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| 1 | Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials显示文摘Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy. | Maxwell TDylla Alexander Dunn Shashwat Anand Anubhav Jain G.Jeffrey Snyder | 2020 | Research2020，，1： | 2 |
| 2 | Poly(ethylene glycol)-pro-drug conjugates:concept,design,and applications显示文摘 | Shashwat S Aher N Patil R | 2012 | J DrugDeliv2012，2012，10： | 1 |
| 3 | Modifications of Cell Signalling and Redox Balance by Targeting Protein Acetylation Using Natural and Engineered Molecules: Implications in Cancer Therapy显示文摘 | Kavya Venkateswaran Amit Verma Anant N. Bhatt Paban K. Agrawala Hanumantharao G. Raj Shashwat Malhotra Ashok K. Prasad Olivier De Wever Marc E. Bracke Luciano Saso Virinder S. Parmar Anju Shrivastava B.S. Dwarakanath | 2014 | Current Topics in Medicinal Chemistry2014，，22： | 1 |
| 4 | Magnetic nanoparticles grafted with cyclodextrin for hydrophobic drug delivery显示文摘 | Shashwat S Banerjee Chen Dong-Hwang | 2007 | Chem Mater2007，19，25： | 1 |
| 5 | Cyclodextrin-conjugated nanocarrier for magnetically guided delivery of hydrophobic drugs显示文摘 | Shashwat S. Banerjee Dong-Hwang Chen | 2009 | Journal of Nanoparticle Research2009，，8： | 1 |
| 6 | A118g polymorphism in mu opioid receptor gene ( oprrnl ) : association with opiate addiction in subjects of Indian origin显示文摘 | Kapur S Shared S Shashwat RA | 2007 | Journal Of Integrative Neuroscience2007，6，4： | 1 |
| 7 | Empirical modeling of dopability in diamond-like semiconductors显示文摘Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications(e.g.,thermoelectrics,transparent conductors,photovoltaics).This barrier has been particularly pernicious in the realm of high-throughput property prediction,where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost.In this work,we explore the application of machine learning for high-throughput carrier concentration range prediction.Bounding the model within diamond-like semiconductors,the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary.The data were analyzed using various statistical and machine learning methods.Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p-and n-type dopability.The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts.Finally,dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials. | Samuel A.Miller Maxwell Dylla Shashwat Anand Kiarash G.ordiz G.Jeffrey Snyder Eric S.Toberer | 2018 | npj Computational Materials2018，，1： | 1 |
| 8 | Cyclodextrin conjugated magnetic colloidal nanoparticles as a nanocarrier for targeted anticancer drug delivery 显示文摘 | Shashwat S B Dong H C | 2008 | Nanotechnology2008，19，26： | 1 |
| 9 | Cyclodextrin-conjugated Nanocarrier for Magnetically Guided Delivery of Hydrophobic Drugs显示文摘 | Shashwat S B Chen D H | 2009 | Reseach Paper2009，11，： | 1 |
| 10 | Treatment of oil spill by sorption technique using fatty acid grafted sawdust 显示文摘 | SHASHWAT S BANERJEE MILIND V JOSHI RADHA V JAYARAM | 2006 | Chemosphere2006，64，： | 1 |
| 11 | A Convergent Understanding of Charged Defects显示文摘CONSPECTUS:Historically,defects in semiconductors and ionic conductors have been studied using very different approaches.In the solid-state ionics community,nonstoichiometry and defect thermochemistry are often probed directly through experiments.The dependency of defect concentrations on chemical conditions(typically oxygen pressure)are modeled using a physical chemistry framework and compactly represented by the well-known Brouwer diagrams. | Shashwat Anand Michael Y.Toriyama Chris Wolverton Sossina M.Haile G.Jeffrey Snyder | 2022 | Accounts of Materials Research2022，3，7： | 0 |
| 12 | Inherent Anharmonicity of Harmonic Solids显示文摘Atomic vibrations,in the form of phonons,are foundational in describing the thermal behavior of materials.The possible frequencies of phonons in materials are governed by the complex bonding between atoms,which is physically represented by a spring-mass model that can account for interactions(spring forces)between the atoms(masses).The lowest-order,harmonic,approximation only considers linear forces between atoms and is thought incapable of explaining phenomena like thermal expansion and thermal conductivity,which are attributed to nonlinear,anharmonic,interactions.Here,we show that the kinetic energy of atoms in a solid produces a pressure much like the kinetic energy of atoms in a gas does.This vibrational or phonon pressure naturally increases with temperature,as it does in a gas and therefore results in a thermal expansion.Because thermal expansion thermodynamically defines a Grüneisen parameterγ,which is a typical metric of anharmonicity,we show that even a harmonic solid will necessarily have some anharmonicity.A consequence of this phonon pressure model is a harmonic estimation of the Grüneisen parameter asγ≈(3/2)(3−4x^(2))/(1+2x^(2)),where x=vt/vl is the ratio of the transverse and longitudinal speeds of sound.We demonstrate the immediate utility of this model by developing a high-throughput harmonic estimate of lattice thermal conductivity that is comparable to other state-of-the-art estimations.By linking harmonic and anharmonic properties explicitly,this study provokes new ideas about the fundamental nature of anharmonicity,while also providing a basis for new material engineering design metrics. | Matthias T.Agne Shashwat Anand G.Jeffrey Snyder | 2022 | Research2022，，3： | 0 |
| 13 | Cascade N-Alkylation/Hemiacetalization for Facile Construction of the Spiroketal Skeleton of Acortatarin Alkaloids with Therapeutic Potentiality in Diabetic Nephropathy显示文摘The concise building of the spiroketal core of acortatarin-type alkaloids as potential therapeutic agents in diabetic nephropathy was established in four steps,through a tandem N-alkylation/hemiacetalization between pyrrole units and the corresponding halo alcohols generated by convenient halomethylation of chiral lactones from natural aldoses. | Pei Cao Zhen-Jie Li Wen-Wu Sun Shashwat Malhotra Yuan-Liang Ma Bin Wu Virinder S.Parmar | 2015 | Natural Products and Bioprospecting2015，5，1： | 0 |
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