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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

ChemOS: Orchestrating autonomous experimentation.

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

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.

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 sp 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.

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.

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.

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.

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

A general synthetic route to inherently luminescent and optically active 6-fold substituted C-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.

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.

Pentaindenocorannulene: Properties, Assemblies, and C Complex.

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