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1.
J Am Chem Soc ; 2020 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-32602711

RESUMO

Sodium cyanoborohydride-derived N-alkylnitriliumboranes were found to be versatile precursors for the synthesis of novel boron-containing heterocycles. The reaction between N-alkylnitriliumboranes and 2-aminopyridines, imidazoles, oxazoles, or isoxazoles leads to the incorporation of the [B-C] motif into a five-membered boramidine, which exists as a mixture of Z and E isomers. The resulting heterocycles are blue fluorescent in both the solid state and in solution with ca. 2700-8400 cm-1 Stokes shifts and quantum yields in the 65-74% range in water and in the 42-84% range in organic solvents. The combination of photophysical properties, structural tunability, stability, and solubility in various media is expected to find application in a range of disciplines.

2.
J Am Chem Soc ; 2020 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-32609494

RESUMO

The ability to understand and predict reactivity is essential for the development of new reactions. In the context of Ni-catalyzed C(sp3)-O functionalization, we have developed a unique strategy employing activated cyclopropanols to aid the design and optimization of a redox-active leaving group for C(sp3)-O arylation. In this chemistry, the cyclopropane ring acts as a reporter of leaving-group reactivity, since the ring-opened product is obtained under polar (2e) conditions, and the ring-closed product is obtained under radical (1e) conditions. Mechanistic studies demonstrate that the optimal leaving group is redox-active and are consistent with a Ni(I)/Ni(III) catalytic cycle. The optimized reaction conditions are also used to synthesize a number of arylcyclopropanes, which are valuable pharmaceutical motifs.

3.
Chem Soc Rev ; 49(11): 3525-3564, 2020 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-32356548

RESUMO

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

5.
ACS Nano ; 14(6): 6589-6598, 2020 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-32338888

RESUMO

Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.

6.
PLoS One ; 15(4): e0229862, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32298284

RESUMO

The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.


Assuntos
Inteligência Artificial , Cromatografia Líquida de Alta Pressão/estatística & dados numéricos , Química Computacional , Software , Algoritmos , Automação/métodos , Internet das Coisas , Robótica
7.
J Am Chem Soc ; 2020 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-32118416

RESUMO

Layered Na-based oxides with the general composition of NaxTMO2 (TM: transition metal) have attracted significant attention for their high compositional diversity that provides tunable electrochemical performance for electrodes in sodium-ion batteries. The various compositions bring forward complex structural chemistry that is decisive for the layered stacking structure, Na-ion conductivity, and the redox activity, potentially promising new avenues in functional material properties. In this work, we have explored the maximum Na content in P2-type layered oxides and discovered that the high-content Na in the host enhances the structural stability; moreover, it promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni2+). This can be rationalized by the increased hybridization of the O(2p)-TM(3d-eg*) states, affecting both the local TM environment as well as the interactions between the NaO2 and TMO2 layers. These properties are highly beneficial for the Na storage capabilities as required for cathode materials in sodium-ion batteries. It leads to excellent Na-ion mobility, a large storage capacity (>100 mAh g-1 between 2.0-4.0 V), yet preventing the detrimental sliding of the TMO2 layers (P2-O2 structural transition), as reflected by the ultralong cycle life (3000 (dis)charge cycles demonstrated). These findings expand the horizons of high Na-content P2-type materials, providing new insights of the electronic and structural chemistry for advanced cathode materials.

8.
Adv Mater ; 32(14): e1907801, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32049386

RESUMO

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.

10.
Lab Chip ; 20(4): 709-716, 2020 02 21.
Artigo em Inglês | MEDLINE | ID: mdl-31895394

RESUMO

High-throughput fluidic technologies have increased the speed and accuracy of fluid processing to the extent that unlocking further gains will require replacing the human operator with a robotic counterpart. Recent advances in chemistry and biology, such as gene editing, have further exacerbated the need for smart, high-throughput experimentation. A growing number of innovations at the intersection of robotics and fluidics illustrate the tremendous opportunity in achieving fully self-driving fluid systems. We envision that the fields of synthetic chemistry and synthetic biology will be the first beneficiaries of AI-directed robotic and fluidic systems, and largely fall within two modalities: complex integrated centralized facilities that produce data, and distributed systems that synthesize products and conduct disease surveillance.

11.
Chem Sci ; 10(36): 8360-8366, 2019 Sep 28.
Artigo em Inglês | MEDLINE | ID: mdl-31803414

RESUMO

Tunable and highly conductive hole transport materials are crucial for the performance of organic electronics applications such as organic light emitting diodes and perovskite solar cells. For commercial applications, these materials' requirements include easy synthesis, high hole mobility, and highly tuned and compatible electronic energy levels. Here, we present a systematic study of a recently discovered, easy-to-synthesize class of spiro[fluorene-9,9'-xanthene]-based organic hole transport materials. Systematic side group functionalization allows us to control the HOMO energy and charge carrier mobility. Analysis of the bulk simulations enables us to derive design rules for mobility enhancement. We show that larger functional groups (e.g. methyl) decrease the conformational disorder due to steric effects and thus increase the hole mobility. Highly asymmetric or polar side groups (e.g. fluorine), however, increase the electrostatic disorder and thus reduce the hole mobility. These generally applicable design rules will help in the future to further optimize organic hole transport materials.

12.
J Chem Phys ; 151(12): 121102, 2019 Sep 28.
Artigo em Inglês | MEDLINE | ID: mdl-31575171

RESUMO

Singlet exciton fission is a mechanism that could potentially enable solar cells to surpass the Shockley-Queisser efficiency limit by converting single high-energy photons into two lower-energy triplet excitons with minimal thermalization loss. The ability to make use of singlet exciton fission to enhance solar cell efficiencies has been limited, however, by the sparsity of singlet fission materials with triplet energies above the bandgaps of common semiconductors such as Si and GaAs. Here, we employ a high-throughput virtual screening procedure to discover new organic singlet exciton fission candidate materials with high-energy (>1.4 eV) triplet excitons. After exploring a search space of 4482 molecules and screening them using time-dependent density functional theory, we identify 88 novel singlet exciton fission candidate materials based on anthracene derivatives. Subsequent purification and characterization of several of these candidates yield two new singlet exciton fission materials: 9,10-dicyanoanthracene (DCA) and 9,10-dichlorooctafluoroanthracene (DCOFA), with triplet energies of 1.54 eV and 1.51 eV, respectively. These materials are readily available and low-cost, making them interesting candidates for exothermic singlet exciton fission sensitization of solar cells. However, formation of triplet excitons in DCA and DCOFA is found to occur via hot singlet exciton fission with excitation energies above ∼3.64 eV, and prominent excimer formation in the solid state will need to be overcome in order to make DCA and DCOFA viable candidates for use in a practical device.

13.
Nat Biotechnol ; 37(9): 1038-1040, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31477924

RESUMO

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.


Assuntos
Aprendizado Profundo , Receptor com Domínio Discoidina 1/antagonistas & inibidores , Receptor com Domínio Discoidina 1/metabolismo , Avaliação Pré-Clínica de Medicamentos/métodos , Animais , Receptor com Domínio Discoidina 1/genética , Cães , Inibidores Enzimáticos , Humanos , Camundongos , Microssomos Hepáticos/metabolismo , Modelos Moleculares , Estrutura Molecular , Conformação Proteica , Ratos
14.
ACS Cent Sci ; 5(7): 1199-1210, 2019 Jul 24.
Artigo em Inglês | MEDLINE | ID: mdl-31404220

RESUMO

A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimate standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here, we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semiempirical quantum chemistry calculations calibrated with Gaussian process (GP) regression against available experimental data and results in higher predictive power than the GCM at low computational cost. Direct calibration of GCM and fingerprint-based predictions (without quantum chemistry) with GP regression also results in significant improvements in prediction accuracy, demonstrating the versatility of the approach. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315 000 redox reactions involving approximately 70 000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated.

15.
ACS Nano ; 13(8): 9421-9430, 2019 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-31386342

RESUMO

Two-dimensional (2D) metal sulfides show great promise for their potential applications as electrode materials of sodium ion-batteries because of the weak interlayer van der Waals interactions, which allow the reversible accommodation and extraction of sodium ions. The sodiation of metal sulfides can undergo a distinct process compared to that of lithiation, which is determined by their metal and structural types. However, the structural and morphological evolution during their electrochemical sodiation is still unclear. Here, we studied the sodiation reaction dynamics of TiS2 by employing in situ transmission electron microscopy and first-principles calculations. During the sodium-ion intercalation process, we observed multiple intermediate phases (phase II, phase Ib, and phase Ia), different from its lithiation counterpart, with varied sodium occupation sites and interlayer stacking sequences. Further insertion of Na ions prompted a multistep extrusion reaction, which led to the phase separation of Ti metal from the Na2S matrix, with its 2D morphology expanded to a 3D morphology. In contrast to regular conversion electrodes, TiS2 still maintained a compact structure after a full sodiation. First-principles calculations reveal that the as-identified phases are thermodynamically preferred at corresponding intercalation/extrusion stages compared to other possible phases. The present work provides the fundamental mechanistic understanding of the sodiation process of 2D transition metal sulfides.

16.
Chem Rev ; 119(19): 10856-10915, 2019 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-31469277

RESUMO

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

17.
J Chem Theory Comput ; 15(7): 4099-4112, 2019 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-31244127

RESUMO

Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the heuristically-aided quantum chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics-simple rules guiding the PES exploration-and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasibility criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity.

18.
J Am Chem Soc ; 141(20): 8014-8019, 2019 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-30945536

RESUMO

Redox flow batteries based on quinone-bearing aqueous electrolytes have emerged as promising systems for energy storage from intermittent renewable sources. The lifetime of these batteries is limited by quinone stability. Here, we confirm that 2,6-dihydroxyanthrahydroquinone tends to form an anthrone intermediate that is vulnerable to subsequent irreversible dimerization. We demonstrate quantitatively that this decomposition pathway is responsible for the loss of battery capacity. Computational studies indicate that the driving force for anthrone formation is greater for anthraquinones with lower reduction potentials. We show that the decomposition can be substantially mitigated. We demonstrate that conditions minimizing anthrone formation and avoiding anthrone dimerization slow the capacity loss rate by over an order of magnitude. We anticipate that this mitigation strategy readily extends to other anthraquinone-based flow batteries and is thus an important step toward realizing renewable electricity storage through long-lived organic flow batteries.

19.
ACS Appl Mater Interfaces ; 11(28): 24825-24836, 2019 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-30908004

RESUMO

The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Such systems learn through every single experiment in an analogy to a human scientific expert. While the general idea of an autonomous system for molecular synthesis and characterization has been around for a while, its implementations for the materials sciences are sparse. Here we provide an overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in.


Assuntos
Simulação por Computador , Desenho de Fármacos , Aprendizado de Máquina , Modelos Moleculares
20.
Chem Sci ; 10(8): 2298-2307, 2019 Feb 28.
Artigo em Inglês | MEDLINE | ID: mdl-30881655

RESUMO

Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without a priori knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of ab initio molecular dynamics simulations - the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans.

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