scholarly journals More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

2020 ◽  
Author(s):  
Qiyuan Zhao ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Domain Knowledge ◽  
Low Cost ◽  
Computational Cost ◽  
Chemical Degradation ◽  
Reaction Pathways ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Success Rates ◽  
Reaction Prediction

<div> <div> <div> <p>Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </p> </div> </div> </div>

scholarly journals More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

2020 ◽  
Author(s):  
Qiyuan Zhao ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Domain Knowledge ◽  
Low Cost ◽  
Computational Cost ◽  
Chemical Degradation ◽  
Reaction Pathways ◽  
Reaction Networks ◽  
Complex Reaction ◽  
Success Rates ◽  
Reaction Prediction

<div> <div> <div> <p>Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </p> </div> </div> </div>

Reactions of an Aluminium(I) Reagent with 1,2-, 1,3- and 1,5-Dienes: Dearomatisation, Reversibility, and a Pericyclic Mechanism

2019 ◽  
Author(s):  
Clare Bakewell ◽  
Martí Garçon ◽  
Richard Y Kong ◽  
Louisa O'Hare ◽  
Andrew J. P. White ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Dft Calculations ◽  
Transition State ◽  
Reaction Pathways ◽  
Aromatic Rings ◽  
Aromatic Character ◽  
Pericyclic Reaction

The reactions of an aluminium(I) reagent with a series of 1,2-, 1,3- and 1,5-dienes are reported. In the case of 1,3-dienes the reaction occurs by a pericyclic reaction mechanism, specifically a cheletropic cycloaddition, to form aluminocyclopentene containing products. This mechanism has been interrogated by stereochemical experiments and DFT calculations. The stereochemical experiments show that the (4+1) cycloaddition follows a suprafacial topology, while calculations support a concerted albeit asynchronous pathway in which the transition state demonstrates aromatic character. Remarkably, the substrate scope of the (4+1) cycloaddition includes dienes that are either in part, or entirely, contained within aromatic rings. In these cases, reactions occur with dearomatisation of the substrate and can be reversible. In the case of 1,2- or 1,5-dienes complementary reactivity is observed; the orthogonal nature of the C=C π-bonds (1,2-diene) and the homoconjugated system (1,5-diene) both disfavour a (4+1) cycloaddition. Rather, reaction pathways are determined by an initial (2+1) cycloaddition to form an aluminocyclopropane intermediate which can in turn undergo insertion of a further C=C π-bond leading to complex organometallic products that incorporate fused hydrocarbon rings.

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

scholarly journals Non-normality and non-monotonic dynamics in complex reaction networks

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Zachary G. Nicolaou ◽  
Takashi Nishikawa ◽  
Schuyler B. Nicholson ◽  
Jason R. Green ◽  
Adilson E. Motter
Keyword(s):  
Reaction Networks ◽  
Complex Reaction

scholarly journals Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5038
Author(s):  
Kosuke Shima ◽  
Masahiro Yamaguchi ◽  
Takumi Yoshida ◽  
Takanobu Otsuka
Keyword(s):  
Inventory Management ◽  
Low Cost ◽  
Computational Cost ◽  
Low Energy ◽  
Manufacturing Strategy ◽  
Implementation Cost ◽  
Measurement Systems ◽  
Process Route ◽  
Kanban System ◽  
Low Computational Cost

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.

scholarly journals Uncertainty quantification for quantum chemical models of complex reaction networks

2016 ◽  
Vol 195 ◽  
pp. 497-520 ◽  
Author(s):  
Jonny Proppe ◽  
Tamara Husch ◽  
Gregor N. Simm ◽  
Markus Reiher
Keyword(s):  
Free Energy ◽  
Electronic Structure ◽  
State Space ◽  
Time Scales ◽  
Reaction Network ◽  
Reaction Networks ◽  
Multiple Time Scales ◽  
Complex Reaction ◽  
Discrete State ◽  
Continuous State

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

scholarly journals Evolution of Wireless Sensor Network for Air Quality Measurements

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 342 ◽  
Author(s):  
Patricia Arroyo ◽  
Jesús Lozano ◽  
José Suárez
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Low Cost ◽  
Principal Component ◽  
Solar Panels ◽  
Success Rates ◽  
Sampling System ◽  
Air Quality Measurements ◽  
Zigbee Protocol ◽  
Benzene Toluene

This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).

scholarly journals Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

AbstractGraph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

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