Article
Thermodynamics
Chengcheng Liu, Keli Lin, Yiru Wang, Bin Yang
Summary: In this study, a multi-fidelity neural network-based surrogate model (MFNNSM) is proposed to accelerate the uncertainty quantification (UQ) of chemical kinetic models. The MFNNSM utilizes the similarity between different fidelity samples to transfer or generate high-fidelity samples. Experimental results show that the MFNNSM can achieve acceleration factors up to 6 and increase to 10 when reusing samples under different conditions.
COMBUSTION AND FLAME
(2023)
Article
Engineering, Chemical
Nouha Lyagoubi, Lamiae Vernieres-Hassimi, Leila Khalij, Lionel Estel
Summary: Errors and uncertainties in input parameters have a significant impact on the reliability, quality, and safety of a chemical process. This study investigates the effects of random variations in input parameters on system reliability and highlights the importance of considering these factors in the mathematical design of chemical reactors.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Energy & Fuels
Timothy I. Anderson, Anthony R. Kovscek
Summary: This article presents a generalized approach for calibrating and quantifying uncertainty in chemical reaction parameters for in situ combustion (ISC). The approach is fully automated and applicable to any ISC reaction model based on hydrocarbon pseudocomponents. The method was applied to characterize a heavy crude oil sample and demonstrated its ability to predict ISC oxidation kinetics and scale up to a combustion tube simulation.
Article
Chemistry, Multidisciplinary
Katherine E. Shulenberger, Sophie C. Coppieters 't Wallant, Megan D. Klein, Alexandra R. McIsaac, Tamar Goldzak, David B. Berkinsky, Hendrik Utzat, Ulugbek Barotov, Troy Van Voorhis, Moungi G. Bawendi
Summary: A spectrally resolved correlation method was developed to study the triply excited state, enabling direct measurements of the recombination pathway for the triexciton. It was found that for core-shell CdSe-CdS nanocrystals, triexciton emission arises exclusively from the band-edge S-like state. Time-dependent density functional theory and extended particle-in-a-sphere calculations show that reduced carrier overlap induced by the core-shell heterostructure can account for the lack of emission observed from the P-like state. These results provide insights for potential control of nanocrystal luminescence using core-shell heterostructures.
Review
Materials Science, Multidisciplinary
Pinar Acar
Summary: This paper reviews recent advances in uncertainty quantification in small-scale materials science, highlighting the critical impact of uncertainties on material response and component performance, and categorizing typical sources of uncertainties. It also discusses future techniques and applications, including the integration of uncertainty quantification with design, optimization, and reliability methods, as well as uncertainty quantification in advanced manufacturing.
PROGRESS IN MATERIALS SCIENCE
(2021)
Article
Physics, Multidisciplinary
Matteo Fadel, Ayaka Usui, Marcus Huber, Nicolai Friis, Giuseppe Vitagliano
Summary: This method offers a practical way to measure entanglement in experiments, especially in situations with limited observable measurements, such as quantifying entanglement using measurements of the first and second moments of the collective spin operator.
PHYSICAL REVIEW LETTERS
(2021)
Article
Biophysics
Junghwa Lee, Seungah Lee, Gwang Lee, Seong Ho Kang
Summary: A single-molecule fourplex nanoimmunosensor has been developed for accurate and simultaneous detection of TSH, T3, and T4. This nanoimmunosensor shows a 1015-fold higher detection sensitivity compared to the conventional enzyme-linked immunosorbent assay, making it suitable for early diagnosis and prognosis monitoring of various thyroid diseases.
BIOSENSORS & BIOELECTRONICS
(2023)
Article
Nanoscience & Nanotechnology
Raul D. Rodriguez, Carlos J. Villagomez, Amirhassan Khodadadi, Stephan Kupfer, Andrey Averkiev, Lina Dedelaite, Feng Tang, Mohammad Y. Khaywah, Vladimir Kolchuzhin, Arunas Ramanavicius, Pierre-Michel Adam, Stefanie Graefe, Evgeniya Sheremet
Summary: The interaction of light with metal nanostructures results in strong amplification and localization of electromagnetic fields. In SERS, signal amplification is mainly attributed to electromagnetic enhancement, while the role of chemical enhancement is still debated. Changes in spectral features can be due to different mechanisms, such as molecular orientation and electric field gradient, in addition to chemical enhancement.
Article
Environmental Sciences
Prince Chapman Agyeman, Ndiye Michael Kebonye, Vahid Khosravi, John Kingsley, Lubos Boruvka, Radim Vasat, Charles Mario Boateng
Summary: This study applies two methods to rapidly monitor zinc concentration in agricultural soil, one using visible near-infrared (Vis-NIR) and machine learning algorithms, and the other using Vis-NIR, soil chemical properties, and machine learning algorithms. The results show that the second method performs better in predicting zinc concentration.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Engineering, Industrial
Zihan Wang, Mohamad Daeipour, Hongyi Xu
Summary: This paper proposes a new methodology to quantify and propagate aleatoric uncertainties distributed in complex topological structures. It introduces a random field-based uncertainty representation approach that captures the topological characteristics using the shortest interior path distance. Parameterization methods and non-intrusive uncertainties propagation methods are employed to propagate the uncertainties. Engineering case studies demonstrate the effectiveness of the proposed methodology.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Materials Science, Multidisciplinary
Burigede Liu, Xingsheng Sun, Kaushik Bhattacharya, Michael Ortiz
Summary: The study develops an approach to quantify the overall uncertainty of material response without the need for integral calculations, utilizing the multiscale and hierarchical nature of material response. It effectively bounds uncertainties at different scales and provides a conservative estimate for the overall uncertainty of material behavior assessment.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2021)
Article
Engineering, Biomedical
Bruno Rego, Dar Weiss, Matthew R. Bersi, Jay D. Humphrey
Summary: This study integrated a novel uncertainty quantification and propagation pipeline within an inverse modeling approach, providing detailed quantitative analysis of the biomechanical properties of the ascending thoracic aorta in mouse models. The extended workflow allows systematic reporting of parameter uncertainties and facilitates group-level statistical analyses of vessel wall mechanics.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
(2021)
Article
Engineering, Mechanical
A. Gray, A. Wimbush, M. de Angelis, P. O. Hristov, D. Calleja, E. Miralles-Dolz, R. Rocchetta
Summary: This paper presents a framework for addressing engineering design challenges with limited empirical data and partial information, including characterisation of uncertainties, data integration, reliability analysis, and risk/reliability based design optimization. The framework's efficacy has been demonstrated through its application to the NASA 2020 uncertainty quantification challenge.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Multidisciplinary Sciences
Ross C. C. Leon, Chih Hwan Yang, Jason C. C. Hwang, Julien Camirand Lemyre, Tuomo Tanttu, Wei Huang, Jonathan Y. Huang, Fay E. Hudson, Kohei M. Itoh, Arne Laucht, Michel Pioro-Ladriere, Andre Saraiva, Andrew S. Dzurak
Summary: An error-corrected quantum processor will require millions of qubits, highlighting the advantage of nanoscale devices like silicon quantum dots. This study investigates two spin qubits in a silicon double quantum dot artificial molecule, showing promise for high-performance spin qubits in multi-electron quantum dots. The authors successfully demonstrate a universal gate set and two-qubit Bell state tomography in a high-occupancy double quantum dot in silicon, paving the way for advanced quantum computing applications.
NATURE COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Bi Wang, Jianqing Wu, Xuelian Li, Jun Shen, Yangjun Zhong
Summary: In this paper, a variant of Q-learning, named uncertainty quantification based Q-learning, is proposed by introducing the hedonistic expected value (HEV) to increase the probability of outputting an optimal partial order in online reinforcement learning. The weights assigned by HEV to the successors are compatible with the existing operators, and the prediction of the return is not only the sum over the weights succeeding the operator but also over the weights following HEV through re-weighting. The proposed algorithm with HEV demonstrates favorable performance in practice.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Physical
Robin Feldmann, Andrea Muolo, Alberto Baiardi, Markus Reiher
Summary: In this work, we introduce the nuclear-electronic all-particle density matrix renormalization group (NEAP-DMRG) method to solve the molecular Schrodinger equation. By combining it with the nuclear-electronic Hartree-Fock (NEHF-DMRG) approach, we treat nuclei and electrons equally. We demonstrate that orbital entanglement and mutual information can be used as reliable metrics to detect strong correlation effects.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Moritz Bensberg, Paul L. Tuertscher, Jan P. Unsleber, Markus Reiher, Johannes Neugebauer
Summary: The accurate description of solvent effects is crucial for many chemical processes. In this study, we propose a hybrid approach based on subsystem density functional theory and continuum solvation schemes for the explicit quantum mechanical description of solute-solvent and solvent-solvent interactions. Our model incorporates consistent subsystem decomposition for transferability and demonstrates good scalability for increasing numbers of subsystems. By comparing the resulting free energies to experimental data, we show that our hybrid model accurately reproduces reaction barriers and energies.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Alberto Baiardi, Anna Klara Kelemen, Markus Reiher
Summary: The DMRG[FEAST] method applies the FEAST algorithm in the density matrix renormalization group (DMRG) algorithm for optimizing both low- and high-energy eigenstates, overcoming limitations of existing advanced excited-state DMRG algorithms. The reliability of DMRG[FEAST] is demonstrated by calculating anharmonic vibrational excitation energies of molecules with up to 30 fully coupled degrees of freedom.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Applied
Miguel Steiner, Markus Reiher
Summary: Autonomous computations can make computational catalysis as important as experimental research, with the advantages of systematic, open-ended, and unbiased exploration, addressing more structures and reaction steps while reducing manual work and bias.
TOPICS IN CATALYSIS
(2022)
Article
Chemistry, Physical
Francesco Bosia, Peikun Zheng, Alain Vaucher, Thomas Weymuth, Pavlo O. Dral, Markus Reiher
Summary: This work discusses the impact of various well-established semi-empirical approximations on calculation speed and their relation to data transfer rates. The study considers desktop computers, local high-performance computing, and remote cloud services to elucidate the effect on interactive calculations for different interfaces.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Robin Feldmann, Alberto Baiardi, Markus Reiher
Summary: This work introduces a generalized framework based on concepts from differential geometry for deriving exact and approximate Newton self-consistent field (SCF) orbital optimization algorithms. Within this framework, the augmented Roothaan-Hall (ARH) algorithm is extended to handle unrestricted electronic and nuclear-electronic calculations. The authors demonstrate that ARH offers a great balance between stability and computational cost for SCF problems that are difficult to converge using conventional first-order optimization strategies. For electronic calculations, ARH overcomes the slow convergence of orbitals in correlated molecules, illustrated by examples of iron-sulfur clusters. For nuclear-electronic calculations, ARH significantly improves convergence even for small molecules, as shown with a series of protonated water clusters.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Review
Chemistry, Multidisciplinary
Katja-Sophia Csizi, Markus Reiher
Summary: Quantum mechanics/molecular mechanics (QM/MM) hybrid models are used to study chemical phenomena in complex molecular environments. While this approach allows for large system sizes at moderate computational costs, constructing the models manually can be tedious. Therefore, developing automated procedures for QM/MM model construction is desired. This article reviews the current state of QM/MM modeling with a focus on automation, covering MM model parametrization, QM region selection, and embedding schemes.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2023)
Review
Biochemistry & Molecular Biology
Alberto Baiardi, Matthias Christandl, Markus Reiher
Summary: Molecular biology and biochemistry interpret microscopic processes in terms of molecular structures and interactions, which are quantum mechanical. However, computational solution of quantum mechanical equations is challenging. Classical mechanics is often used to understand molecular function, mapping electron and nucleus interactions onto classical surrogate potentials. This simplifies computation but ignores quantum correlations. This work discusses how quantum computation can improve simulations of biomolecules, considering both quantum mechanical and classical problems in molecular biology, as well as data-driven approaches of bioinformatics.
Article
Chemistry, Multidisciplinary
Matthew D. Wodrich, Ruben Laplaza, Nicolai Cramer, Markus Reiher, Clemence Corminboeuf
Summary: In this mini review, a computational pipeline developed in the framework of NCCR Catalysis is presented, which can successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions. The pipeline is based on the SCINE Molassembler module, a graph-based software that provides molecular construction algorithms for all periodic table elements. With this pipeline, simultaneous functionalization and generation of ensembles of transition state conformers is possible, allowing exploration of the influence of various substituents on the overall enantiomeric ratio. This provides quick and reliable access to energetically low-lying transition states, which is crucial for in silico catalyst optimization.
Article
Chemistry, Multidisciplinary
Moritz Bensberg, Markus Reiher
Summary: Investigating a reactive chemical system with automated reaction network exploration algorithms allows for a more detailed understanding of the chemical mechanism compared to manual investigation. The proposed algorithm identifies and explores kinetically accessible regions of the reaction network in real-time, providing an unprecedented mechanistic picture. Using the example of the multi-component proline-catalyzed Michael addition reaction, the algorithm demonstrates its ability to uncover intricate details of the reaction mechanism.
ISRAEL JOURNAL OF CHEMISTRY
(2023)
Article
Chemistry, Physical
Marco Eckhoff, Markus Reiher
Summary: This paper introduces a machine learning potential (MLP) that can maintain high accuracy and requires little computational demand. By introducing element-embracing atom-centered symmetry functions (eeACSFs), MLPs can be trained for each individual system and uncertainty quantification can be used to continually adapt the MLP. Continual learning strategies are proposed to enable autonomous and on-the-fly training on a continuous stream of new data.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Physical
Moritz Bensberg, Markus Reiher
Summary: This study demonstrates how active orbital spaces can be consistently selected along reaction coordinates in a fully automated way. The approach combines the Direct Orbital Selection orbital mapping ansatz with the fully automated active space selection algorithm AUTOCAS, without the need for structure interpolation between reactants and products. The algorithm is demonstrated for the potential energy profile of the homolytic carbon-carbon bond dissociation and rotation around the double bond of 1-pentene in the electronic ground state, but it also applies to electronically excited Born-Oppenheimer surfaces.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Chemistry, Physical
Robin Feldmann, Alberto Baiardi, Markus Reiher
Summary: In this paper, a symmetry projection technique is presented for enforcing rotational and parity symmetries in nuclear-electronic Hartree-Fock wave functions. A trial wave function with the correct symmetry properties is generated by projecting the wave function onto representations of the three-dimensional rotation group. The efficiency of the technique is demonstrated by calculating the energies of low-lying rotational states of H-2 and H-3(+) molecules.
JOURNAL OF PHYSICAL CHEMISTRY A
(2023)
Article
Chemistry, Multidisciplinary
Enric Petrus, Diego Garay-Ruiz, Markus Reiher, Carles Bo
Summary: In this study, a unique computational approach was used to successfully simulate the self-assembly processes of metal-oxide nanoclusters. By estimating activation energies and correcting pK (a) values, multi-time-scale kinetic simulations were conducted, reproducing reactions ranging from tens of femtoseconds to months of reaction time. Analysis of the kinetic data and reaction network topology revealed the details of the main reaction mechanisms, explaining the origin of kinetic and thermodynamic control. Simulations at alkaline pH fully reproduced experimental evidence as clusters did not form under those conditions.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2023)
Article
Chemistry, Physical
Robin Feldmann, Alberto Baiardi, Markus Reiher
Summary: We propose a symmetry projection technique for enforcing rotational and parity symmetries in nuclear-electronic Hartree-Fock wave functions, which treats electrons and nuclei equally. By projecting the wave function onto representations of the three-dimensional rotation group, the technique generates a trial wave function with the correct symmetry properties and makes the wave function an eigenfunction of the angular momentum operator.
JOURNAL OF PHYSICAL CHEMISTRY A
(2023)