982 research outputs found
By how much can closed-loop frameworks accelerate computational materials discovery?
The implementation of automation and machine learning surrogatization within
closed-loop computational workflows is an increasingly popular approach to
accelerate materials discovery. However, the scale of the speedup associated
with this paradigm shift from traditional manual approaches remains an open
question. In this work, we rigorously quantify the acceleration from each of
the components within a closed-loop framework for material hypothesis
evaluation by identifying four distinct sources of speedup: (1) task
automation, (2) calculation runtime improvements, (3) sequential
learning-driven design space search, and (4) surrogatization of expensive
simulations with machine learning models. This is done using a time-keeping
ledger to record runs of automated software and corresponding manual
computational experiments within the context of electrocatalysis. From a
combination of the first three sources of acceleration, we estimate that
overall hypothesis evaluation time can be reduced by over 90%, i.e., achieving
a speedup of . Further, by introducing surrogatization into the
loop, we estimate that the design time can be reduced by over 95%, i.e.,
achieving a speedup of -. Our findings present a clear
value proposition for utilizing closed-loop approaches for accelerating
materials discovery.Comment: added Supplementary Informatio
A Critical Evaluation of a Self-Driving Laboratory for the Optimization of Electrodeposited Earth-Abundant Mixed-Metal Oxide Catalysts for the Oxygen Evolution Reaction (OER)
This work highlights the potential of earth-abundant mixed-metal oxide
catalysts for the acid-based oxygen evolution reaction. These catalysts offer
numerous combinations of metal-centre compositions, which can enhance catalytic
activity and stability compared to precious-metal-based catalysts commonly used
today. Despite substantial research in this field, there is a need for new
methods and approaches to accelerate the exploration of these materials. In
this study, we present a comprehensive approach to designing, developing, and
implementing a self-driving laboratory to optimize the electrodeposition
synthesis of amorphous mixed-metal oxide catalysts for the acidic oxygen
evolution reaction. We particularly emphasize the development of methodologies
to address experimental variability. We investigate crucial parameters and
considerations when transitioning from manual bench-top synthesis and
evaluation to automation and machine learning guided optimization. We address
both experimental and optimization algorithm considerations in the presence of
experimental variability. To illustrate our approach, we demonstrate the
optimization of CoFeMnPbOx electrodeposited catalyst materials through multiple
campaigns. Our results highlight considerations for optimizing overpotential
and stability based on the outcomes of our experiments.Comment: 46 pages, 8 main body figures, 11 supporting information figure
Progress and prospects for accelerating materials science with automated and autonomous workflows
Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery
Toward a Unified Description of Battery Data
Battery research initiatives and giga-scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high-performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery-related activities across different fields, accelerate the flow of knowledge in both human- and machine-readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need.publishedVersio
SimStack: An Intuitive Workflow Framework
Establishing a fundamental understanding of the nature of materials via computational simulation approaches requires knowledge from different areas, including physics, materials science, chemistry, mechanical engineering, mathematics, and computer science. Accurate modeling of the characteristics of a particular system usually involves multiple scales and therefore requires the combination of methods from various fields into custom-tailored simulation workflows. The typical approach to developing patch-work solutions on a case-to-case basis requires extensive expertise in scripting, command-line execution, and knowledge of all methods and tools involved for data preparation, data transfer between modules, module execution, and analysis. Therefore multiscale simulations involving state-of-the-art methods suffer from limited scalability, reproducibility, and flexibility. In this work, we present the workflow framework SimStack that enables rapid prototyping of simulation workflows involving modules from various sources. In this platform, multiscale- and multimodule workflows for execution on remote computational resources are crafted via drag and drop, minimizing the required expertise and effort for workflow setup. By hiding the complexity of high-performance computations on remote resources and maximizing reproducibility, SimStack enables users from academia and industry to combine cutting-edge models into custom-tailored, scalable simulation solutions
More is Different: Modern Computational Modeling for Heterogeneous Catalysis
La combinació d'observacions experimentals i estudis de la Density Functional Theory (DFT) és un dels pilars de la
investigació química moderna. Atès que permeten recopilar informació física addicional d'un sistema químic,
difícilment accessible a través de l'entorn experimental, aquests estudis es fan servir àmpliament per modelar i predir
el comportament d'una gran varietat de compostos químics en entorns únics. A la catàlisi heterogènia, els models
DFT s'utilitzen habitualment per avaluar la interacció entre els compostos moleculars i els catalitzadors, vinculant
aquestes interpretacions amb els resultats experimentals. Tanmateix, l'alta complexitat trobada tant als escenaris
catalítics com a la reactivitat, implica la necessitat de metodologies sofisticades que requereixen automatització,
emmagatzematge i anàlisi per estudiar correctament aquests sistemes. Aquest treball presenta el desenvolupament i
la combinació de múltiples metodologies per avaluar correctament la complexitat d'aquests sistemes químics. A més,
aquest treball mostra com s'han utilitzat les tècniques proporcionades per estudiar noves configuracions catalítiques
d'interès acadèmic i industrial.La combinación de observaciones experimentales y estudios de la Density Functional Theory (DFT) es uno de los
pilares de la investigación química moderna. Dado que permiten recopilar información física adicional de un sistema
químico, difícilmente accesible a través del entorno experimental, estos estudios se emplean ampliamente para
modelar y predecir el comportamiento de una gran variedad de compuestos químicos en entornos únicos. En la
catálisis heterogénea, los modelos DFT se emplean habitualmente para evaluar la interacción entre los compuestos
moleculares y los catalizadores, vinculando estas interpretaciones con los resultados experimentales. Sin embargo, la
alta complejidad encontrada tanto en los escenarios catalíticos como en la reactividad, implica la necesidad de
metodologías sofisticadas que requieren de automatización, almacenamiento y análisis para estudiar correctamente
estos sistemas. Este trabajo presenta el desarrollo y la combinación de múltiples metodologías con el objetivo de
evaluar correctamente la complejidad de estos sistemas químicos. Además, este trabajo muestra cómo las técnicas
proporcionadas se han utilizado para estudiar nuevas configuraciones catalíticas de interés académico e industrial.The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern
chemical research. As they enable the collection of additional physical information of a chemical system, hardly
accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict
the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous
catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular
compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity
found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation,
storage and analysis to correctly study these systems. Here, I present the development and combination of multiple
methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been
actively used to study novel catalytic settings of academic and industrial interest
Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol
Lithium-ion batteries (LIBs) are crucial for the green economy, powering
portable electronics, electric vehicles, and renewable energy systems. The
solid-electrolyte interphase (SEI) is vital for LIB operation, performance, and
safety. SEI forms due to thermal instability at the anode-electrolyte
interface, with electrolyte reduction products stabilizing it as an
electrochemical buffer. This article aims to enhance the parametrization of the
ReaxFF force field for accurate molecular dynamics (MD) simulations of SEI in
LIBs.
Focus is on Lithium Fluoride (LiF), an inorganic salt with favorable
properties in the passivation layer. The protocol heavily relies on Python
libraries for atomistic simulations, enabling robust automation of
reparameterization steps. The proposed configurations and dataset enable the
new ReaxFF to accurately represent the solid nature of LiF and improve mass
transport property prediction in MD simulations. Optimized ReaxFF surpasses
previous force fields by adjusting lithium diffusivity, resulting in a
significant improvement in room temperature prediction by two orders of
magnitude.
However, our comprehensive investigation reveals ReaxFF's strong sensitivity
to the training set, challenging its ability to interpolate the potential
energy surface. Consequently, the current ReaxFF formulation is suitable for
modeling specific phenomena by utilizing the proposed interactive
reparameterization protocol and constructing a dataset. This work is an
important step towards refining ReaxFF for precise reactive MD simulations,
shedding light on challenges and limitations in force field parametrization.
The demonstrated limitations underscore the potential for developing more
advanced force fields through our interactive reparameterization protocol,
enabling accurate and comprehensive MD simulations in the future.Comment: 56 pages, 8 figures (main) 21 figures (suppi
Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation
Synthesis of thin films has traditionally relied upon slow, sequential
processes carried out with substantial human intervention, frequently utilizing
a mix of experience and serendipity to optimize material structure and
properties. With recent advances in autonomous systems which combine synthesis,
characterization, and decision making with artificial intelligence (AI), large
parameter spaces can be explored autonomously at rates beyond what is possible
by human experimentalists, greatly accelerating discovery, optimization, and
understanding in materials synthesis which directly address the grand
challenges in synthesis science. Here, we demonstrate autonomous synthesis of a
contemporary 2D material by combining the highly versatile pulsed laser
deposition (PLD) technique with automation and machine learning (ML). We
incorporated in situ and real-time spectroscopy, a high-throughput methodology,
and cloud connectivity to enable autonomous synthesis workflows with PLD.
Ultrathin WSe2 films were grown using co-ablation of two targets and showed a
10x increase in throughput over traditional PLD workflows. Gaussian process
regression and Bayesian optimization were used with in situ Raman spectroscopy
to autonomously discover two distinct growth windows and the process-property
relationship after sampling only 0.25% of a large 4D parameter space. Any
material that can be grown with PLD could be autonomously synthesized with our
platform and workflows, enabling accelerated discovery and optimization of a
vast number of materials
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