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| 1 | Active learning for accelerated design of layered materials显示文摘Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices.Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties,such as electronic band structure and thermal transport coefficients.However,screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources.Moreover,the functional dependence of material properties on the structures is often complicated,making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection.Here,we present a Gaussian process regression model,which predicts material properties of an input hetero-structure,as well as an active learning model based on Bayesian optimization,which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations.The electronic band gap,conduction/valence band dispersions,and thermoelectric performance are used as representative material properties for prediction and optimization.The Materials Project platform is used for electronic structure computation,while the BoltzTraP code is used to compute thermoelectric properties.Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties.The models can be used for predictions with respect to any material property and our software,including data preparation code based on the Python Materials Genomics(PyMatGen)library as well as python-based machine learning code,is available open source. | Lindsay Bassman Pankaj Rajak Rajiv K.Kalia Aiichiro Nakano Fei Sha Jifeng Sun David J.Singh Muratahan Aykol Patrick Huck Kristin Persson Priya Vashishta | 2018 | npj Computational Materials2018，，1： | 10 |
| 2 | Multiobjective genetic training and uncertainty quantification of reactive force fields显示文摘The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes.ReaxFF parameters are commonly trained to fit a predefined set of quantummechanical data,but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions.Here,we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest.ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly,where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification.Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications.The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO_(3) by H_(2)S precursor,which is an essential reaction step for chemical vapor deposition synthesis of MoS_(2) layers.Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes,which quantitatively reproduces quantum molecular dynamics simulations while providing error bars. | Ankit Mishra Sungwook Hong Pankaj Rajak Chunyang Sheng Ken-ichi Nomura Rajiv K.Kalia Aiichiro Nakano Priya Vashishta | 2018 | npj Computational Materials2018，，1： | 3 |
| 3 | Frequency-dependent dielectric constant prediction of polymers using machine learning显示文摘The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature applications.To aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of magnitude.Our model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm. | Lihua Chen Chiho Kim Rohit Batra Jordan P.Lightstone Chao Wu Zongze Li Ajinkya A.Deshmukh Yifei Wang Huan D.Tran Priya Vashishta Gregory A.Sotzing Yang Cao Rampi Ramprasad | 2020 | npj Computational Materials2020，，1： | 2 |
| 4 | Effect of supplement boron on nutrient utilization mineral status and blood biochemical constituents in lambs fed high fluorides diet显示文摘 | Vashishta SN Kapoor V Yadav PS | 1997 | Fluoride1997，301，： | 1 |
| 5 | Immuno- logical effects of yeast-and mushroom-derived beta-glucans 显示文摘 | Vetvicka V Vashishta A Saraswat-Ohri S | 2008 | J Med Food2008，11，4： | 1 |
| 6 | Structural transformation,intermediate-range order,and dynamical behavior of SiO2 glass at high pressures显示文摘 | Jin W Kalia R K Vashishta P | | 0，，19： | 1 |
| 7 | Nuclear factor of activated T-cells isoform c4 (NFATc4/NFAT3) as a mediator of antiapoptotic transcription in NMDA receptor-stimulated cortical neurons 显示文摘 | Vashishta A Habas A Pruunsild P | 2009 | J Neurosci2009，29，15： | 1 |
| 8 | Effoct of supplement boron on nutrient utilization mineral status and blood biochemical constituents in lambs fed high fluorides diet显示文摘 | Vashishta SN Kapoor V Yadav PS | 1997 | fluoride1997，30，1： | 1 |
| 9 | Biological prop- erties of (1-3)-D-glucan based synthetic oligosaccharides显示文摘 | Saraswat-Ohri S Vashishta A Vetvicka V | 2011 | J Med Food2011，14，4： | 1 |
| 10 | Intraosseous schwan- noma of the frontal bone显示文摘 | Goyal R Saikia U N Vashishta R K | 2008 | Orthopedics2008，31，3： | 1 |
| 11 | Depletion of procathep sin D gene expression by RNA interference: a potential therapeutic target for breast cancer 显示文摘 | Ohris S Vashishta A Proctor M | 2007 | Cancer Biol Ther2007，6，7： | 1 |
| 12 | Structural transitions in superionic conductors显示文摘 | PARRINELLO M RAHMAN A VASHISHTA P | 1983 | Phys Rev Lett1983，50，： | 1 |
| 13 | Inelastic neutron scattering and dynamics of ions in the super-ionic conductor Ag2S 显示文摘 | EBBSJO I VASHISHTA P DEJUS R | 1987 | J Phys C Solid State Phys1987，20，： | 1 |
| 14 | Effoct of supplement boron on nutrient utilization mineral status and blood biochemical constituents in lambs fed high fluorides diet 显示文摘 | Vashishta SN Kapoor V Yadav PS | 1997 | fluoride1997，30，1： | 1 |
| 15 | Congenital hepatic fibrosis in Indian显示文摘 | Poddar U Thapa BR Vashishta PK | 1999 | J Gastroenterrol Hepatol1999，14，： | 1 |
| 16 | Suppression of dendritic cell-mediated responses by genes in calcium and cysteine protease pathways during Mycobacterium tuberculosis infection显示文摘 | Singhal J Agrawal N Vashishta M | 2012 | J Biol Chem2012，287，11： | 1 |
| 17 | Pleiotropic effects of cathepsin D显示文摘 | Vashishta A Ohri SS Vetvicka V | 2009 | Endocr Metab Immune Disord Drug Targets2009，9，4： | 1 |
| 18 | Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials显示文摘Mechanical behavior of 2D materials such as MoS_(2) can be tuned by the ancient art of kirigami.Experiments and atomistic simulations show that 2D materials can be stretched more than 50%by strategic insertion of cuts.However,designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts.We use reinforcement learning(RL)to generate a wide range of highly stretchable MoS_(2) kirigami structures.The RL agent is trained by a small fraction(1.45%)of molecular dynamics simulation data,randomly sampled from a search space of over 4 million candidates for MoS_(2)kirigami structures with 6 cuts.After training,the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%,but also gains mechanistic insight to propose highly stretchable(above 40%)kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions. | Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta | 2021 | npj Computational Materials2021，，1： | 1 |
| 19 | Multimillion Atom Molecular Dynamics Simulations of Nanostructures on Parallel Computers显示文摘 | VASHISHTA P KALIA R K NAKANO A | 2003 | Journal of Nanoparticle Research2003，5，12： | 1 |
| 20 | Structural transformation, amorphization, and fracture in nanowires: A multimillion-atom molecular dynamics study显示文摘 | WALSH P LI W KALIA R K NAKANO A VASHISHTA P SAINI S | 2001 | Appl Phys Lett2001，78，21： | 1 |
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