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

Association of Variants in the SPTLC1 Gene With Juvenile Amyotrophic Lateral Sclerosis

Importance: Juvenile amyotrophic lateral sclerosis (ALS) is a rare form of ALS characterized by age of symptom onset less than 25 years and a variable presentation.Objective: To identify the genetic variants associated with juvenile ALS.Design, Setting, and Participants: In this multicenter family-based genetic study, trio whole-exome sequencing was performed to identify the disease-associated gene in a case series of unrelated patients diagnosed with juvenile ALS and severe growth retardation. The patients and their family members were enrolled at academic hospitals and a government research facility between March 1, 2016, and March 13, 2020, and were observed until October 1, 2020. Whole-exome sequencing was also performed in a series of patients with juvenile ALS. A total of 66 patients with juvenile ALS and 6258 adult patients with ALS participated in the study. Patients were selected for the study based on their diagnosis, and all eligible participants were enrolled in the study. None of the participants had a family history of neurological disorders, suggesting de novo variants as the underlying genetic mechanism.Main Outcomes and Measures: De novo variants present only in the index case and not in unaffected family members.Results: Trio whole-exome sequencing was performed in 3 patients diagnosed with juvenile ALS and their parents. An additional 63 patients with juvenile ALS and 6258 adult patients with ALS were subsequently screened for variants in the SPTLC1 gene. De novo variants in SPTLC1 (p.Ala20Ser in 2 patients and p.Ser331Tyr in 1 patient) were identified in 3 unrelated patients diagnosed with juvenile ALS and failure to thrive. A fourth variant (p.Leu39del) was identified in a patient with juvenile ALS where parental DNA was unavailable. Variants in this gene have been previously shown to be associated with autosomal-dominant hereditary sensory autonomic neuropathy, type 1A, by disrupting an essential enzyme complex in the sphingolipid synthesis pathway.Conclusions and Relevance: These data broaden the phenotype associated with SPTLC1 and suggest that patients presenting with juvenile ALS should be screened for variants in this gene.</p

EVC 2022 closed codes

Closed codes applying the Neglected, Acknowledged, Targeted (NAT) taxonomy to the interviews described in:https://figshare.com/articles/online_resource/EVC_Interview_Guide/23552844https://figshare.com/articles/presentation/EVC_Interview_Slides/23552808</ul

Assessing the frontier : Active learning, model accuracy, and multi-objective candidate discovery and optimization

Discovering novel chemicals and materials can be greatly accelerated by iterative machine learning-informed proposal of candidates-active learning. However, standard global error metrics for model quality are not predictive of discovery performance and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing candidates. Furthermore, through synthetic cases, an experimental thermoelectric dataset and a computational organic molecule dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for the acquisition function design.publishe

HPD: Human Proximity Device

With the progressive nature and growing capabilities of sensor technologies, and their increasing commercial availability, developing more affordable, modular, and increasingly smaller devices for real-world applications has become more common now than ever. Considering the recent Coronavirus pandemic, the importance of discerning the number of human individuals within a given enclosed space has become more pronounced for personal safety. Our research into developing a device that counts the number of unique human interactions within an adjustable proximity aims to combine concise and comprehensible statistics, efficient sensor power management, and portability to allow user interactions to be tracked in a set location or on the move. Our current implementation uses a combination of Passive Infrared, LiDAR, and Image Processing to collect and process data locally and provide a running total of all unique interactions. Then, at the user’s discretion, the data can be uploaded through Bluetooth to a web application for further data processing. The device is handheld and portable, and after starting its process, it does not need any additional input until the user wants to stop collecting data. Upon the completion of our device and the setup of our application environment, we will solidify our design with the aggregation and interpretation of our image segmentation and lidar proximity data to report on our phone app as a future feature update