Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

Mutations in the Polycomb Group Gene polyhomeotic Lead to Epithelial Instability in both the Ovary and Wing Imaginal Disc in Drosophila

Most human cancers originate from epithelial tissues and cell polarity and adhesion defects can lead to metastasis. The Polycomb-Group of chromatin factors were first characterized in Drosophila as repressors of homeotic genes during development, while studies in mammals indicate a conserved role in body plan organization, as well as an implication in other processes such as stem cell maintenance, cell proliferation, and tumorigenesis. We have analyzed the function of the Drosophila Polycomb-Group gene polyhomeotic in epithelial cells of two different organs, the ovary and the wing imaginal disc.Clonal analysis of loss and gain of function of polyhomeotic resulted in segregation between mutant and wild-type cells in both the follicular and wing imaginal disc epithelia, without excessive cell proliferation. Both basal and apical expulsion of mutant cells was observed, the former characterized by specific reorganization of cell adhesion and polarity proteins, the latter by complete cytoplasmic diffusion of these proteins. Among several candidate target genes tested, only the homeotic gene Abdominal-B was a target of PH in both ovarian and wing disc cells. Although overexpression of Abdominal-B was sufficient to cause cell segregation in the wing disc, epistatic analysis indicated that the presence of Abdominal-B is not necessary for expulsion of polyhomeotic mutant epithelial cells suggesting that additional polyhomeotic targets are implicated in this phenomenon.Our results indicate that polyhomeotic mutations have a direct effect on epithelial integrity that can be uncoupled from overproliferation. We show that cells in an epithelium expressing different levels of polyhomeotic sort out indicating differential adhesive properties between the cell populations. Interestingly, we found distinct modalities between apical and basal expulsion of ph mutant cells and further studies of this phenomenon should allow parallels to be made with the modified adhesive and polarity properties of different types of epithelial tumors

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Impact of Systemic Inflammation and Autoimmune Diseases on apoA-I and HDL Plasma Levels and Functions

The cholesterol of high-density lipoproteins (HDLs) and its major proteic component, apoA-I, have been widely investigated as potential predictors of acute cardiovascular (CV) events. In particular, HDL cholesterol levels were shown to be inversely and independently associated with the risk of acute CV diseases in different patient populations, including autoimmune and chronic inflammatory disorders. Some relevant and direct anti-inflammatory activities of HDL have been also recently identified targeting both immune and vascular cell subsets. These studies recently highlighted the improvement of HDL function (instead of circulating levels) as a promising treatment strategy to reduce inflammation and associated CV risk in several diseases, such as systemic lupus erythematosus and rheumatoid arthritis. In these diseases, anti-inflammatory treatments targeting HDL function might improve both disease activity and CV risk. In this narrative review, we will focus on the pathophysiological relevance of HDL and apoA-I levels/functions in different acute and chronic inflammatory pathophysiological conditions

Oxidovanadium(IV) complexes with chrysin and silibinin: Anticancer activity and mechanisms of action in a human colon adenocarcinoma model

Vanadium compounds were studied during recent years to be considered as a representative of a new class of nonplatinum metal antitumor agents in combination to its low toxicity. On the other hand, flavonoids are a wide family of polyphenolic compounds synthesized by plants that display many interesting biological effects. Since coordination of ligands to metals can improve the pharmacological properties, we report herein, for the first time, a exhaustive study of the mechanisms of action of two oxidovanadium(IV) complexes with the flavonoids: silibinin Na₂[VO(silibinin)₂2]·6H₂O (VOsil) and chrysin [VO(chrysin)₂EtOH]₂(VOchrys) on human colon adenocarcinoma derived cell line HT-29. The complexes inhibited the cell viability of colon adenocarcinoma cells in a dose dependent manner with a greater potency than that the free ligands and free metal, demonstrating the benefit of complexation. The decrease of the ratio of the amount of reduced glutathione to the amount of oxidized glutathione were involved in the deleterious effects of both complexes. Besides, VOchrys caused cell cycle arrest in G2/M phase while VOsil activated caspase 3 and triggering the cells directly to apoptosis. Moreover, VOsil diminished the NF-kB activation via increasing the sensitivity of cells to apoptosis. On the other hand, VOsil inhibited the topoisomerase IB activity concluding that this is important target involved in the anticancer vanadium effects. As a whole, the results presented herein demonstrate that VOsil has a stronger deleterious action than VOchrys on HT-29 cells, whereby suggesting that Vosil is the potentially best candidate for future use in alternative anti-tumor treatments

New light-harvesting materials using accurate and efficient bandgap calculations

Electronic bandgap calculations are presented for 2400 experimentally known materials from the Materials Project database and the bandgaps, obtained with different types of functionals within density functional theory and (partial) self-consistent GW approximation, are compared for 20 randomly chosen compounds forming an unconventional set of ternary and quaternary materials. It is shown that the computationally cheap GLLB-SC potential gives results in good agreement (around 15%) with the more advanced and demanding eigenvalue-self-consistent GW. This allows for a high-throughput screening of materials for different applications where the bandgaps are used as descriptors for the efficiency of a photoelectrochemical device. Here, new light harvesting materials are proposed to be used in a one-photon photoelectrochemical device for water splitting by combining the estimation of the bandgaps with the stability analysis using Pourbaix diagrams and with the evaluation of the position of the band edges. Using this methodology, 25 candidate materials are obtained and 5 of them appear to have a realistic possibility of being used as photocatalyst in a one-photon water splitting device