Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous, distributed scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows.

When it comes to manufacturing new lightweight, yet strong components for new passenger jets, scientists are treating the process like trying to brew the most delicious cup of coffee. By using artificial intelligence (AI) and machine learning, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are intelligently and automatically selecting the perfect settings for a different kind of hot brew -- the process of friction stir welding, a common ingredient needed to manufacture airplane components. In a new collaboration with GE Research, Edison Welding Institute and GKN Aerospace, Argonne computer scientists are putting the power of the laboratory's automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost. This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist Prasanna Balaprakash and his colleagues at Argonne.

As part of the transportation authorities' efforts to address this problem, researchers from across the US Department of Energy's (DOE) Argonne National Laboratory in collaboration with the Lawrence Berkeley National Laboratory (LBNL) have developed a new artificial intelligence model to help alleviate congestion on the city's streets. That data was then used to train a model to forecast traffics, congestion spots, and average speed of cars on the routes. The new model can look at the past hour, and then predict the next hour of traffic with great accuracy within milliseconds. "The AI and supercomputing capabilities that have been used in this work allow us to tackle really large problems. The scale of this project is large, and this amount of data requires an equally large computing resource to tackle it," said Prasanna Balaprakash, a computer scientist in Argonne National Laboratory.

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash. "In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time."

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "It's a bit like having a self-driving car in traffic; the algorithm can detect when it needs to make adjustments in the'dials' it uses to do the computation." "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash.

Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data. While increasing swaths of collected data and growing scales of computing power are helping to improve our understanding of cancer, further development of data-driven methods for the disease's diagnosis, detection and prognosis is necessary. There is a particular need to develop deep-learning methods -- that is, machine learning algorithms capable of extracting science from unstructured data. Researchers from the U.S. Department of Energy's (DOE) Argonne National Laboratory have made strides toward accelerating such efforts by presenting a method for the automated generation of neural networks. As detailed in a paper for presentation at the SC19 conference, the researchers, utilizing resources from the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility, have established a neural architecture search (NAS) that, for a class of representative cancer data, automates the development of deep-learning-based predictive models.

Utilizing ALCF supercomputing resources, Argonne researchers are developing the deep learning framework MaLTESE with autonomous -- or self-driving -- and cloud-connected vehicles in mind. This work could help meet demand to deliver better engine performance, fuel economy and reduced emissions. Researchers used nearly the full capacity of the ALCF's Theta system to simulate a typical 25-minute drive cycle of 250,000 vehicles. Researchers at Argonne are developing the deep learning framework MaLTESE (Machine Learning Tool for Engine Simulations and Experiments) to meet ever-increasing demands to deliver better engine performance, fuel economy and reduced emissions. Automotive manufacturers are facing an ever-increasing demand to deliver better engine performance, fuel economy and reduced emissions.

And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?

And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?