Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO4F, molecular adsorption in metal-organic frameworks, an order–disorder transition of Cu-Au alloys, and material discovery for a Fischer–Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.
BackgroundLung cancer is the leading cause of cancer-related death worldwide. Epidermal growth factor receptor (EGFR) - tyrosine kinase inhibitor (TKI) is used for the patients with EGFR-mutant lung cancer. Recently, phase III studies in the patients with EGFR-mutant demonstrated that EGFR-TKI monotherapy improved progression-free survival compared with platinum-doublet chemotherapy. The echinoderm microtubule-associated protein-like 4 (EML4) - anaplastic lymphoma kinase (ALK) fusion oncogene represents one of the newest molecular targets in non-small cell lung cancer (NSCLC). Patients who harbor EML4-ALK fusions have been associated with a lack of EGFR or KRAS mutations.Case presentationWe report a 39-year-old patient diagnosed as adenocarcinoma harboring EGFR mutation and EML4-ALK fusion gene. We treated this patient with erlotinib as the third line therapy, but no clinical benefit was obtained.ConclusionWe experienced a rare case with EGFR mutation and EML4-ALK. Any clinical benefit using EGFR-TKI was not obtained in our case. The therapeutic choice for the patients with more than one driver mutations is unclear. We needs further understanding of the lung cancer molecular biology and the biomarker infomation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.