Publications (76)
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
Cameron J. Owen, Steven B. Torrisi, Yu Xie +6
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…
Charge density and redox potential of LiNiO2 using ab initio diffusion quantum Monte Carlo
Kayahan Saritas, Eric R. Fadel, Boris Kozinsky +1
Electronic structure of layered LiNiO2 has been controversial despite numerous theoretical and experimental reports regarding its nature. We investigate the charge densities, lithi…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…
Theory of Cation Solvation and Ionic Association in Non-Aqueous Solvent Mixtures
Zachary A. H. Goodwin, Michael McEldrew, Boris Kozinsky +1
Conventional lithium-ion batteries, and many next-generation technologies, rely on organic electrolytes with multiple solvents to achieve the desired physicochemical and interfacia…
Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential
Chang-Ti Chou, Menghang Wang, Chao Yang +4
Predicting atomic-scale interfacial structures remains a central challenge in materials science due to their structural complexity and the difficulty of direct comparison between c…
Microscopic picture of paraelectric perovskites from structural prototypes
Michele Kotiuga, Samed Halilov, Boris Kozinsky +3
We show with first-principles molecular dynamics the persistence of intrinsic Ti off-centerings for BaTiO in its cubic paraelectric phase. Intriguingly, the…
Exploring Charge Density Waves in two-dimensional NbSe2 with Machine Learning
Norma Rivano, Francesco Libbi, Chuin Wei Tan +8
Niobium diselenide (NbSe) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, r…
Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC
Yu Xie, Jonathan Vandermause, Senja Ramakers +3
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.…
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
Taufeq Mohammed Razakh, Thomas Linker, Ye Luo +12
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electro…
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Albert Musaelian, Simon Batzner, Anders Johansson +4
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences.…
Atomistic simulations of out-of-equilibrium quantum nuclear dynamics
Francesco Libbi, Anders Johansson, Lorenzo Monacelli +1
The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these e…
Accelerated screening of thermoelectric materials by first-principles computations of electron-phonon scattering
Georgy Samsonidze, Boris Kozinsky
Recent discovery of new materials for thermoelectric energy conversion is enabled by efficient prediction of materials' performance from first-principles, without empirically fitte…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…
Spectral denoising for unsupervised analysis of correlated ionic transport
Nicola Molinari, Yu Xie, Ian Leifer +3
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement…
Stability, mechanisms and kinetics of emergence of Au surface reconstructions using Bayesian force fields
Cameron J. Owen, Yu Xie, Anders Johansson +2
Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformation…
Electron-Phonon Interactions and the Intrinsic Electrical Resistivity of Graphene
Cheol-Hwan Park, Nicola Bonini, Thibault Sohier +5
We present a first-principles study of the temperature- and density-dependent intrinsic electrical resistivity of graphene. We use density-functional theory and density-functional…
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
Yu Xie, Jonathan Vandermause, Lixin Sun +2
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions…
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Blake R. Duschatko, Xiang Fu, Cameron Owen +4
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temper…
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Albert Zhu, Simon Batzner, Albert Musaelian +1
Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current appro…
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9
First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning…
Towards Sustainable Ultrawide Bandgap Van der Waals Materials: An ab initio Screening Effort
Chuin Wei Tan, Linqiang Xu, Chen Chen Er +6
The sustainable development of next-generation device technology is paramount in the face of climate change and the looming energy crisis. Tremendous efforts have been made in the…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…
Static dielectric properties of carbon nanotubes from first principles
Boris Kozinsky, Nicola Marzari
We characterize the response of isolated single- (SWNT) and multi-wall (MWNT) carbon nanotubes and bundles to static electric fields using first-principles calculations and density…
Surface roughening in nanoparticle catalysts
Cameron J. Owen, Nicholas Marcella, Christopher R. O'Connor +7
Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and ch…
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events
Jonathan Vandermause, Steven B. Torrisi, Simon Batzner +4
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training effic…
Room-temperature decomposition of the ethaline deep eutectic solvent
Julia H. Yang, Amanda Whai Shin Ooi, Zachary A. H. Goodwin +5
Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here…
AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
Sebastiaan. P. Huber, Spyros Zoupanos, Martin Uhrin +19
The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developm…
Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
Yu Xie, Menghang Wang, Senja Ramakers +2
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent ve…
Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
Kyle Bystrom, Stefano Falletta, Boris Kozinsky
The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT…
Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
The paper presents fast and accurate equivariant machine‑learned interatomic potentials (NequIP and Allegro) as foundation models that scale to ultra‑large datasets while maintaini…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…
Unraveling the Catalytic Effect of Hydrogen Adsorption on Pt Nanoparticle Shape-Change
Cameron J. Owen, Nicholas Marcella, Yu Xie +4
The activity of metal catalysts depends sensitively on dynamic structural changes that occur during operating conditions. The mechanistic understanding underlying such transformati…
Electrostatics in Periodic Boundary Conditions and Real-space Corrections
Ismaila Dabo, Boris Kozinsky, Nicholas E. Singh-Miller +1
We address periodic-image errors arising from the use of periodic boundary conditions to describe systems that do not exhibit full three-dimensional periodicity. The difference bet…
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints
Kyle Bystrom, Boris Kozinsky
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals develop…
Effects of sublattice symmetry and frustration on ionic transport in garnet solid electrolytes
Boris Kozinsky, Sneha A. Akhade, Pierre Hirel +5
We use rigorous group-theoretic techniques and molecular dynamics to investigate the connection between structural symmetry and ionic conductivity in the garnet family of solid Li-…
High-Throughput Screening of Perovskite Alloys for Piezoelectric Performance and Formability
Rickard Armiento, Boris Kozinsky, Geoffroy Hautier +2
We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phas…
Electron-phonon-averaged approximation for first-principles computations of electron relaxation times and transport properties in semiconductor materials
Georgy Samsonidze, Boris Kozinsky
We present a simple and efficient approximation to the electron-phonon scattering rate suitable for high-throughput screening of candidate materials for thermoelectric devices, bas…
Uncertainty Driven Active Learning of Coarse Grained Free Energy Models
Blake R. Duschatko, Jonathan Vandermause, Nicola Molinari +1
Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are ap…
Charge ordering and hopping in a triangular array of quantum dots
L. S. Levitov, Boris Kozinsky
We demonstrate a mapping between the problem of charge ordering in a triangular array of quantum dots and a frustrated Ising spin model. Charge correlation in the low temperature s…
Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics
Jonathan Vandermause, Yu Xie, Jin Soo Lim +2
Accurate modeling of chemically reactive systems has traditionally relied on either expensive ab initio approaches or flexible bond-order force fields such as ReaxFF that require c…
Unsupervised landmark analysis for jump detection in molecular dynamics simulations
Leonid Kahle, Albert Musaelian, Nicola Marzari +1
Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of t…
Alternate cleavage structure and electronic inhomogeneity in Ca-doped YBaCuO
Larissa B. Little, Jennifer Coulter, Ruizhe Kang +10
YBaCuO (YBCO) has favorable macroscopic superconducting properties of up to 93 K and up to 150 T. However, its nanoscale electronic structu…
Interband tunneling effects on materials transport properties using the first principles Wigner distribution
Andrea Cepellotti, Boris Kozinsky
Electronic transport in narrow gap semiconductors is characterized by spontaneous vertical transitions between carriers in the valence and conduction bands, a phenomenon also known…
Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4
Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and datasets have led to increasingly acc…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…
Nonlocal Machine-Learned Exchange Functional for Molecules and Solids
Kyle Bystrom, Boris Kozinsky
The design of better exchange-correlation functionals for Density Functional Theory (DFT) is a central challenge of modern electronic structure theory. However, current development…
Engineering ideal helical topological networks in stanene via Zn decoration
Jennifer Coulter, Mark R. Hirsbrunner, Oleg Dubinkin +2
The xene family of topological insulators plays a key role in many proposals for topological electronic, spintronic, and valleytronic devices. These proposals rely on applying loca…
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic…
Coupled reaction and diffusion governing interface evolution in solid-state batteries
Jingxuan Ding, Laura Zichi, Matteo Carli +4
Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid…
Unified Differentiable Learning of Electric Response
Stefano Falletta, Andrea Cepellotti, Anders Johansson +4
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning
Anders Johansson, Yu Xie, Cameron J. Owen +4
Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H/Pt(111) using the…
Electronic, vibrational and transport properties of pnictogen substituted ternary skutterudites
Dmitri Volja, Boris Kozinsky, An Li +3
First principles calculations are used to investigate electronic band structure and vibrational spectra of pnictogen substituted ternary skutterudites. We compare the results with…
Atomistic evolution of active sites in multi-component heterogeneous catalysts
Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Albert Musaelian, Anders Johansson, Simon Batzner +1
This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combinat…
Insights and challenges of applying the method to transition metal oxides
Georgy Samsonidze, Cheol-Hwan Park, Boris Kozinsky
The ab initio method is considered as the most accurate approach for calculating the band gaps of semiconductors and insulators. Yet its application to transition metal oxides…
Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields
Cameron J. Owen, Amirhossein D. Naghdi, Anders Johansson +3
Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions,…
Salt-in-Ionic-Liquid Electrolytes: Ion Network Formation and Negative Effective Charges of Alkali Metal Cations
Michael McEldrew, Zachary A. H. Goodwin, Nicola Molinari +3
Salt-in-ionic liquid electrolytes have attracted significant attention as potential electrolytes for next generation batteries largely due to their safety enhancements over typical…
Laser-driven ferroelectricity in via quantum fluctuation quenching
Francesco Libbi, Lorenzo Monacelli, Boris Kozinsky
Similar to other perovskites in its family, exhibits a significant softening of the ferroelectric mode with decreasing temperature, a behavior that typically h…
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner +4
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…
Anomalous thermoelectric transport phenomena from interband electron-phonon scattering
Natalya S. Fedorova, Andrea Cepellotti, Boris Kozinsky
The Seebeck coefficient and electrical conductivity are two critical quantities to optimize simultaneously in designing thermoelectric materials, and they are determined by the dyn…
Secular Evolution of Hierarchical Triple Star Systems
Eric B. Ford, Boris Kozinsky, Frederic A. Rasio
We derive octupole-level secular perturbation equations for hierarchical triple systems, using classical Hamiltonian perturbation techniques. By extending previous work done to lea…
A Recipe for Charge Density Prediction
Xiang Fu, Andrew Rosen, Kyle Bystrom +5
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in si…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause +3
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural n…
Equivalence of charged and neutral density functional formulations for correcting the many-body self-interaction of polarons
Stefano Falletta, Jennifer Coulter, Joel B. Varley +4
The electron self-interaction problem in density functional theory affects the accurate modeling of polarons, particularly their localization and formation energy. Charged and neut…
BoltzWann: A code for the evaluation of thermoelectric and electronic transport properties with a maximally-localized Wannier functions basis
Giovanni Pizzi, Dmitri Volja, Boris Kozinsky +2
We present a new code to evaluate thermoelectric and electronic transport properties of extended systems with a maximally-localized Wannier function basis set. The semiclassical Bo…
Quantum theory of nonlinear phononics
Francesco Libbi, Boris Kozinsky
The recent capability to use THz pulses to control the nuclear quantum degrees of freedom in crystals has opened promising avenues for the advanced manipulation of material propert…
Multitask machine learning of collective variables for enhanced sampling of rare events
Lixin Sun, Jonathan Vandermause, Simon Batzner +4
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.…
Can LLMs extract scientific consensus? A case study in high-temperature superconductivity
Mouyang Cheng, Wenhao He, Zhuotao Jin +9
Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. Whi…
Phoebe: a High-Performance Framework for Solving Phonon and Electron Boltzmann Transport Equations
Andrea Cepellotti, Jennifer Coulter, Anders Johansson +2
Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors and energy conversion devices. Computational modeling c…
AiiDA: Automated Interactive Infrastructure and Database for Computational Science
Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini +2
Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still…
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner +5
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents th…
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
Jonathan Vandermause, Anders Johansson, Yucong Miao +2
Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of biomedical and engineering devices, but direct molecular dynamics simulations of the martensitic B19' -…
Electron-phonon drag enhancement of transport properties from fully coupled \textit{ab initio} Boltzmann formalism
Nakib H. Protik, Boris Kozinsky
We present a combined treatment of the non-equilibrium dynamics and transport of electrons and phonons by carrying out \textit{ab initio} calculations of the fully coupled electron…
Ultrafast quantum dynamics in under impulsive THz radiation
Francesco Libbi, Anders Johansson, Boris Kozinsky +1
Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. Despite significant advances, the microscopic processes governing the…