Robotic systems for chemical synthesis are growing in popularity but can be difficult to run and maintain because of the lack of a standard operating system or capacity for direct access to the literature through natural language processing. Here we show an extendable chemical execution architecture that can be populated by automatically reading the literature, leading to a universal autonomous workflow. The robotic synthesis code can be corrected in natural language without any programming knowledge and, because of the standard, is hardware independent. This chemical code can then be combined with a graph describing the hardware modules and compiled into platform-specific, low-level robotic instructions for execution. We showcase automated syntheses of 12 compounds from the literature, including the analgesic lidocaine, the Dess-Martin periodinane oxidation reagent, and the fluorinating agent AlkylFluor.

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.

Managing energy consumption has become vitally important to battery-operated portable and embedded systems. Dynamic voltage scaling (DVS) reduces the processor's dynamic power consumption quadratically at the expense of linearly decreasing the performance. When reducing energy with DVS for real-time systems, one must consider the performance penalty to ensure that deadlines can be met. In this paper, we introduce a novel collaborative approach between the compiler and the operating system (OS) to reduce energy consumption. We use the compiler to annotate an application's source code with path-dependent information called power-management hints (PMHs). This fine-grained information captures the temporal behavior of the application, which varies by executing different paths. During program execution, the OS periodically changes the processor's frequency and voltage based on the temporal information provided by the PMHs. These speed adaptation points are called power-management points (PMPs). We evaluate our scheme using three embedded applications: a video decoder, automatic target recognition, and a sub-band tuner. Our scheme shows an energy reduction of up to 57% over no power-management and up to 32% over a static power-management scheme. We compare our scheme to other schemes that solely utilize PMPs for power-management and show experimentally that our scheme achieves more energy savings. We also analyze the advantages and disadvantages of our approach relative to another compiler-directed scheme.

The tetrahedral cobalt(II) compound (PhP)[Co(SPh)] was the first mononuclear transition-metal complex shown to exhibit slow relaxation of the magnetization in zero external magnetic field. Because the relative populations of the d orbitals play a vital role in dictating the magnitude of the magnetic anisotropy, the magnetic behavior of this complex is directly related to its electronic structure, yet the exact role of the soft, thiophenolate ligands in influencing the d-electron configuration has previously only been investigated via theoretical methods. To provide detailed experimental insight into the effect of this ligand field, the electron density distribution in this compound was determined from low-temperature, single-crystal X-ray diffraction data and subsequent multipole modeling. Topological analysis of the electron density indicates significant covalent contributions to the cobalt-sulfur bonds. The derived d-orbital populations further reveal a fully occupied d orbital, minor d orbital population, and nearly equal population of the d , d, and d orbitals. Notably, we find that an electrostatic interaction between Co(II) and one hydrogen atom from a thiophenolate group in the xz plane increases the energy of the d orbital, leading to the nearly equal population with d and strong magnetic anisotropy.

Reducing device energy has become one of the most important challenges to embedded systems designers. Processors with dynamic voltage scaling permit trading performance for reduced energy consumption as a program executes. In this paper, we rst present a novel hybrid scheme that uses dynamic voltage scaling to adjust the performance o f embedded applications to reduce energy consumption while also meeting time constraints. Our ne-grained a p p r oach uses the compiler to insert power management hints in the application c ode. These hints convey path-speci c runtime information about the program's progress to power management p o i n ts invoked b y t h e o p erating system that adjust processor performance. Second, we present an algorithm for inserting power management hints along di erent program paths. Finally, we experimentally evaluate our approach and show that signi cant energy reduction can be achieved. On two embedded applications, MPEG movie decoding and automatic target recognition, our scheme reduces energy by up to 79% over no power management and by up to 50% over static power management. We also experimentally demonstrate that our scheme achieves more energy savings compared to two purely compiler-directed schemes.