaches ◽  
Nanoparticles Synthesis ◽  
First Time

<p>We report, for the first time, new insights into the nature of the band gap of <a>CuGeO<sub>3</sub> </a>(CGO) nanocrystals synthesized from a microwave-assisted hydrothermal method in the presence of citrate. To the best of our knowledge, this synthetic approach has the shortest reaction time and it works at the lowest temperatures reported in the literature for the preparation of these materials. The influence of the surfactant on the structural, electronic, optical, and photocatalytic properties of CGO nanocrystals is discussed by a combination of experimental and theoretical approaches, and that results elucidates the nature of the band gap of synthetized CGO nanocrystals. We believe that this particular strategy is one of the most critical parameters for the development of innovative applications and that result could shed some light on the emerging material design with entirely new properties.</p> <p><b> </b></p>

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

Chemical composition and electronic structure of the passive layer formed on stainless steels in a glucose-oxidase solution

2008 ◽  
Vol 54 (1) ◽  
pp. 123-132 ◽  
Author(s):  
C. Marconnet ◽  
Y. Wouters ◽  
F. Miserque ◽  
C. Dagbert ◽  
J.-P. Petit ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Chemical Composition ◽  
Glucose Oxidase ◽  
Stainless Steels ◽  
Passive Layer

Modern quantum mechanical techniques and computations on the electronic structure of polymers

2005 ◽  
pp. 183-188
Author(s):  
J. M. André ◽  
B. Champagne ◽  
J. Delhalle ◽  
J. G. Fripiat ◽  
D. H. Mosley
Keyword(s):  
Electronic Structure ◽  
Quantum Mechanical

scholarly journals Quantum simulation of chemistry with sublinear scaling in basis size

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Ryan Babbush ◽  
Dominic W. Berry ◽  
Jarrod R. McClean ◽  
Hartmut Neven
Keyword(s):  
Electronic Structure ◽  
Plane Wave ◽  
Quantum Chemistry ◽  
Plane Waves ◽  
Quantum Algorithm ◽  
Discretization Error ◽  
Rotating Frame ◽  
Interaction Picture ◽  
Basis Size ◽  
Oppenheimer Approximation

Abstract We present a quantum algorithm for simulating quantum chemistry with gate complexity $$\tilde {\cal{O}}(N^{1/3}\eta ^{8/3})$$ O ̃ ( N 1 ∕ 3 η 8 ∕ 3 ) where η is the number of electrons and N is the number of plane wave orbitals. In comparison, the most efficient prior algorithms for simulating electronic structure using plane waves (which are at least as efficient as algorithms using any other basis) have complexity $$\tilde {\cal{O}}(N^{8/3}{\mathrm{/}}\eta ^{2/3})$$ O ̃ ( N 8 ∕ 3 ∕ η 2 ∕ 3 ) . We achieve our scaling in first quantization by performing simulation in the rotating frame of the kinetic operator using interaction picture techniques. Our algorithm is far more efficient than all prior approaches when N ≫ η, as is needed to suppress discretization error when representing molecules in the plane wave basis, or when simulating without the Born-Oppenheimer approximation.

scholarly journals Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

2020 ◽  
Author(s):  
Nicolas Tielker ◽  
Lukas Eberlein ◽  
Gerhard Hessler ◽  
K. Friedemann Schmidt ◽  
Stefan Güssregen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Method Development ◽  
Biological Data ◽  
Quantum Mechanical ◽  
Model Assessment ◽  
Ligand Interaction ◽  
Blind Prediction ◽  
Mechanical Methods

Abstract Joint academic–industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein–ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum–mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum–mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol–water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia–industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.

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