Steven B. Torrisi

发表

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

Santosh K. Suram, Brian A. Rohr, Yang Ha, 2020, npj Computational Materials.

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Jonathan Vandermause, Simon Batzner, Alexie M. Kolpak, 2020, npj Computational Materials.

Automated training of machine learned potentials with Bayesian active learning

Jonathan Vandermause, Simon Batzner, Alexie M. Kolpak, 2020 .

Two-dimensional forms of robust CO2 reduction photocatalysts

Steven B. Torrisi, Joseph H. Montoya, K. Persson, 2019, npj 2D Materials and Applications.

On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events

Jonathan Vandermause, Simon Batzner, Alexie M. Kolpak, 2019 .

Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates interchromosomal movement

Steven B. Torrisi, D. G. Brickner, J. Brickner, 2021, bioRxiv.

Relaxation and domain formation in incommensurate two-dimensional heterostructures

Steven B. Torrisi, E. Kaxiras, M. Luskin, 2018, Physical Review B.

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

Brian A. Rohr, Matthew R. Carbone, Steven B. Torrisi, 2020, npj Computational Materials.

New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research

Steven B. Torrisi, Joseph H. Montoya, Hao Li, 2022, Energy & Environmental Science.

Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates inter-chromosomal movement

Steven B. Torrisi, D. G. Brickner, J. Brickner, 2021, eLife.

Agents for sequential learning using multiple-fidelity data

Steven B. Torrisi, Joseph H. Montoya, Muratahan Aykol, 2022, Scientific Reports.

Toward autonomous materials research: Recent progress and future challenges

Patrick K. Herring, Steven B. Torrisi, Joseph H. Montoya, 2022, Applied Physics Reviews.

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Simon L. Batzner, Steven B. Torrisi, B. Kozinsky, 2019, npj Computational Materials.

Decoding reactive structures in dilute alloy catalysts

Steven B. Torrisi, J. Aizenberg, A. Frenkel, 2022, Nature communications.

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

Simon L. Batzner, Steven B. Torrisi, Robert Bosch Llc, 2023, 2302.12993.

Effects of structural distortions on the electronic structure of T -type transition metal dichalcogenides

Steven B. Torrisi, E. Kaxiras, S. Fang, 2020, 2003.10473.

PHYSICAL REVIEW B 102, 045128 (2020) Effects of structural distortions on the electronic structure of T -type transition metal dichalcogenides

Steven B. Torrisi, E. Kaxiras, S. Fang, 2020 .

Electronic structure calculations of twisted multi-layer graphene superlattices

Steven B. Torrisi, E. Kaxiras, G. Tritsaris, 2020, 2D Materials.

Early Prediction of Ion Transport Properties in Solid Polymer Electrolytes Using Machine Learning and System Behavior-Based Descriptors of Molecular Dynamics Simulations

Steven B. Torrisi, B. Storey, A. Khajeh, 2023, Macromolecules.

Materials cartography: A forward-looking perspective on materials representation and devising better maps

Steven B. Torrisi, Joseph H. Montoya, M. Bazant, 2023, APL Machine Learning.

Oxygen Vacancy Formation Energy in Metal Oxides: High-Throughput Computational Studies and Machine-Learning Predictions

Steven B. Torrisi, B. Baldassarri, Abhijith M. Gopakumar, 2023, Chemistry of Materials.

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

Simon L. Batzner, Steven B. Torrisi, B. Kozinsky, 2023, 2302.12993.

High-throughput ab initio design of atomic interfaces using InterMatch

Steven B. Torrisi, A. Pasupathy, C. Dean, 2022, Nature communications.