Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions

Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.

Dynamic Probe of Superionic Transition in Fluorite Structured Compounds

Superionic transition(SIT)is an extraordinary phenomenon where a compound attains high ionic conductivity through anomalous disordering of mobile-ion sublattice.Comprehending SIT offers notable prospects for the advancement of superionic conductors(SICs)for diverse applications.However,the investigation of SIT is impeded by its intricate and stochastic characteristics,coupled with the absence of adequate methods for characterizing,quantifying,and analyzing its microscopic properties.Here we show that the SIT can be discerned through the dynamic signatures of disordering events underlying the increase in ionic conductivity.The adoption of a dynamic perspective as opposed to the conventional treatment of equilibrium properties brings significant advantage to scrutinize the microscopic characteristics of SIT.Our results show the SIT in the prototypical family of fluorite compounds is characterized by the scaleinvariant disordering dynamics independent of temperature or extent of disorder.The observation of scale-invariance in the absence of external tuning implies that the superionic conduction is self-tuned to criticality by intrinsic dynamics.The connection between ionic diffusion and self-organized criticality provides a novel platform for understanding,analyzing,and manipulating SIT towards better SICs.

Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

Nanoscale cantilevers(nanocantilevers)made from carbon nanotubes(CNTs)provide tremendous benefits in sensing and electromagnetic applications.This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis,which contain manual,time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs.Here,we demonstrate a simple and Artificial Intelligence(Al)-assisted method for the effcient fabrication of a massive CNT-based nanocantilever.We used randomly positioned single CNTs on the substrate.The trained deep neural network recognizes the CNTs,measures their positions,and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever.Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s,whereas comparable manual processing requires 12 h.Notwithstanding the small measurement error by the trained network(within 200 nm for 90%of the recognized CNTs),more than 34 nanocantilevers were successfully fabricated in one process.Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever,in which the output current is obtained with a low applied voltage.We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing.The activation function,which is a key function in a neural network,was physically realized using an individual CNT-based field emitter.The introduced neural network with the CNT-based field emitters recognized handwritten images successfully.We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool.Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science experiment.Measuring a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in performance.By building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses.Human interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist.We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data.

A review of the recent progress in battery informatics

Batteries are of paramount importance for the energy storage,consumption,and transportation in the current and future society.Recently machine learning(ML)has demonstrated success for improving lithium-ion technologies and beyond.This in-depth review aims to provide state-of-art achievements in the interdisciplinary field of ML and battery research and engineering,the battery informatics.We highlight a crucial hurdle in battery informatics,the availability of battery data,and explain the mitigation of the data scarcity challenge with a detailed review of recent achievements.This review is concluded with a perspective in this new but exciting field.

A strategy to apply machine learning to small datasets in materials science

There is growing interest in applying machine learning techniques in the research of materials science.However,although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields,the influence of availability of materials data on training machine learning models has not yet been studied,which prevents the possibility to establish accurate predictive rules using small materials datasets.Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models.Instead of affecting the model precision directly,the effect of data size is mediated by the degree of freedom(DoF)of model,resulting in the phenomenon of association between precision and DoF.The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction,which consequently restricts the accurate prediction in unknown domains.We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data,which increases the accuracy of prediction without the cost of higher DoF.In three case studies of predicting the band gap of binary semiconductors,lattice thermal conductivity,and elastic properties of zeolites,the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels,demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

Multi-component background learning automates signal detection for spectroscopic data

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities.Artificial Intelligence offers new possibilities for automating data interpretation to generate large,high-quality datasets.Background subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data interpretation.Herein,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest.The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals.While the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework.Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.