Exploring the high-pressure materials genome

A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by predicting the composition, structure, and properties of high-pressure compounds. However, such techniques are usually computationally expensive and not suitable for large-scale combinatorial exploration. On the other hand, data-driven computational approaches using large materials databases are useful for the analysis of energetics and stability of hundreds of thousands of compounds, but their utility for materials discovery is largely limited to idealized conditions of zero temperature and pressure. Here, we present a novel framework combining the two computational approaches, using a simple linear approximation to the enthalpy of a compound in conjunction with ambient-conditions data currently available in high-throughput databases of calculated materials properties. We demonstrate its utility by explaining the occurrence of phases in nature that are not ground states at ambient conditions and estimating the pressures at which such ambient-metastable phases become thermodynamically accessible, as well as guiding the exploration of ambient-immiscible binary systems via sophisticated structural search methods to discover new stable high-pressure phases.Comment: 14 pages, 6 figure

The Phase Diagram of all Inorganic Materials

Understanding how the arrangement of atoms and their interactions determine material behavior has been the dominant paradigm in materials science. A complementary approach is studying the organizational structure of networks of materials, defined on the basis of interactions between materials themselves. In this work, we present the "phase diagram of all known inorganic materials", an extremely-dense complex network of nearly $2.1 \times 10^4$ stable inorganic materials (nodes) connected with $41 \times 10^6$ tie-lines (edges) defining their two-phase equilibria, as computed via high-throughput density functional theory. We show that the degree distribution of this network follows a lognormal form, with each material connected to on average 18% of the other materials in the network via tie-lines. Analyzing the structure and topology of this network has potential to uncover new materials knowledge inaccessible from the traditional bottom-up (atoms to materials) approaches. As an example, we derive a data-driven metric for the reactivity of a material as characterized by its connectedness in the network, and quantitatively identify the noblest materials in nature

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 $\sim$$10\times$. Further, by introducing surrogatization into the loop, we estimate that the design time can be reduced by over 95%, i.e., achieving a speedup of $\sim$$15$-$20\times$. Our findings present a clear value proposition for utilizing closed-loop approaches for accelerating materials discovery.Comment: added Supplementary Informatio

Reproducibility in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD

A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations; for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 {\AA}$^3$/atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and 0.15 $\mu_{\rm B}$/formula unit (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. We trace some of the larger discrepancies to choices involving pseudopotentials, the DFT+U formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility.Comment: Authors VIH and CKHB contributed equally to this wor

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe