We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quartz that gradually dissolves until it reaches a quasi-steady state. At the end of the simulation, we measure the quartz remaining in each fracture within the domain. We observe that a small backbone of fracture exists, where the quartz is fully dissolved which leads to increased flow and transport. However, depending on the DFN topology and the rate of dissolution, we observe a large variability of these changes, which indicates an interplay between the fracture network structure and the impact of geochemical dissolution. In this work, we developed a machine learning framework to extract the important features that support mineralization in the form of dissolution. In addition, we use structural and topological features of the fracture network to predict the remaining quartz volume in quasi-steady state conditions. As a first step to characterizing carbon mineralization, we study dissolution with this framework. We studied a variety of reaction and fracture parameters and their impact on the dissolution of quartz in fracture networks. We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system. For the first time, we use a combination of a finite-volume reservoir model and graph-based approach to study reactive transport in a complex fracture network to determine the key features that control dissolution.

The problem of plastic waste hides in plain sight, a ubiquitous part of our lives we rarely question. But a closer examination of the situation is shocking. To date, humans have created around 11 billion metric tons of plastic. Only 9% of the plastic ever produced has been recycled. To make matters worse, plastic production is growing dramatically; in fact, half of all plastics in existence have been produced in just the last two decades.

This came during a discussion with Venkat Viswanathan about the potential for electric aviation--an exciting prospect as well as a huge challenge, given the steep demands on batteries during flight. In our discussion, Viswanathan said one of the reasons he saw hope for electric aviation is the potential of AI to speed up battery research. In fact, he cofounded a startup called Aionics in 2020 to bring AI into battery development. On stage at ClimateTech, Viswanathan announced a new research partnership that he says could make AI a key force in developing future EV batteries. The deal is between Aionics and Cellforce, a German battery maker that's a subsidiary of Porsche.

The foraging behavior of animals is a paradigm of target search in nature. Understanding which foraging strategies are optimal and how animals learn them are central challenges in modeling animal foraging. While the question of optimality has wide-ranging implications across fields such as economy, physics, and ecology, the question of learnability is a topic of ongoing debate in evolutionary biology. Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework. To this end, we model foragers as learning agents. We first prove theoretically that maximizing rewards in our reinforcement learning model is equivalent to optimizing foraging efficiency. We then show with numerical experiments that, in the paradigmatic model of non-destructive search, our agents learn foraging strategies which outperform the efficiency of some of the best known strategies such as L\'evy walks. These findings highlight the potential of reinforcement learning as a versatile framework not only for optimizing search strategies but also to model the learning process, thus shedding light on the role of learning in natural optimization processes.

Historically, researchers in materials discovery have devised and tested options through some mix of hunches, informed speculation, and trial by error. But it's a difficult and time-consuming process simply given the vast array of possible substances and combinations, which can send researchers down numerous false paths. In the case of electrolyte ingredients, "you can mix and match them in billions of ways," says Venkat Viswanathan, an associate professor at Carnegie Mellon, a co-author of the Nature Communications paper, and a cofounder and chief scientist at Aionics. He collaborated with Jay Whitacre, director of the university's Wilton E. Scott Institute for Energy Innovation and the co-principal investigator on the project, along with other Carnegie researchers to explore how robotics and machine learning could help. The promise of a system like Clio and Dragonfly is that it can rapidly work through a wider array of possibilities than human researchers can, and apply what it learns in a systematic way.

To tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon dioxide from the atmosphere. But many of these challenges will be insurmountable without behind-the-scenes breakthroughs in materials science. Today's materials lack key properties needed for scalable climate-friendly technologies. Batteries, for example, require improved materials that can yield higher energy densities and longer discharge times. Without such improvements, commercial batteries won't be able to power mass-market electric vehicles and support a renewable-powered grid.

Autonomous vehicles come at a cost: increased energy use. Some analysts suggest that these increased power needs are significant enough to drastically reduce vehicle range thus eliminating the possibility of electric autonomous vehicles. Instead, these analysts claim autonomous vehicles must be gas-electric hybrids. In a paper published in Nature Energy, Carnegie Mellon University researchers Aniruddh Mohan, Shashank Sripad, Parth Vaishnav and Venkat Viswanathan determined that electric power can supply enough energy for an autonomous vehicle without a significant decrease in range. Two revolutions are happening side-by-side in the automotive industry: the transition to electric power and the rise of autonomous vehicles. Self-driving cars may use more energy than people-driven cars to power sensors and computers for safe navigation.

As India battles to contain the spread of coronavirus, artificial intelligence and technology start-ups in the country are leveraging their tools and solutions to help those in the frontline combat the crisis. Invento Robotics, a Bengaluru-based start-up, has been re-purposing its robots -- to screening and diagnostics robots -- to help doctors and healthcare workers from getting exposed to infected patients. The screening robot helps with data collection (name of the patient, symptoms exhibited) and validation (temperature checks) in a contactless manner. Those having a high body temperature, or exhibiting symptoms of the virus, or those whose family members have tested positive will be directed towards the diagnostics robot, which enables a video conversation with a doctor sitting in a remote location, and procures a prescription thereafter, for the patient. "We started the pilot with two robots this week in two hospitals and we have about 10 hospitals that are waiting for the robots," said CEO Balaji Viswanathan.

China's grand designs to dominate the future of clean energy paid off spectacularly this week. In a public offering on June 11 in Shenzhen, battery giant Contemporary Amperex Technology Ltd. (CATL) raised nearly $1 billion to fund ambitious expansion plans, and its stock has been shooting up every day since. Thanks largely to the company's new plants, China will be making 70 percent of the world's electric-vehicle batteries by 2021, according to Bloomberg New Energy Finance (BNEF). The rapid rise of CATL is arguably the clearest, though certainly not the only, payoff from China's calculated efforts to bolster its domestic battery and electric-vehicle industries--two of the most promising sectors in clean energy. These efforts have largely followed the same playbook China used to get ahead in solar panels, including highly automated manufacturing; aggressive efforts to lock in global supply chains; foreign acquisitions and licensing; and hefty doses of government support and protectionism.

In a matter of months, General Data Protection Regulation (GDPR) will become a law throughout Europe, deeming a complete overhaul in the way artificial intelligence techniques are used in business settings. By May 25, the GDPR will become fully enforceable throughout the European Union, states the EU GDPR timeline. The coming deadline, which will be enforced in the next 100 days, has sparked a debate among the AI research community and tech giants who are now scrambling to meet the EU's data privacy and algorithmic fairness guidelines. Well, for the EU citizens, GDPR has strengthened their rights by ushering in a new era by unifying data protection rules and placing new obligations on tech enterprises on the process of collecting personal user data. The forthcoming regulations have firmly divided Europe into two different camps – a) one that welcomes the need for data privacy and algorithmic fairness in society, b) tech giants who are bristling at the thought of new challenges, such as asking for user consent in simpler terms and tackling the black box problem of AI, which would make eventually make it illegal, with fines imposed to the tune of 4 percent of global turnover, reportedly.