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RISS 인기검색어
- 검색키워드
- 저자 : Davies Daniel W. (검색결과 3 건)
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Davies, Daniel ,W.,Butler, Keith T.,Skelton, Jonathan M.,Xie, Congwei,Oganov, Artem R.,Walsh, Aron Royal Society of Chemistry 2018 Chemical Science Vol.9 No.4
<▼1><P>The standard paradigm in computational materials science is INPUT: <SMALL>STRUCTURE;</SMALL> OUTPUT: <SMALL>PROPERTIES</SMALL>, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace.</P></▼1><▼2><P>The standard paradigm in computational materials science is INPUT: S<SMALL>TRUCTURE</SMALL>; OUTPUT: P<SMALL>ROPERTIES</SMALL>, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace. We report a high-throughput screening procedure that uses compositional descriptors to search for new photoactive semiconducting compounds. We show how feeding high-ranking element combinations to structure prediction algorithms can constitute a pragmatic computer-aided materials design approach. Techniques based on structural analogy (data mining of known lattice types) and global searches (direct optimisation using evolutionary algorithms) are combined for translating between chemical composition and crystal structure. The properties of four novel chalcohalides (Sn<SUB>5</SUB>S<SUB>4</SUB>Cl<SUB>2</SUB>, Sn<SUB>4</SUB>SF<SUB>6</SUB>, Cd<SUB>5</SUB>S<SUB>4</SUB>Cl<SUB>2</SUB> and Cd<SUB>4</SUB>SF<SUB>6</SUB>) are predicted, of which two are calculated to have bandgaps in the visible range of the electromagnetic spectrum.</P></▼2>
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High Coronary Shear Stress in Patients With Coronary Artery Disease Predicts Myocardial Infarction
Kumar, Arnav,Thompson, Elizabeth W.,Lefieux, Adrien,Molony, David S.,Davis, Emily L.,Chand, Nikita,Fournier, Stephane,Lee, Hee Su,Suh, Jon,Sato, Kimi,Ko, Yi-An,Molloy, Daniel,Chandran, Karthic,Hossein Elsevier 2018 JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY - Vol.72 No.16
<P><B>Abstract</B></P> <P><B>Background</B></P> <P>Coronary lesions with low fractional flow reserve (FFR) that are treated medically are associated with higher revascularization rates. High wall shear stress (WSS) has been linked with increased plaque vulnerability.</P> <P><B>Objectives</B></P> <P>This study investigated the prognostic value of WSS measured in the proximal segments of lesions (WSS<SUB>prox</SUB>) to predict myocardial infarction (MI) in patients with stable coronary artery disease (CAD) and hemodynamically significant lesions. The authors hypothesized that in patients with low FFR and stable CAD, higher WSS<SUB>prox</SUB> would predict MI.</P> <P><B>Methods</B></P> <P>Among 441 patients in the FAME II (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation II) trial with FFR ≤0.80 who were randomized to medical therapy alone, 34 (8%) had subsequent MI within 3 years. Patients with vessel-related MI and adequate angiograms for 3-dimensional reconstruction (n = 29) were propensity matched to a control group with no MI (n = 29) by using demographic and clinical variables. Coronary lesions were divided into proximal, middle, and distal, along with 5-mm upstream and downstream segments. WSS was calculated for each segment.</P> <P><B>Results</B></P> <P>Median age was 62 years, and 46 (79%) were male. In the marginal Cox model, whereas lower FFR showed a trend (hazard ratio: 0.084; p = 0.064), higher WSS<SUB>prox</SUB> (hazard ratio: 1.234; p = 0.002, C-index = 0.65) predicted MI. Adding WSS<SUB>prox</SUB> to FFR resulted in a significant increase in global chi-square for predicting MI (p = 0.045), a net reclassification improvement of 0.69 (p = 0.005), and an integrated discrimination index of 0.11 (p = 0.010).</P> <P><B>Conclusions</B></P> <P>In patients with stable CAD and hemodynamically significant lesions, higher WSS in the proximal segments of atherosclerotic lesions is predictive of MI and has incremental prognostic value over FFR.</P> <P><B>Central Illustration</B></P> <P>[DISPLAY OMISSION]</P>
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Machine learning for molecular and materials science
Butler, Keith T.,Davies, Daniel W.,Cartwright, Hugh,Isayev, Olexandr,Walsh, Aron Nature Publishing Group UK 2018 Nature Vol.559 No.7715
<P>Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</P>
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