Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

Rappoport, Dmitrij; Galvin, Cooper J.; Zubarev, Dmitry Yu.; Aspuru‐Guzik, Alán

doi:10.1021/ct401004r

Cited by 133 publications

(164 citation statements)

References 99 publications

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“…Of these,itchooses node with score 8( which is lower than any node in the PQ) and explores into two daughter nodes with scores 61 and 76 (Figure 18 d). These,h owever,a re higher in score than the best PQ option (35), so the algorithm reverts "locally" into node 8, finds nothing to explore there,r everts into node 5 (better than 35 in the PQ) and expands into node with score 10 (so far so good) but then encounters three high-score nodes 60,92,98 (Figure 18 e). Having failed to find any promising syntheses branching from node 5, the algorithm keeps 8and 10 as already visited, adds 60,61,76,91,92,98 to the PQ (which now comprises nodes 35,60,61,63,71,76,85,91,92,93,97,98) and moves to the best available PQ option.…”

Section: "Intelligent" Searchesmentioning

confidence: 99%

“…In this particular example,t his best option is the already explored endpoint 35. This endpoint is now expanded into nodes 56,68,73,88, of which the first is better than any entries in the PQ (now 60,61,63,68,71,73,76,85,88,91,92,93,97,98) (Figure 18 f). Accordingly,t he algorithm moves to 56 and expands it into options with scores 57, 64 and 77 (Figure 18 g).…”

Section: "Intelligent" Searchesmentioning

confidence: 99%

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Computer‐Assisted Synthetic Planning: The End of the Beginning

et al. 2016

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Exactly half a century has passed since the launch of the first documented research project (1965 Dendral) on computer-assisted organic synthesis. Many more programs were created in the 1970s and 1980s but the enthusiasm of these pioneering days had largely dissipated by the 2000s, and the challenge of teaching the computer how to plan organic syntheses earned itself the reputation of a "mission impossible". This is quite curious given that, in the meantime, computers have "learned" many other skills that had been considered exclusive domains of human intellect and creativity-for example, machines can nowadays play chess better than human world champions and they can compose classical music pleasant to the human ear. Although there have been no similar feats in organic synthesis, this Review argues that to concede defeat would be premature. Indeed, bringing together the combination of modern computational power and algorithms from graph/network theory, chemical rules (with full stereo- and regiochemistry) coded in appropriate formats, and the elements of quantum mechanics, the machine can finally be "taught" how to plan syntheses of non-trivial organic molecules in a matter of seconds to minutes. The Review begins with an overview of some basic theoretical concepts essential for the big-data analysis of chemical syntheses. It progresses to the problem of optimizing pathways involving known reactions. It culminates with discussion of algorithms that allow for a completely de novo and fully automated design of syntheses leading to relatively complex targets, including those that have not been made before. Of course, there are still things to be improved, but computers are finally becoming relevant and helpful to the practice of organic-synthetic planning. Paraphrasing Churchill's famous words after the Allies' first major victory over the Axis forces in Africa, it is not the end, it is not even the beginning of the end, but it is the end of the beginning for the computer-assisted synthesis planning. The machine is here to stay.

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Section: "Intelligent" Searchesmentioning

confidence: 99%

Section: "Intelligent" Searchesmentioning

confidence: 99%

Computer‐Assisted Synthetic Planning: The End of the Beginning

et al. 2016

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“…In the past, automatic retrosynthesis or reaction prediction (see Figure 5a) required information from databases and/or the manual encoding of chemical rules. Newer developments include the automated extraction of reaction rules, [143] new models for chemical reasoning, [144] heuristics aided methods, [145] and the use of machine learning. Some of the most widely used systems are ChemPlanner, [141] PathFinder, ICSynth, LHASA, CAMEO, SOPHIA, and EROS.…”

Section: Synthetic Accessibilitymentioning

confidence: 99%

“…More recent approaches to the prediction of reactions and retrosynthesis may solve several problems that limit the systems used so far. Newer developments include the automated extraction of reaction rules, [143] new models for chemical reasoning, [144] heuristics aided methods, [145] and the use of machine learning. In particular, the application of machine learning is very promising with respect to high throughput reaction prediction and retrosynthesis.…”

Section: Synthetic Accessibilitymentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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organic/inorganic perovskites solar cells [6] to printable electronic circuits based on organic field-effect transistors (OFETs). [7] While OLED displays outperform their inorganic counterparts in terms of energy efficiency, [8] scientific and technical challenges concerning the stability and processability of the organic materials used in large-area OLEDs and organic solar cells remain. New challenges arise from applications, such as displays on flexible substrates, OLED lightning, large area displays as well as for printable or solution processable larger area solar cells. [8] Many of the remaining challenges are material related, e.g., the low mobility of charge carriers in organic materials in general and in amorphous organic semiconductors in particular. There are other materials related issues, such as limited OLED life times due to unstable blue hosts and emitters, [9] low fill factors, and therefore reduced power conversion efficiencies of organic solar cells, [10,11] low conductivity and high costs of organic charge transport layers of perovskite solar cells [6,12,13] and low conductivity and hard processability of crystalline OFET materials. [14] Conductivity and injection can be improved by doping the organic thin films with molecular dopants with high electron affinities (p-type) [15] or low ionization energies (n-type). [16] The doping mechanism of organic materials is in many cases not well understood, [17] making material and device optimization a costly experimental endeavor.The development of better materials is presently based on chemical insight, in part guided by theoretical understanding, or the experimental screening of large numbers of compounds. Given the size of the potentially available chemical space this remains a costly and time-consuming approach. Recent successes in experimental design of novel materials and concepts include the development of a stable strong molecular n-type dopant, [16] a study about the quantitative relation between interaction parameter, miscibility, and function of conjugated polymer donors and small-molecule acceptors for bulk heterojunctions as used in organic solar cells [18] the development of a universal strategy for ohmic hole injection into organic semiconductors with high ionization energies [19] and many others. [20][21][22][23][24][25] Another example is the development of strategies to harvest triplet excitons in organic light emitting diodes. For this purpose, novel classes of emitter molecules were developed, which include thermally activated delayed fluorescence (TADF)-based molecules, [26][27][28][29][30][31][32] rotationally accessed spin-state inversion, [33] and radical-based emitters. [34] Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly inf...

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“…This network, which also specifies connections between saddles and minima as the edges in the graph, provides important information for studying mechanisms of conformational changes in the system. A related representation was proposed to set up a network for organic reactions (36). In the present scheme, our graph representation allows us to apply graph analysis methods on the HDFES in which the vertices are characterized by a set of key features or molecular structural similarity, so that important properties of the system can be subsequently revealed and elucidated.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Locating landmarks on high-dimensional free energy surfaces

Chen¹,

Tuckerman

2015

Proc. Natl. Acad. Sci. U.S.A.

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Coarse graining of complex systems possessing many degrees of freedom can often be a useful approach for analyzing and understanding key features of these systems in terms of just a few variables. The relevant energy landscape in a coarse-grained description is the free energy surface as a function of the coarsegrained variables, which, despite the dimensional reduction, can still be an object of high dimension. Consequently, navigating and exploring this high-dimensional free energy surface is a nontrivial task. In this paper, we use techniques from multiscale modeling, stochastic optimization, and machine learning to devise a strategy for locating minima and saddle points (termed "landmarks") on a high-dimensional free energy surface "on the fly" and without requiring prior knowledge of or an explicit form for the surface. In addition, we propose a compact graph representation of the landmarks and connections between them, and we show that the graph nodes can be subsequently analyzed and clustered based on key attributes that elucidate important properties of the system. Finally, we show that knowledge of landmark locations allows for the efficient determination of their relative free energies via enhanced sampling techniques.free energy surface | stochastic optimization | activation-relaxation | machine learning | network representation U nderstanding the conformational equilibria of complex systems remains a significant challenge in the computational molecular sciences. Whether one is interested in predicting biomolecular structure, generating and thermodynamically ranking the polymorphs of molecular crystals, or studying the phase behavior of complex materials, the very large number of degrees of freedom renders such problems highly nontrivial. Often the most important conformational states in a system can be characterized in terms of a subset of collective degrees of freedom or "collective variables" (CVs), and the problem of mapping out the conformational equilibria amounts to generating the marginal probability distribution in these CVs, from which the associated free energy surface (FES) can be generated. Unfortunately, due to the existence of many minima on the potential energy surface separated by high barriers, transitions from basin to basin on this surface are rare, so that the FES cannot be generated on any reasonable timescale using standard molecular dynamics (MD) or Monte Carlo (MC) methods. Various enhanced sampling approaches have been devised to accelerate the exploration of such "rough" or "frustrated" energy landscapes to generate such FESs, either by elevating the temperature in the subspace of the CVs (1-3) or by applying a bias potential on the FES (4-7) as in the popular metadynamics approach (4, 5). Recently, we have shown that these two classes of methods can be effectively combined (8), and others have shown that various types of free energy dynamics are possible (9).Although it is often claimed that a low-dimensional manifold involving the selected CVs and embedded in the full phas...

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Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

Cited by 133 publications

References 99 publications

Computer‐Assisted Synthetic Planning: The End of the Beginning

Computer‐Assisted Synthetic Planning: The End of the Beginning

Toward Design of Novel Materials for Organic Electronics

Locating landmarks on high-dimensional free energy surfaces

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