Quantum algorithm for alchemical optimization in material design

Barkoutsos, Panagiotis Kl.; Gkritsis, Fotios; Ollitrault, Pauline J.; Sokolov, Igor O.; Woerner, Stefan; Tavernelli, Ivano

doi:10.1039/d0sc05718e

Cited by 19 publications

(13 citation statements)

References 36 publications

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“…Inverse molecular design optimizes molecular species with respect to desired functionalities in the chemical space. Chemical space-based inverse molecular design approaches involve the variational particle number (variable proton and electron number) method, the linear combination of atomic potentials (LCAP), and quantum computation algorithm-based alchemical optimization . These approaches enable atomic-level design by using the information on target functionalities at the first-principles level of theory to guide the exploration toward a functional molecule in the chemical space.…”

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confidence: 99%

“…Chemical space-based inverse molecular design approaches involve the variational particle number (variable proton and electron number) method, 3 the linear combination of atomic potentials (LCAP), 4 and quantum computation algorithmbased alchemical optimization. 5 These approaches enable atomic-level design by using the information on target functionalities at the first-principles level of theory to guide the exploration toward a functional molecule in the chemical space. For this purpose, computational quantum alchemy is effective for high-throughput screening because the perturbation approach can avoid exhaustive enumeration by standard quantum chemistry calculations.…”

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confidence: 99%

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Exploration of Chemical Space for Designing Functional Molecules Accounting for Geometric Stability

Shiraogawa

Hasegawa

2022

J. Phys. Chem. Lett.

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The design of functional molecules is regarded as searching for molecules with desired functionalities in chemical space populated by candidate molecules. Considering the geometric stability of molecules during the search process is crucial for designing realistic molecules. Here, we propose a method for designing functional molecules by exploring chemical space while explicitly accounting for geometric stability based on computational quantum alchemy. The proposed design method allows the simultaneous prediction of functional molecule in the equilibrium structure and its target desired property in an inverse design fashion without preparing the molecular geometries and performing brute-force screening. The applicability of the design method is proven by obtaining molecules with the desired atomization and electronic energies in various chemical spaces: (BF, CO), (CH4, NH3), 18 BN-doped benzene derivatives, and 3.1 × 105 BN-doped phenanthrene derivatives.

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confidence: 99%

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confidence: 99%

Exploration of Chemical Space for Designing Functional Molecules Accounting for Geometric Stability

Shiraogawa

Hasegawa

2022

J. Phys. Chem. Lett.

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“…That is, while many of the proposed simulations would be faster and more accurate representations of the same systems than would be achievable classically, they are much the same type of experiment without reaching into the design space. While there has been some notion of how quantum search may assist in design, most results promise at most a quadratic speedup in contrast to ex-ponential speedups in direct simulation [29], though we note the direct simulation subroutines used in a potential search would still benefit from improved speed or accuracy. Due to the immense size of the design space, this quadratic speedup is not terribly compelling if it is not combined with structured strategies.…”

Section: Digital Chemistry Experiments With Quantum Computersmentioning

confidence: 95%

What the foundations of quantum computer science teach us about chemistry

McClean

Rubin

Harrigan

et al. 2021

The Journal of Chemical Physics

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With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.

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“…Candidate sensing materials such as MOFs , and biomolecules are computationally expensive to screen due to their complexity and size. Quantum computers may therefore supplement ongoing efforts to computationally design and screen potential metal extraction agents. , Studies currently underway apply quantum computers for material design , and modeling binding site interactions, and similar techniques may be applied to develop highly selective chelation agents.…”

Section: Quantum Computing and Simulations For Energy Applicationsmentioning

confidence: 99%

Quantum Computing and Simulations for Energy Applications: Review and Perspective

et al. 2022

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Quantum computing and simulations are creating transformative opportunities by exploiting the principles of quantum mechanics in new ways to generate and process information. It is expected that a variety of areas ranging from day-to-day activities to making advanced scientific discoveries are going to benefit from such computations. Several early stage applications of quantum computing and simulation have already been demonstrated, and these preliminary results show that quantum computing and simulations could significantly accelerate the deployment of new technologies urgently needed to meet the growing demand for energy while safeguarding the environment. Exciting examples include developing new materials such as alloys, catalysts, oxygen carriers, CO2 sorbents/solvents, and energy storage materials; optimizing traffic flows and energy supply chains; locating energy generation facilities such as wind and solar farms and fossil and nuclear power plants; designing pipeline networks for transporting hydrogen, natural gas, and CO2; and speeding up tasks such as seismic imaging and inversion, reservoir simulation, and computational fluid dynamics. In this review, we introduce different aspects of quantum computing and simulations and discuss the status of theoretical and experimental approaches. We then specifically highlight a growing number of application areas in the energy sector. We conclude by providing an analysis of high-value application directions to address energy sector challenges.

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Quantum algorithm for alchemical optimization in material design

Abstract: The development of tailored materials for specific applications is an active field of research in chemistry, material science and drug discovery. The number of possible molecules obtainable from a set...

Cited by 19 publications

References 36 publications

Exploration of Chemical Space for Designing Functional Molecules Accounting for Geometric Stability

Exploration of Chemical Space for Designing Functional Molecules Accounting for Geometric Stability

What the foundations of quantum computer science teach us about chemistry

Quantum Computing and Simulations for Energy Applications: Review and Perspective

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