Project description:Hit finding in early drug discovery is often based on high throughput screening (HTS) of existing and historical compound libraries, which can limit chemical diversity, is time-consuming, very costly, and environmentally not sustainable. On-the-fly compound synthesis and in situ screening in a highly miniaturized and automated format has the potential to greatly reduce the medicinal chemistry environmental footprint. Here, we used acoustic dispensing technology to synthesize a library in a 1536 well format based on the Groebcke-Blackburn-Bienaymé reaction (GBB-3CR) on a nanomole scale. The unpurified library was screened by differential scanning fluorimetry (DSF) and cross-validated using microscale thermophoresis (MST) against the oncogenic protein-protein interaction menin-MLL. Several GBB reaction products were found as μM menin binder, and the structural basis of the interactions with menin was elucidated by co-crystal structure analysis. Miniaturization and automation of the organic synthesis and screening process can lead to an acceleration in the early drug discovery process, which is an alternative to classical HTS and a step towards the paradigm of continuous manufacturing.
Project description:Miniaturization and acceleration of synthetic chemistry is an emerging area in pharmaceutical, agrochemical, and materials research and development. Herein, we describe the synthesis of iminopyrrolidine-2-carboxylic acid derivatives using chiral glutamine, oxo components, and isocyanide building blocks in an unprecedented Ugi-3-component reaction. We used I-DOT, a positive-pressure-based low-volume and non-contact dispensing technology to prepare more than 1000 different derivatives in a fully automated fashion. In general, the reaction is stereoselective, proceeds in good yields, and tolerates a wide variety of functional groups. We exemplify a pipeline of fast and efficient nanomole-scale scouting to millimole-scale synthesis for the discovery of a useful novel reaction with great scope.
Project description:A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self-optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On-Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future.
Project description:Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.
Project description:Perovskite solar cells are among the most promising photovoltaic technologies in academia and have the potential to become commercially available in the near future. However, there are still a few unresolved issues regarding device lifetime and fabrication cost of perovskite solar cells in order to be competitive with existing technologies. Herein, we report small organic molecules with introduced vinyl groups as hole transporting materials, which are capable of undergoing thermal polymerization, forming solvent-resistant 3D networks. Novel compounds have been synthesized from relatively inexpensive starting materials and their purification is less time-consuming when compared to polymers; therefore this type of hole transporter can be a promising alternative to lower the manufacturing cost of perovskite solar cells.
Project description:Additive manufacturing has become one of the forefront technologies in fabrication, enabling products impossible to manufacture before. Although many materials exist for additive manufacturing, most suffer from performance trade-offs. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerating the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multiobjective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better performing materials. The algorithm is coupled with a semiautonomous fabrication platform to substantially reduce the number of performed experiments and overall time to solution. Without prior knowledge of the primary formulations, the proposed methodology autonomously uncovers 12 optimal formulations and enlarges the discovered performance space 288 times after only 30 experimental iterations. This methodology could be easily generalized to other material design systems and enable automated discovery.
Project description:Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments - the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents - to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms.
Project description:Novel crosslinking bio polyurethane based polymeric solid-solid phase change materials (SSPCM) were synthesized using castor oil (CO) based hyperbranched polyols as crosslinkers. CO-based hyperbranched polyols were synthesized by grafting 1-mercaptoethanol or α-thioglycerol via a thiol-ene click reaction method (coded as COM and COT, respectively). Subsequently, the three SSPCMs were synthesized by a two-step prepolymer method. Polyethylene glycol was used as the phase change material in the SSPCMs, while the CO-based hyperbranched polyols and two types of diisocyanate (hexamethylene diisocyanate (HDI) and 4,4'-diphenylmethane diisocyanate) served as the molecular frameworks. Fourier transform infrared spectroscopy indicated the successful synthesis of the SSPCMs. The solid-solid transition of the prepared SSPCMs was confirmed by X-ray diffraction analysis and polarized optical microscopy. The thermal transition properties of the SSPCMs were analyzed by differential scanning microscopy. The isocyanate and crosslinker types had a significant influence on the phase transition properties. The SSPCM samples prepared using HDI and COT exhibited the highest phase transition enthalpy of 126.5 J/g. The thermal cycling test and thermogravimetric analysis revealed that SSPCMs exhibit outstanding thermal durability. Thus, the novel SSPCMs based on hyperbranched polyols have great potential for application as thermal energy storage materials.
Project description:High Entropy Alloys (HEAs) are new classes of structural metallic materials that show remarkable property combinations. Yet, often times interesting compositions are still found by trial and error. Here we show an "Effective Atomic Radii for Strength" (EARS) methodology, together with different semi-empirical and first-principle models, can be used to predict the extent of solid solution strengthening to discover and design new HEAs with unprecedented properties. We have designed a Cr45Ni27.5Co27.5 alloy with a yield strength over 50% greater with equivalent ductility than the strongest HEA (Cr33.3Ni33.3Co33.3) from the CrMnFeNiCo family reported to date. We show that values determined by the EARS methodology are more physically representative of multicomponent concentrated solid solutions. Our methodology permits high throughput, property-driven discovery and design of HEAs, enabling the development of future high-performance advanced materials for extreme environments.
Project description:To close the gap between the rates of computational screening and experimental realization of novel materials1,2, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.