FAIR data enabling new horizons for materials research.

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data.

Designing materials for catalysis is challenging because the performance is governed by an intricate interplay of various multiscale phenomena, such as the chemical reactions on surfaces and the materials' restructuring during the catalytic process. In the case of supported catalysts, the role of the support material can be also crucial. Here, we address this intricacy challenge by a symbolic-regression artificial intelligence (AI) approach. We identify the key physicochemical parameters correlated with the measured performance, out of many offered candidate parameters characterizing the materials, reaction environment, and possibly relevant underlying phenomena. Importantly, these parameters are obtained by both experiments and ab initio simulations. The identified key parameters might be called "materials genes", in analogy to genes in biology: they correlate with the property or function of interest, but the explicit physical relationship is not (necessarily) known. To demonstrate the approach, we investigate the CO2 hydrogenation catalyzed by cobalt nanoparticles supported on silica. Crucially, the silica support is modified with the additive metals magnesium, calcium, titanium, aluminum, or zirconium, which results in six materials with significantly different performances. These systems mimic hydrothermal vents, which might have produced the first organic molecules on Earth. The key parameters correlated with the CH3OH selectivity reflect the reducibility of cobalt species, the adsorption strength of reaction intermediates, and the chemical nature of the additive metal. By using an AI model trained on basic elemental properties of the additive metals (e.g., ionization potential) as physicochemical parameters, new additives are suggested. The predicted CH3OH selectivity of cobalt catalysts supported on silica modified with vanadium and zinc is confirmed by new experiments.

Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework.

Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly defined only for one-electron systems, it has inspired the self-interaction correction method proposed by Perdew and Zunger (PZ-SIC), which has shown promise in mitigating the many-electron SIE. However, the PZ-SIC method is known for its significant numerical instability. In this study, we introduce a novel constraint that facilitates self-consistent localization of the SIC orbitals in the spirit of Edmiston-Ruedenberg orbitals [Rev. Mod. Phys. 35, 457 (1963)]. Our practical implementation within the all-electron numeric atom-centered orbitals code FHI-aims guarantees efficient and stable convergence of the self-consistent PZ-SIC equations for both molecules and solids. We further demonstrate that our PZ-SIC approach effectively mitigates the SIE in the meta-generalized gradient approximation SCAN functional, significantly improving the accuracy for ionization potentials, charge-transfer energies, and bandgaps for a diverse selection of molecules and solids. However, our PZ-SIC method does have its limitations. It cannot improve the already accurate SCAN results for properties such as cohesive energies, lattice constants, and bulk modulus in our test sets. This highlights the need for new-generation DFAs with more comprehensive applicability.

Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation.

Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides "rules" on how the catalyst properties may be tuned in order to achieve the desired performance.

Anharmonicity in Thermal Insulators: An Analysis from First Principles.

The anharmonicity of atomic motion limits the thermal conductivity in crystalline solids. However, a microscopic understanding of the mechanisms active in strong thermal insulators is lacking. In this Letter, we classify 465 experimentally known materials with respect to their anharmonicity and perform fully anharmonic ab initio Green-Kubo calculations for 58 of them, finding 28 thermal insulators with κ<10 W/mK including 6 with ultralow κ≲1 W/mK. Our analysis reveals that the underlying strong anharmonic dynamics is driven by the exploration of metastable intrinsic defect geometries. This is at variance with the frequently applied perturbative approach, in which the dynamics is assumed to evolve around a single stable geometry.

Recent advances in the SISSO method and their implementation in the SISSO++ code.

Accurate and explainable artificial-intelligence (AI) models are promising tools for accelerating the discovery of new materials. Recently, symbolic regression has become an increasingly popular tool for explainable AI because it yields models that are relatively simple analytical descriptions of target properties. Due to its deterministic nature, the sure-independence screening and sparsifying operator (SISSO) method is a particularly promising approach for this application. Here, we describe the new advancements of the SISSO algorithm, as implemented into SISSO++, a C++ code with Python bindings. We introduce a new representation of the mathematical expressions found by SISSO. This is a first step toward introducing "grammar" rules into the feature creation step. Importantly, by introducing a controlled nonlinear optimization to the feature creation step, we expand the range of possible descriptors found by the methodology. Finally, we introduce refinements to the solver algorithms for both regression and classification, which drastically increase the reliability and efficiency of SISSO. For all these improvements to the basic SISSO algorithm, we not only illustrate their potential impact but also fully detail how they operate both mathematically and computationally.

Ab Initio Approach for Thermodynamic Surface Phases with Full Consideration of Anharmonic Effects: The Example of Hydrogen at Si(100).

A reliable description of surfaces structures in a reactive environment is crucial to understand materials' functions. We present a first-principles theory of replica-exchange grand-canonical-ensemble molecular dynamics and apply it to evaluate phase equilibria of surfaces in a reactive gas-phase environment. We identify the different surface phases and locate phase boundaries including triple and critical points. The approach is demonstrated by addressing open questions for the Si(100) surface in contact with a hydrogen atmosphere. In the range from 300 to 1000 K, we find 25 distinct thermodynamically stable surface phases, for which we also provide microscopic descriptions. Most of the identified phases, including few order-disorder phase transitions, have not yet been observed experimentally. Furthermore, we show that the dynamic Si-Si bonds forming and breaking is the driving force behind the phase transition between 3×1 and 2×1 adsorption patterns.

Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites.

Symbolic regression identifies nonlinear, analytical expressions relating materials properties and key physical parameters. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge hierarchically: identified expressions are used as inputs for further obtaining more complex expressions. Crucially, this framework can transfer knowledge among properties, as demonstrated using the sure-independence-screening-and-sparsifying-operator approach to identify expressions for lattice constant and cohesive energy, which are then used to model the bulk modulus of ABO_{3} perovskites.

How previous treatment changes the metabolomic profile in patients with metastatic breast cancer.

PURPOSE: Metabolites are in the spotlight of attention as promising novel breast cancer biomarkers. However, no study has been conducted concerning changes in the metabolomics profile of metastatic breast cancer patients according to previous therapy. METHODS: We performed a retrospective, single-center, nonrandomized, partially blinded, treatment-based study. Metastatic breast cancer (MBC) patients were enrolled between 03/2010 and 09/2016 at the beginning of a new systemic therapy. The endogenous metabolites in the plasma samples were analyzed using the AbsoluteIDQ® p180 Kit (Biocrates Life Sciences AG, Innsbruck) a targeted, quality and quantitative-controlled metabolomics approach. The statistical analysis was performed using R package, version 3.3.1. ANOVA was used to statistically assess age differences within groups. Furthermore, we analyzed the CTC status of the patients using the CellSearch™ assay. RESULTS: We included 178 patients in our study. Upon dividing the study population according to therapy before study inclusion, we found the following: 4 patients had received no therapy, 165 chemotherapy, and 135 anti-hormonal therapy, 30 with anti-Her2 therapy and 38 had received treatment with bevacizumab. Two metabolites were found to be significantly different, depending on the further therapy of the patients: methionine and serine. Whereas methionine levels were higher in the blood of patients who received an anti-Her2-therapy, serine was lower in patients with endocrine therapy only. CONCLUSION: We identified two metabolites for which concentrations differed significantly depending on previous therapies, which could help to choose the next therapy in patients who have already received numerous different treatments.

Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT).

Catalysis is involved in around 85 % of manufacturing industry and contributes an estimated 25 % to the global domestic product, with the majority of the processes relying on heterogeneous catalysis. Despite the importance in different global segments, the fundamental understanding of heterogeneously catalysed processes lags substantially behind that achieved in other fields. The newly established Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT) targets innovative concepts that could contribute to the scientific developments needed in the research field to achieve net zero greenhouse gas emissions in the chemical industries. This Viewpoint Article presents some of our research activities and visions on the current and future challenges of heterogeneous catalysis regarding green industry and the circular economy by focusing explicitly on critical processes. Namely, hydrogen production, ammonia synthesis, and carbon dioxide reduction, along with new aspects of acetylene chemistry.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence.

ABSTRACT: The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data," containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. IMPACT STATEMENT: Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43577-021-00165-6.

Real-world efficacy of docetaxel plus nintedanib after chemo-immunotherapy failure in advanced pulmonary adenocarcinoma.

Aim: This real-world analysis evaluated docetaxel plus nintedanib in patients with advanced pulmonary adenocarcinoma after chemotherapy and immune checkpoint inhibitor failure, for whom treatment options are limited. Methods: Data were sourced retrospectively from seven German centers. Results: Of 93 patients, overall response rate was 41.4% (disease control rate: 75.9%). Of 57 patients given third-line docetaxel plus nintedanib, overall response rate was 50.0% (disease control rate: 82.7%). Median overall survival following third-line docetaxel plus nintedanib was 8.4 months. Adverse events were consistent with the known safety profile of docetaxel plus nintedanib. Conclusion: To date, this was the largest retrospective, real-world analysis of docetaxel plus nintedanib after chemotherapy-immunotherapy failure, indicating that docetaxel plus nintedanib offers meaningful clinical benefits in this setting.

Lay abstract The standard of care for patients with lung adenocarcinoma has advanced with the introduction of immunotherapy in the first-line setting. However, limited clinical data are available to help guide treatment decisions after failure of chemotherapy and immunotherapy. Nintedanib is an oral antiangiogenic agent that is approved in the EU and other countries in combination with docetaxel for the treatment of patients with advanced/metastatic lung adenocarcinoma after first-line chemotherapy. This study is a retrospective, real-world analysis of docetaxel plus nintedanib in 93 patients with advanced lung adenocarcinoma who progressed on immunotherapy (either in sequence or in combination with chemotherapy). The results suggest that docetaxel plus nintedanib offers a meaningful clinical benefit in this setting. Safety findings were generally consistent with the known safety profile of docetaxel plus nintedanib.

Survey of the Preparation of Cardiovascular Emergency Medications for Adult Cardiovascular Anesthesia.

OBJECTIVES: To describe current practices and safety concerns regarding cardiac emergency medications in cardiac anesthesia. DESIGN: An anonymous survey with multiple-choice questions. SETTINGS: Online survey using Opinio platform. PARTICIPANTS: Cardiac anesthesiologists from United States and Canada. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Response rate was 12% (n = 320), with 78% of respondents from the United States and 22% from Canada. The majority of the respondents were experienced (66%), academic (60%), and worked in large cardiac institutions (81%). Most cardiac emergency medications were prepared in the operating room (53.4%), followed by the pharmacy (34%) and industry (8.2%). American respondents had more medications prepared by a pharmacy (53%) versus Canadian (10%, p < 0.001). The majority (85%) considered expiration time of cardiac medications prepared in the operating room to be more than 12 hours. Familiarity with the American Society of Anesthesiologists guidelines on labeling was 58%, other medication safety guidelines 25%, and 34% were not familiar with any guidelines. The majority used color-coded labeling (95%), and a minority (11%) used bar-code systems. Most respondents (69%) agreed that lack of availability of preprepared medications could compromise patient safety. Having to prepare medications by themselves was a concern for respondents based on distractions (66%), lack of availability for emergencies (53%), labeling errors (41%), incorrect concentration (36%), sterility (33%), and stability (30%). CONCLUSION: This survey found that cardiac emergency medications commonly are prepared in the operating room. The authors identified gaps in familiarity with parenteral medications safety guidelines. Most safety concerns could be addressed with the application of current medication safety guidelines.

The prophylactic use of fibrinogen concentrate in high-risk cardiac surgery.

BACKGROUND: Perioperative blood loss is a major contributor to morbidity and mortality in cardiac surgery. Plasma fibrinogen levels play an essential role in hemostasis and deplete quickly during hemorrhage. The objective of this study was to determine whether prophylactic fibrinogen concentrate administration lowers overall blood product transfusion requirements in high-risk cardiac surgery in patients with low fibrinogen plasma levels. METHODS: The study was performed in a prospective, randomized, and double-blinded design. The investigation included 62 patients undergoing elective, high-risk cardiac surgery. After weaning from cardiopulmonary bypass and reversal of heparin patients received either fibrinogen concentrate or placebo. The primary outcome variable was overall blood product usage 24 hours after intervention. RESULTS: The fibrinogen group received numerically fewer total units of blood products than the placebo group, but the difference was not statistically or clinically significant (for groups n = 27; n = 29 and 19 vs 37 units, respectively, P = .908). The overall transfusion rate in both groups was significantly lower than the institutional average suggested (fibrinogen group 26%, placebo group 28%). The fibrinogen group showed significantly higher fibrinogen levels (2.38 vs 1.83 g/L (end of surgery), P < .001; 3.33 vs 2.68 g/L (12 hours after intervention), P = .003) and improved viscoelastic coagulation parameters (FIBTEM MCF, 27 vs 23 mm, P = .022). CONCLUSION: This randomized, controlled trial demonstrates that point-of-care guided and prophylactic treatment with fibrinogen concentrate does not reduce transfusion of blood products in a setting of unexpectedly low transfusion rate as tested in this cohort, but may improve coagulation parameters in the setting of high-risk cardiac surgery.

Understanding band gaps of solids in generalized Kohn-Sham theory.

The fundamental energy gap of a periodic solid distinguishes insulators from metals and characterizes low-energy single-electron excitations. However, the gap in the band structure of the exact multiplicative Kohn-Sham (KS) potential substantially underestimates the fundamental gap, a major limitation of KS density-functional theory. Here, we give a simple proof of a theorem: In generalized KS theory (GKS), the band gap of an extended system equals the fundamental gap for the approximate functional if the GKS potential operator is continuous and the density change is delocalized when an electron or hole is added. Our theorem explains how GKS band gaps from metageneralized gradient approximations (meta-GGAs) and hybrid functionals can be more realistic than those from GGAs or even from the exact KS potential. The theorem also follows from earlier work. The band edges in the GKS one-electron spectrum are also related to measurable energies. A linear chain of hydrogen molecules, solid aluminum arsenide, and solid argon provide numerical illustrations.

A plasma metabolite panel as biomarkers for early primary breast cancer detection.

In recent years, metabolites have attracted substantial attention as promising novel biomarkers of various diseases. However, breast cancer plasma metabolite studies are still in their infancy. Here, we investigated the potential of metabolites to serve as minimally invasive, early detection markers of primary breast cancer. We profiled metabolites extracted from the plasma of primary breast cancer patients and healthy controls using tandem mass spectrometry (UHPLC-MS/MS and FIA-MS/MS). Two metabolites were found to be upregulated, while 16 metabolites were downregulated in primary breast cancer patients compared to healthy controls in both the training and validation cohorts. A panel of seven metabolites was selected by LASSO regression analysis. This panel could differentiate primary breast cancer patients from healthy controls, with an AUC of 0.87 (95% CI: 0.81 ~ 0.92) in the training cohort and an AUC of 0.80 (95% CI: 0.71 ~ 0.87) in the validation cohort. These significantly differentiated metabolites are mainly involved in the amino acid metabolism and breast cancer cell growth pathways. In conclusion, using a metabolomics approach, we identified metabolites that have potential value for development of a multimarker blood-based test to complement and improve early breast cancer detection. The panel identified herein might be part of a prescreening tool, especially for younger women or for closely observing women with certain risks, to facilitate decision making regarding which individuals should undergo further diagnostic tests. In the future, the combination of metabolites and other blood-based molecular marker sets, such as DNA methylation, microRNA, and cell-free DNA mutation markers, will be an attractive option.

Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers.

Checkpoint inhibitors (CPI) have significantly changed the therapeutic landscape of oncology. We adopted a non-invasive metabolomic approach to understand immunotherapy response and failure in 28 urological cancer patients. In total, 134 metabolites were quantified in patient sera before the first, second, and third CPI doses. Modeling the association between metabolites and CPI response and patient characteristics revealed that one predictive metabolite class  (n = 9/10) were very long-chain fatty acid-containing lipids (VLCFA-containing lipids). The best predictive performance was achieved through a multivariate model, including age and a centroid of VLCFA-containing lipids prior to first immunotherapy (sensitivity: 0.850, specificity: 0.825, ROC: 0.935). We hypothesize that the association of VLCFA-containing lipids with CPI response is based on enhanced peroxisome signaling in T cells, which results in a switch to fatty acid catabolism. Beyond use as a novel predictive non-invasive biomarker, we envision that nutritional supplementation with VLCFA-containing lipids might serve as an immuno sensitizer.

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis.

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis, implemented within the open-source, ab initio framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds characterized in the AFLOW.org repository, and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof of concept, thermodynamic characterizations have been performed for more than 1300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion-including 17 promising C15 b-type structures and 2 half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis and is located at aflow.org/aflow-chull .