147 research outputs found
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials requires the
automatic and accurate analysis of X-ray diffraction spectra. For this task, we
designed a probabilistic deep learning algorithm to identify complex
multi-phase mixtures. At the core of this algorithm lies an ensemble
convolutional neural network trained on simulated diffraction spectra, which
are systematically augmented with physics-informed perturbations to account for
artifacts that can arise during experimental sample preparation and synthesis.
Larger perturbations associated with off-stoichiometry are also captured by
supplementing the training set with hypothetical solid solutions. Spectra
containing mixtures of materials are analyzed with a newly developed branching
algorithm that utilizes the probabilistic nature of the neural network to
explore suspected mixtures and identify the set of phases that maximize
confidence in the prediction. Our model is benchmarked on simulated and
experimentally measured diffraction spectra, showing exceptional performance
with accuracies exceeding those given by previously reported methods based on
profile matching and deep learning. We envision that the algorithm presented
here may be integrated in experimental workflows to facilitate the
high-throughput and autonomous discovery of inorganic materials
Autonomous decision making for solid-state synthesis of inorganic materials
To aid in the automation of inorganic materials synthesis, we introduce an
algorithm (ARROWS3) that guides the selection of precursors used in solid-state
reactions. Given a target phase, ARROWS3 iteratively proposes experiments and
learns from their outcomes to identify an optimal set of precursors that leads
to maximal yield of that target. Initial experiments are selected based on
thermochemical data collected from first principles calculations, which enable
the identification of precursors exhibiting large thermodynamic force to form
the desired target. Should the initial experiments fail, their associated
reaction paths are determined by sampling a range of synthesis temperatures and
identifying their products. ARROWS3 then uses this information to pinpoint
which intermediate reactions consume most of the available free energy
associated with the starting materials. In subsequent experimental iterations,
precursors are selected to avoid such unfavorable reactions and therefore
maintain a strong driving force to form the target. We validate this approach
on three experimental datasets containing results from more than 200 distinct
synthesis procedures. When compared to several black-box optimization
algorithms, ARROWS3 identifies the most effective set of precursors for each
target while requiring substantially fewer experimental iterations. These
findings highlight the importance of using domain knowledge in the design of
optimization algorithms for materials synthesis, which are critical for the
development of fully autonomous research platforms
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Predicting the stability of the perovskite structure remains a longstanding
challenge for the discovery of new functional materials for many applications
including photovoltaics and electrocatalysts. We developed an accurate,
physically interpretable, and one-dimensional tolerance factor, {\tau}, that
correctly predicts 92% of compounds as perovskite or nonperovskite for an
experimental dataset of 576 materials ( , ,
, , ) using a novel data analytics approach based on SISSO
(sure independence screening and sparsifying operator). {\tau} is shown to
generalize outside the training set for 1,034 experimentally realized single
and double perovskites (91% accuracy) and is applied to identify 23,314 new
double perovskites () ranked by their probability of
being stable as perovskite. This work guides experimentalists and theorists
towards which perovskites are most likely to be successfully synthesized and
demonstrates an approach to descriptor identification that can be extended to
arbitrary applications beyond perovskite stability predictions
Machine-learning rationalization and prediction of solid-state synthesis conditions
There currently exist no quantitative methods to determine the appropriate
conditions for solid-state synthesis. This not only hinders the experimental
realization of novel materials but also complicates the interpretation and
understanding of solid-state reaction mechanisms. Here, we demonstrate a
machine-learning approach that predicts synthesis conditions using large
solid-state synthesis datasets text-mined from scientific journal articles.
Using feature importance ranking analysis, we discovered that optimal heating
temperatures have strong correlations with the stability of precursor materials
quantified using melting points and formation energies (, ). In contrast, features derived from the thermodynamics of
synthesis-related reactions did not directly correlate to the chosen heating
temperatures. This correlation between optimal solid-state heating temperature
and precursor stability extends Tamman's rule from intermetallics to oxide
systems, suggesting the importance of reaction kinetics in determining
synthesis conditions. Heating times are shown to be strongly correlated with
the chosen experimental procedures and instrument setups, which may be
indicative of human bias in the dataset. Using these predictive features, we
constructed machine-learning models with good performance and general
applicability to predict the conditions required to synthesize diverse chemical
systems. Codes and data used in this work can be found at:
https://github.com/CederGroupHub/s4
Players, Characters, and the Gamer's Dilemma
Is there any difference between playing video games in which the player's character commits murder and video games in which the player's character commits pedophilic acts? Morgan Luck's “Gamer's Dilemma” has established this question as a puzzle concerning notions of permissibility and harm. We propose that a fruitful alternative way to approach the question is through an account of aesthetic engagement. We develop an alternative to the dominant account of the relationship between players and the actions of their characters, and argue that the ethical difference between so-called “virtual murder” and “virtual pedophilia” is to be understood in terms of the fiction-making resources available to players. We propose that the relevant considerations for potential players to navigate concern (1) attempting to make certain characters intelligible, and (2) using aspects of oneself as resources for homomorphic representation.Peer reviewe
Observing and modeling the sequential pairwise reactions that drive solid-state ceramic synthesis
Solid-state synthesis from powder precursors is the primary processing route
to advanced multicomponent ceramic materials. Designing ceramic synthesis
routes is usually a laborious, trial-and-error process, as heterogeneous
mixtures of powder precursors often evolve through a complicated series of
reaction intermediates. Here, we show that phase evolution from multiple
precursors can be modeled as a sequence of pairwise interfacial reactions, with
thermodynamic driving forces that can be efficiently calculated using ab initio
methods. Using the synthesis of the classic high-temperature superconductor
YBaCuO (YBCO) as a representative system, we rationalize how
replacing the common BaCO precursor with BaO redirects phase evolution
through a kinetically-facile pathway. Our model is validated from in situ X-ray
diffraction and in situ microscopy observations, which show rapid YBCO
formation from BaO in only 30 minutes. By combining thermodynamic modeling
with in situ characterization, we introduce a new computable framework to
interpret and ultimately design synthesis pathways to complex ceramic
materials
Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data
Background: MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown. Methodology: We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters. Conclusion: miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex. © 2009 Corcoran et al
Astrometry and geodesy with radio interferometry: experiments, models, results
Summarizes current status of radio interferometry at radio frequencies
between Earth-based receivers, for astrometric and geodetic applications.
Emphasizes theoretical models of VLBI observables that are required to extract
results at the present accuracy levels of 1 cm and 1 nanoradian. Highlights the
achievements of VLBI during the past two decades in reference frames, Earth
orientation, atmospheric effects on microwave propagation, and relativity.Comment: 83 pages, 19 Postscript figures. To be published in Rev. Mod. Phys.,
Vol. 70, Oct. 199
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