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New tools enable new ways of working, and materials science is no exception. In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows.

Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.

As complementary metal–oxide–semiconductor (CMOS) scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to extend the performance of today’s computers substantially. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a computational memory unit, are used for both processing and memory. However, to reach the numerical accuracy typically required for data analytics and scientific computing, limitations arising from device variability and non-ideal device characteristics need to be addressed. Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to iteratively improve the accuracy of the solution. The system therefore benefits from both the high precision of digital computing and the energy/areal efficiency of in-memory computing. We experimentally demonstrate the efficacy of the approach by accurately solving systems of linear equations, in particular, a system of 5,000 equations using 998,752 phase-change memory devices. A hybrid system that combines a von Neumann machine with a computational memory unit can offer both the high precision of digital computing and the energy/areal efficiency of in-memory computing, which is illustrated by accurately solving a system of 5,000 equations using 998,752 phase-change memory devices.

Liquid argon detectors, with mass up to 100 kton, are being actively studied in the context of proton decay searches, neutrino astrophysics and for the next generation of long baseline neutrino oscillation experiments to study the neutrino mass hierarchy and CP violation in the leptonic sector. The proposed Giant Liquid Argon Charge Imaging ExpeRiment (GLACIER) offers a well defined conceptual design for such a detector. In this paper we present the GLACIER design and some of the R&D activities pursued within the GLACIER.

Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a power-efficient computing paradigm that combines low- and high-precision arithmetic. We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a fine-grain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications.

Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a power-efficient computing paradigm that combines low- and high-precision arithmetic. We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a fine-grain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications.

Aluminas and their surface chemistry play a vital role in many areas of modern technology. The behavior of adsorbed water is particularly important and poorly understood. Simulations of hydrated α-alumina (0001) surfaces with ab initio molecular dynamics elucidate many aspects of this problem, especially the complex dynamics of water dissociation and related surface reactions. At low water coverage, free energy profiles established that molecularly adsorbed water is metastable and dissociates readily, even in the absence of defects, by a kinetically preferred pathway. Observations at higher water coverage revealed rapid dissociation and unanticipated collective effects, including water-catalyzed dissociation and proton transfer reactions between adsorbed water and hydroxide. The results provide a consistent interpretation of the measured coverage dependence of water heats of adsorption, hydroxyl vibrational spectra, and other experiments.

On June 26, 2007, IBM announced the Blue Gene/P(TM) system as the leading offering in its massively parallel Blue Gene® supercomputer line, succeeding the Blue Gene/L(TM) system. The Blue Gene/P system is designed to scale to at least 262,144 quad-processor nodes, with a peak performance of 3.56 petaflops. More significantly, the Blue Gene/P system enables this unprecedented scaling via architectural and design choices that maximize performance per watt, performance per square foot, and mean time between failures. This paper describes our vision of this petascale system, that is, a system capable of delivering more than a quadrillion (10^sup 15^) floating-point operations per second. We also provide an overview of the system architecture, packaging, system software, and initial benchmark results. [PUBLICATION ABSTRACT]