Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method.

Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, and only a handful of examples are currently known. High-throughput virtual screening could assist in finding novel classes of these molecules, but current efforts are hampered by the high computational cost of the required quantum chemical methods. We present a method based on the semiempirical Pariser-Parr-Pople theory augmented by perturbation theory and show that it reproduces inverted gaps at a fraction of the cost of currently employed excited-state calculations. Our study paves the way for ultrahigh-throughput virtual screening and inverse design to accelerate the discovery and development of this new generation of OLED materials.

A Materials Acceleration Platform for Organic Laser Discovery.

Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development.

Guided discovery of chemical reaction pathways with imposed activation.

Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.

Toward Quantum Computing with Molecular Electronics.

In this study, we explore the use of molecules and molecular electronics for quantum computing. We construct one-qubit gates using one-electron scattering in molecules and two-qubit controlled-phase gates using electron-electron scattering along metallic leads. Furthermore, we propose a class of circuit implementations, and show initial applications of the framework by illustrating one-qubit gates using the molecular electronic structure of molecular hydrogen as a baseline model.

Routescore: Punching the Ticket to More Efficient Materials Development.

Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving laboratories. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization) and to simulate a self-driving laboratory that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ∼3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization and especially in downselecting promising candidates from a large chemical space via an a priori estimation of the synthetic costs.

Data-Driven Strategies for Accelerated Materials Design.

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency.In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

Pulsed two-photon coherent control of channelrhodopsin-2 photocurrent in live brain cells.

Channelrhodopsin-2 (ChR2) is an ion channel activated by the absorption of light. A recent experiment demonstrated that the current emanating from neurons in live brain cells expressing ChR2 can be controlled using two-photon phase control. Here, we propose an experimentally testable coherent control mechanism for this phenomenon. Significantly, we describe how femtosecond, quantum coherent processes arising from weak-field ultrafast excitation are responsible for the reported control of the millisecond classical dynamics of the neuronal current.

Boramidine: A Versatile Structural Motif for the Design of Fluorescent Heterocycles.

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.

An efficient spectral method for numerical time-dependent perturbation theory.

We develop the Fourier-Laplace Inversion of the Perturbation Theory (FLIPT), a novel numerically exact "black box" method to compute perturbative expansions of the density matrix with rigorous convergence conditions. Specifically, the FLIPT method is extremely well-suited to simulate multiphoton pulsed laser experiments with complex pulse shapes. The n-dimensional frequency integrals of the nth order perturbative expansion are evaluated numerically using tensor products. The N-point discretized integrals are computed in O(N2) operations, a significant improvement over the O(Nn) scaling of standard quadrature methods.

Interfering resonance as an underlying mechanism in the adaptive feedback control of radiationless transitions: Retinal isomerization.

Control of molecular processes via adaptive feedback often yields highly structured laser pulses that have eluded physical explanation. By contrast, coherent control approaches propose physically transparent mechanisms but are not readily visible in experimental results. Here, an analysis of a condensed phase adaptive feedback control experiment on retinal isomerization shows that it manifests a quantum interference based coherent control mechanism: control via interfering resonances. The result promises deep insight into the physical basis for the adaptive feedback control of a broad class of bound state processes.

Multiple interfaces in diffusional phase transitions in binary mesogen-nonmesogen mixtures undergoing metastable phase separations.

Theory and simulations of simultaneous chemical demixing and phase ordering are performed for a mixed order parameter system with an isotropic-isotropic (I-I) phase separation that is metastable with respect to an isotropic-nematic (I-N) phase-ordering transition. Under certain conditions, the disordered phase transforms into an ordered phase via the motion of a double front containing a metastable phase produced by I-I demixing, a thermodynamically driven mechanism not previously reported. Different kinetic regimes are found depending on the location of the initial conditions in the thermodynamic phase diagram and the ratio between diffusional and nematic phase-ordering mobilities. For a diffusional process, depending if the temperature is above or below the critical codissolution point, an inflection point or a phase separation takes place in the depletion layer. This phase separation leads to the formation of a second interface where the separation of the two metastable isotropic phases grows monotonically with time. The observed deviations from the typical Fickian concentration profiles are associated with strong positive deviations of the mixture from ideality due to couplings between concentration and nematic ordering. Although systems of interest include liquid-crystalline nanocomposites, this mechanism may apply to any mixture that can undergo an order-disorder transition and demix.