Publications

In Preparation
Nicola Molinari, Yu Xie, Ian Leifer, and Boris Kozinsky. In Preparation. “Spectral de-noising for calculating correlated transport properties”.
Submitted
Jin Soo Lim, Jonathan Vandermause, Matthijs A. Van Spronsen, Albert Musaelian, Christopher R. O'Connor, Tobias Egle, Yu S. Xie, Lixin Sun, Nicola Molinari, Jacob Florian, Kaining Duanmu, Robert Madix, Philippe Sautet, Cynthia M. Friend, and Boris Kozinsky. Submitted. “Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics”. Publisher's Version
2020
Jonathan Vandermause, Steven B Torrisi, Simon Batzner, Yu Xie, Lixin Sun, Alexie M Kolpak, and Boris Kozinsky. 3/18/2020. “On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events.” NPJ Comput Mater. Publisher's VersionAbstract
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.
flare_npj.pdf
Nicola Molinari and Boris Kozinsky. 3/12/2020. “Chelation-induced reversal of negative cation transference number in ionic-liquid electrolytes.” The Journal of Physical Chemistry. Publisher's VersionAbstract
Strong anion–cation interaction in lithium-salt/ionic liquid electrolytes leads to ionic association that decreases the Li transference number, even causing it to be negative. We show that these interactions can be greatly reduced by adding cyclic ethylene oxide molecules, and we quantitatively examine the effect using rigorous multispecies concentrated solution theory coupled with molecular dynamics simulations. The added molecules, primarily lithium ionophore V also known as 12-crown-4, have high affinity to lithium, therefore disrupting the lithium cation–anion coupling, resulting in a significantly improved transference number. First, we investigate the lithium–anion spatial correlation by studying their clusters and show that the 12-crown-4 ether allows the formation of previously nonexisting positively charged lithium-containing complexes. We then prove that the chelators actively compete with the anion to coordinate lithium ions by showing that the persistence-over-time of a given anion coordination cage decreases when ionophore molecules are added to the system. Last, we report an increase in the lithium transference number for a variety of chemistries as a function of added 12-crown-4 (and another ionophore, 18-crown-6) molecules, and even positive values can be reached. Our results provide a foundation for new design and optimization strategies to reverse the sign of and increase the transference number in highly correlated concentrated electrolytes.
Chelation-Induced_Reversal_of_Negative_Cation_Transference_Number_in_Ionic_Liquid_Electrolytes.pdf
2019
Jonathan P Mailoa, Mordechai Kornbluth, Simon L Batzner, Georgy Samsonidze, Stephen T Lam, Jonathan Vandermause, Chris Ablitt, Nicola Molinari, and Boris Kozinsky. 9/30/2019. “A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems.” Nature Machine Intelligence, 1, Pp. 471-479. Publisher's VersionAbstract
Neural network force field (NNFF) is a method for performing regression on atomic structure–force relationships, bypassing the expensive quantum mechanics calculations that prevent the execution of long ab initio quality molecular dynamics (MD) simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and network-feature spatial derivatives, which are computationally expensive. Here, we show a staggered NNFF architecture that exploits both rotation-invariant and -covariant features to directly predict atomic force vectors without using spatial derivatives, and we demonstrate 2.2× NNFF–MD acceleration over a state-of-the-art C++ engine using a Python engine. This fast architecture enables us to develop NNFF for complex ternary- and quaternary-element extended systems composed of long polymer chains, amorphous oxide and surface chemical reactions. The rotation-invariant–covariant architecture described here can also directly predict complex covariant vector outputs from local environments, in other domains beyond computational material science.
A_fast_neural_network_approach_for_direct_covariant_forces_prediction_in_complex_multi-element_extended_systems.pdf
Mordechai Kornbluth, Jonathan Mailoa, Boris Kozinsky, Georgy Samsonidze, and John F Christensen. 2019. “Amorphous LiF as an Artificial SEI Layer for Lithium Batteries.” United States of America. us20190036120a1.pdf
Jin Soo Lim, Nicola Molinari, Kaining Duanmu, Philippe Sautet, and Boris Kozinsky. 2019. “Automated detection and characterization of surface restructuring events in bimetallic catalysts.” The Journal of Physical Chemistry C. lim_et_al._-_2019_-_automated_detection_and_characterization_of_surface_restructuring_events_in_bimetallic_catalysts.pdf
Paul Le Floch, Nicola Molinari, Kewang Nan, Shuwen Zhang, Boris Kozinsky, Zhigang Suo, and Jia Liu. 2019. “Fundamental limits to the electrochemical impedance stability of dielectric elastomers in bioelectronics.” Nano letters. lefloch.pdf
Nicola Molinari, Jonathan Pradana Mailoa, and Boris Kozinsky. 2019. “General Trend of Negative Li Effective Charge in Ionic Liquid Electrolytes.” The journal of physical chemistry letters. molinari_mailoa_kozinsky_-_2019_-_general_trend_of_a_negative_li_effective_charge_in_ionic_liquid_electrolytes.pdf
Malar Azagarsamy, Kulandaivelu Sivanandan, Hany Basam Eitouni, Jonathan P Mailoa, Georgy Samsonidze, Karim R Gadelrab, and Boris Kozinsky. 2019. “Poly (anhydride)-based polymer electrolytes for high voltage lithium ion batteries.” United States of America. us20190131653a1.pdf
Malar Azagarsamy, Kulandaivelu Sivanandan, Hany Basam Eitouni, Jonathan P Mailoa, Georgy Samsonidze, Karim R Gadelrab, and Boris Kozinsky. 2019. “Poly (ketone)-based polymer electrolytes for high voltage lithium ion batteries.” United States of America. us20190190067a1.pdf
Daehyun Wee, Jeeyoung Kim, Semi Bang, Georgy Samsonidze, and Boris Kozinsky. 2019. “Quantification of uncertainties in thermoelectric properties of materials from a first-principles prediction method: An approach based on Gaussian process regression.” Physical Review Materials, 3, 3, Pp. 033803. wee_et_al._-_2019_-_quantification_of_uncertainties_in_thermoelectric_properties_of_materials_from_a_first-principles_prediction_method.pdf
Eric R Fadel, Francesco Faglioni, Georgy Samsonidze, Nicola Molinari, Boris V Merinov, William A Goddard III, Jeffrey C Grossman, Jonathan P Mailoa, and Boris Kozinsky. 2019. “Role of solvent-anion charge transfer in oxidative degradation of battery electrolytes.” Nature communications, 10. fadel_et_al._-_2019_-_role_of_solvent-anion_charge_transfer_in_oxidative_degradation_of_battery_electrolytes.pdf
Nicola Molinari, Jonathan P Mailoa, Nathan Craig, Jake Christensen, and Boris Kozinsky. 2019. “Transport anomalies emerging from strong correlation in ionic liquid electrolytes.” Journal of Power Sources, 428, Pp. 27–36. molinari_et_al._-_2019_-_transport_anomalies_emerging_from_strong_correlation_in_ionic_liquid_electrolytes.pdf
Leonid Kahle, Albert Musaelian, Nicola Marzari, and Boris Kozinsky. 2019. “Unsupervised landmark analysis for jump detection in molecular dynamics simulations.” Physical Review Materials, 3, 5, Pp. 055404. kahle_et_al._-_2019_-_unsupervised_landmark_analysis_for_jump_detection_in_molecular_dynamics_simulations.pdf
2018
Georgy Samsonidze and Boris Kozinsky. 2018. “Accelerated Screening of Thermoelectric Materials by First-Principles Computations of Electron–Phonon Scattering.” Advanced Energy Materials, 8, 20, Pp. 1800246. samsonidze_et_al-2018-advanced_energy_materials.pdf
Daniel J Brooks, Boris V Merinov, William A Goddard III, Boris Kozinsky, and Jonathan Mailoa. 2018. “Atomistic Description of Ionic Diffusion in PEO–LiTFSI: Effect of Temperature, Molecular Weight, and Ionic Concentration.” Macromolecules, 51, 21, Pp. 8987–8995. brooks_et_al._-_2018_-_atomistic_description_of_ionic_diffusion_in_peo-litfsi_effect_of_temperature_molecular_weight_and_ionic_conce.pdf
John F Christensen, Boris Kozinsky, and Sondra Hellstrom. 2018. “Coated Cathode Active Material for Engineered Solid-State Battery Interfaces.” United States of America. us20180083315a1.pdf
Nicola Molinari, Jonathan P Mailoa, and Boris Kozinsky. 2018. “Effect of Salt Concentration on Ion Clustering and Transport in Polymer Solid Electrolytes: A Molecular Dynamics Study of PEO–LiTFSI.” Chemistry of Materials, 30, 18, Pp. 6298–6306. molinari_et_al._-_2018_-_effect_of_salt_concentration_on_ion_clustering_and_transport_in_polymer_solid_electrolytes_a_molecular_dynami.pdf
Francesco Faglioni, Boris V Merinov, William A Goddard, and Boris Kozinsky. 2018. “Factors affecting cyclic durability of all-solid-state lithium batteries using poly (ethylene oxide)-based polymer electrolytes and recommendations to achieve improved performance.” Physical Chemistry Chemical Physics, 20, 41, Pp. 26098–26104. faglioni_et_al._-_2018_-_factors_affecting_cyclic_durability_of_all-solid-state_lithium_batteries_using_polyethylene_oxide-based_polym.pdf

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