Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes.

Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.

How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science.

Accelerating R&D is essential to address some of the challenges humanity is currently facing, such as achieving the global sustainability goals. Today's Edisonian approach of trial-and-error still prevalent in R&D labs takes up to two decades of fundamental and applied research for new materials to reach the market. Turning around this situation calls for strategies to upgrade R&D and expedite innovation. By conducting smart experiment planning that is data-driven and guided by AI/ML, researchers can more efficiently search through the complex - often constrained - space of possible experiments and find or hit the global optima much faster than with the current approaches. Moreover, with digitized data management, researchers will be able to maximize the utility of their data in the short and long terms with the aid of statistics, ML and visualization tools. In what follows, we describe a framework and lay out the key technologies to accelerate R&D and optimize experiment planning.

Unexpected three-state hydrochromism of a donor-acceptor self-complex with fluctuations in relative humidity.

Water in our environment is ever present, particularly in our atmosphere, from which it may be adsorbed by materials hygroscopically. At the molecular level, the binding of water molecules to various materials is driven by weak interactions but can have profound effects on physical properties, including the donor-acceptor interactions in charge transfer (CT) salts. Herein we present the unexpected three-state hydrochromatic switching of a bipyridinium-based donor-acceptor self-complex with changes in relative humidity (RH) and subsequent stable hydrate formation. RH is typically an overlooked variable that can vary greatly. These findings suggest that care should be taken to consider fluctuations in RH when characterizing the solid state optical band gap and CT absorption bands for organic donor-acceptor CT salt complexes.

Data-science driven autonomous process optimization.

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

Designing and understanding light-harvesting devices with machine learning.

Understanding the fundamental processes of light-harvesting is crucial to the development of clean energy materials and devices. Biological organisms have evolved complex metabolic mechanisms to efficiently convert sunlight into chemical energy. Unraveling the secrets of this conversion has inspired the design of clean energy technologies, including solar cells and photocatalytic water splitting. Describing the emergence of macroscopic properties from microscopic processes poses the challenge to bridge length and time scales of several orders of magnitude. Machine learning experiences increased popularity as a tool to bridge the gap between multi-level theoretical models and Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices.

From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning.

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.

ChemOS: An orchestration software to democratize autonomous discovery.

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.

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.

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.

Azaindenocorannulenes: Synthesis, Properties, and Chirality.

Palladium-catalyzed intramolecular arylation provides bowl-shaped azaindenocorannulenes 7-9. Crystals of 8 show bowl-in-bowl columnar stacking. A substituent model rationalizes the first reduction potential of 18 related molecular bowls. The absolute configurations of bowls 7 and 9 are correlated with VCD and ECD spectra. The bowl inversion barrier of 9 (>190 kJ/mol) shows it to be more inert configurationally than chiral biaryl, phosphenes, or [ n]helicenes.

Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories.

Finding the ideal conditions satisfying multiple pre-defined targets simultaneously is a challenging decision-making process, which impacts science, engineering, and economics. Additional complexity arises for tasks involving experimentation or expensive computations, as the number of evaluated conditions must be kept low. We propose Chimera as a general purpose achievement scalarizing function for multi-target optimization where evaluations are the limiting factor. Chimera combines concepts of a priori scalarizing with lexicographic approaches and is applicable to any set of n unknown objectives. Importantly, it does not require detailed prior knowledge about individual objectives. The performance of Chimera is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the applicability and performance of Chimera with two practical examples: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a four-pigment excitonic system for an efficient energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find ideal conditions. Additionally, the presented applications highlight the interpretability of Chimera to corroborate design choices for tailoring system parameters.

Phoenics: A Bayesian Optimizer for Chemistry.

We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with concepts from Bayesian kernel density estimation. As such, Phoenics allows to tackle typical optimization problems in chemistry for which objective evaluations are limited, due to either budgeted resources or time-consuming evaluations of the conditions, including experimentation or enduring computations. Phoenics proposes new conditions based on all previous observations, avoiding, thus, redundant evaluations to locate the optimal conditions. It enables an efficient parallel search based on intuitive sampling strategies implicitly biasing toward exploration or exploitation of the search space. Our benchmarks indicate that Phoenics is less sensitive to the response surface than already established optimization algorithms. We showcase the applicability of Phoenics on the Oregonator, a complex case-study describing a nonlinear chemical reaction network. Despite the large search space, Phoenics quickly identifies the conditions which yield the desired target dynamic behavior. Overall, we recommend Phoenics for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.

Melatonin-directed micellization: a case for tryptophan metabolites and their classical bioisosteres as templates for the self-assembly of bipyridinium-based supramolecular amphiphiles in water.

The bulk solution properties of amphiphilic formulations are derivative of their self-assembly into higher ordered supramolecular assemblies known as micelles and of their ordering at the air-water interface. Exerting control over the surface-active properties of amphiphiles and their propensity to aggregate in pure water is most often fine-tuned by covalent modification of their molecular structure. Nevertheless structural constraints which limit the performance of amphiphiles do emerge when trying to develop more sophisticated systems which undergo for example, shape-defined controlled assembly and/or respond to external stimuli. In this regard, the template-modulated assembly of the so-called "supramolecular amphiphiles" continues to make progress ordering molecules that otherwise have very little to no driving force to aggregate in a prescribed manner in aqueous solutions. Herein we describe the template-modulated micellization and ordering at the air-water interface of bipyridinium-based supramolecular amphiphiles triggered by host-guest interactions with high specificity for the neurotransmitter melatonin over its biosynthetic synthon l-tryptophan and the thermodynamic parameters governing the template-modulated micellization process. When bound to the bipyridinium units of micellized surfactant molecules, melatonin effectively serves as "molecular glue" capable of lowering the CMC by 52% as compared to untemplated solutions. Analysis of this system suggests that a hallmark of donor-acceptor template-modulated micellization in water is a strong positively correlated temperature dependence of the CMC and the absence of a U-shaped CMC-temperature curve. Our findings make a case for the incorporation of l-tryptophan-based metabolites and their classical synthetic pharmaceutical bioisosteres as potential targets/components of donor-acceptor CT-based supramolecular amphiphile systems/materials operating in water.

1,2,3- versus 1,2-Indeno Ring Fusions Influence Structure Property and Chirality of Corannulene Bowls.

Annulated corannulenes 3-5 form via distinct synthetic pathways: (i) Pd-catalyzed sp3 CH insertion, (ii) Pd-catalyzed aryl coupling, and (iii) silyl cation-promoted C-F activation/CH insertion. Crystal structure, redox, and photophysical studies elucidate the differing influence of 1,2,3- versus 1,2-indeno ring fusions. Mono and dianions of 3-5 are characterized. Resolution of 4 gives enantiopure forms, allowing assessment of the bowl-inversion barrier.

Correction: General optimization procedure towards the design of a new family of minimal parameter spin-component-scaled double-hybrid density functional theory.

Correction for 'General optimization procedure towards the design of a new family of minimal parameter spin-component-scaled double-hybrid density functional theory' by Loïc M. Roch and Kim K. Baldridge, Phys. Chem. Chem. Phys., 2017, 19, 26191-26200.

ChemOS: Orchestrating autonomous experimentation.

ChemOS aims to catalyze the expansion of autonomous laboratories and to disrupt the conventional approach to experimentation.

General optimization procedure towards the design of a new family of minimal parameter spin-component-scaled double-hybrid density functional theory.

A general optimization procedure towards the development and implementation of a new family of minimal parameter spin-component-scaled double-hybrid (mSD) density functional theory (DFT) is presented. The nature of the proposed exchange-correlation functional establishes a methodology with minimal empiricism. This new family of double-hybrid (DH) density functionals is demonstrated using the PBEPBE functional, illustrating the optimization procedure to the mSD-PBEPBE method, and the performance characteristics shown for a set of non-covalent complexes covering a broad regime of weak interactions. With only two parameters, mSD-PBEPBE and its cost-effective counterpart, RI-mSD-PBEPBE, show a mean absolute error of ca. 0.4 kcal mol-1 averaged over 66 weak interacting systems. Following a successive 2D-grid refinement for a CBS extrapolation of the coefficients, the optimization procedure can be recommended for the design and implementation of a variety of additional DH methods using any of the plethora of currently available functionals.

Dispersion-Corrected Spin-Component-Scaled Double-Hybrid Density Functional Theory: Implementation and Performance for Non-covalent Interactions.

The implementation of 300 combinations of generalized gradient approximation/local density approximation exchange-correlation dispersion-corrected spin-component-scaled double-hybrid (DSD) density functional theory (DFT) methods has been carried out and the performance assessed against several DFT and post-Hartree-Fock methods, enabling further advancements toward the long-standing challenge of accurate prediction of interaction energies and associated properties. The resulting framework is flexible and has been further extended to include the resolution of identity (RI) approximation for solving the critical four-center two-electron repulsion integrals in the basis of the Kohn-Sham orbitals for cost effectiveness. To evaluate the performance of this set of new cost-effective methods, denoted as RI-DSD-DFTs, seven validation data sets were designed to cover a broad range of non-covalent interactions with characteristic stabilizing contributions. Inclusion of the perturbative treatment of correlation effects is shown to significantly improve the description of weak interactions. The set of DSD-DFTs provide interaction energies with root-mean-square deviations and mean absolute errors within 0.5 kcal/mol. The cost-effective RI-DSD-DFT counterparts deviate by less than 0.18 kcal/mol on average with only 2% of the computational cost.

[3+3] Cyclocondensation of Disubstituted Biphenyl Dialdehydes: Access to Inherently Luminescent and Optically Active Hexa-substituted C3-Symmetric and Asymmetric Trianglimine Macrocycles.

A general synthetic route to inherently luminescent and optically active 6-fold substituted C3-symmetric and asymmetric biphenyl-based trianglimines has been developed. The synthesis of these hexa-substituted triangular macrocycles takes advantage of a convenient method for the synthesis of symmetrically and asymmetrically difunctionalized biphenyl dialdehydes through a convergent two-step aromatic nucleophilic substitution-one-pot Suzuki-coupling reaction protocol. A modular [3+3] diamine-dialdehyde cyclocondensation reaction between both the symmetrically and asymmetrically difunctionalized-4,4'-biphenyldialdehydes with enantiomerically pure (1R,2R)-1,2-diaminocyclohexane was employed to construct the hexa-substituted triangular macrocycles. B97-D/6-311G(2d,p) density functional theory determined structures and X-ray crystallographic analysis reveal that the six substituents appended to the biphenyl legs of the trianglimine macrocycles adopt an alternating conformation not unlike the 1,3,5-alternate conformation observed for calix[6]arenes. Reduction of the imine bonds using NaBH4 afforded the corresponding 6-fold substituted trianglamine without the need to alkylate the amine nitrogen atoms which could hinder their later use as metal coordination sites and without having to introduce asymmetric carbons.

Toward Accurate Adsorption Energetics on Clay Surfaces.

Clay minerals are ubiquitous in nature, and the manner in which they interact with their surroundings has important industrial and environmental implications. Consequently, a molecular-level understanding of the adsorption of molecules on clay surfaces is crucial. In this regard computer simulations play an important role, yet the accuracy of widely used empirical force fields (FF) and density functional theory (DFT) exchange-correlation functionals is often unclear in adsorption systems dominated by weak interactions. Herein we present results from quantum Monte Carlo (QMC) for water and methanol adsorption on the prototypical clay kaolinite. To the best of our knowledge, this is the first time QMC has been used to investigate adsorption at a complex, natural surface such as a clay. As well as being valuable in their own right, the QMC benchmarks obtained provide reference data against which the performance of cheaper DFT methods can be tested. Indeed using various DFT exchange-correlation functionals yields a very broad range of adsorption energies, and it is unclear a priori which evaluation is better. QMC reveals that in the systems considered here it is essential to account for van der Waals (vdW) dispersion forces since this alters both the absolute and relative adsorption energies of water and methanol. We show, via FF simulations, that incorrect relative energies can lead to significant changes in the interfacial densities of water and methanol solutions at the kaolinite interface. Despite the clear improvements offered by the vdW-corrected and the vdW-inclusive functionals, absolute adsorption energies are often overestimated, suggesting that the treatment of vdW forces in DFT is not yet a solved problem.

Pentaindenocorannulene: Properties, Assemblies, and C60 Complex.

Pentaindenocorannulene (C50 H20 , 1), a deep bowl polynuclear aromatic hydrocarbon, accepts 4 electrons, crystallizes in columnar bowl-in-bowl assemblies and forms a nested C60 @12 complex. Spectra, structures and computations are presented.