203 citations · 278 across the 25 of their papers we have counts for
28 papers
BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials
Laura Zichi, Gil Harari, Chuin Wei Tan +6
Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expens…
RS-CIDER: A non-local machine learning model for approximating screened hybrid functionals
Zhuotao Jin, Mohamed S. Abdallah, Boris Kozinsky +1
Screened hybrid functionals such as HSE06 improve the description of band gaps, charge localization, and redox energetics relative to semilocal approximations, but their explicit H…
Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
Mohamed S. Abdallah, Zhuotao Jin, Boris Kozinsky +1
The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learni…
DynaCrys: Crystal Generation with Dynamic Space-Group Diffusion
Zhuotao Jin, Xiaoyun Wang, Nicholas Brawand +5
The search for new crystalline materials spans an enormous compositional and structural space. Generating candidates in this space requires jointly modeling discrete crystallograph…
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large a…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid electrolytes CsHPO and CsHSO, the origin of fast proton conduction has long been debated, attributed either to local proton hopping or to polyanion rotati…
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…
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…
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…
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…
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…
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…
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…