Session-II/13h00m.md

-# High-Throughput Structure Solution and Structure Prediction of Inorganic Compounds
\ No newline at end of file
+# High-Throughput Structure Solution and Structure Prediction of Inorganic Compounds
+
+| Information | Photo |
+| ------ | ------ |
+| `Name` <br> Chris Wolverton <br> <br> `Affiliation` <br> Northwestern University | <img src="uploads/440942c07be2027a20a5603db0d03c76/image1__1_.jpeg" height="150"> |
+
+## Abstract
+
+Sean D. Griesemer<sup>a</sup>, Cheol Woo Park<sup>b</sup>, Logan Ward<sup>c</sup>, Chris Wolverton<sup>a</sup> <br>
+
+<sup>a</sup> Northwestern University, Evanston IL 60208 <br>
+<sup>b</sup> Lawrence Livermore National Laboratory, Livermore, CA 94550 <br>
+<sup>c</sup> Argonne National Laboratory, Lemont, IL 60439 <br>
+
+The computational search for new stable inorganic compounds is faster now than ever before, thanks to the automation of high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive, due to the combinatorically explosive number of hypothetical compounds to consider. To guide the search towards the most likely stable compounds, one must have methods to (a) solve crystal structures from diffraction data where experimental data exists (“structure solution”) and (b) predict unknown crystal structures where no experimental data exists (“structure prediction”). We illustrate methods to tackle both of these challenges: 
+
+Structure solution - DFT calculations require crystal structure information as input; however, due to inherent challenges in solving a compound’s structure from powder diffraction data alone, there are thousands of experimentally synthesized compounds whose structures remain unsolved. We present a rapid DFT-based structure solution method capable of resolving numerous outstanding structure solution problems at low computational cost. As this approach is straightforward and inexpensive, we employ it to solve 514 previously unsolved structures from the Powder Diffraction File, resulting in a 1.3% expansion of the set of all experimental structures in the OQMD. 
+
+Structure prediction – Recommendation engines have been developed in the literature, ranging in strategy from recommending compounds that are chemically similar to existing compounds, to deep learning of formation energies. We conduct a systematic comparison of the performance of previously developed recommendation engines in recovering stable hypothetical compounds in the Open Quantum Materials Database (OQMD), and develop workflows to execute these methods in a highly efficient manner.  In addition, we apply a variety of machine-learning-based tools to automate the prediction of new inorganic compounds. Leveraging these tools, we perform high throughput predictions of thousands of new stable, previously-undiscovered inorganic compounds, and significantly enhance the number of energetically stable compounds in the OQMD. 
+
+## Biography
\ No newline at end of file