170 citations · 818 across the 28 of their papers we have counts for
29 papers
AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution
Ken J. Jenewein, Faezeh Habib Zadeh, Xiaoxiao Wang +19
Catalyzing acidic oxygen evolution at the proton-exchange-membrane water electrolysis (PEMWE) anode relies almost entirely on iridium or ruthenium, drawn from concentrated supply c…
Thermal Equilibrium Vacancy Concentration in an Alloy with Chemical Short-Range Order
Hao Tang, Hoje Chun, Rafael Gomez-Bombarelli +2
The equilibrium vacancy concentration in multi-principal element alloys remains a controversial and nontrivial subject, primarily because of chemical complexity and chemical short-…
DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
Elton Pan, Soonhyoung Kwon, Sulin Liu +9
The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and…
Deep Learning of Mean First Passage Time Scape: Chemical Short-Range Order and Kinetics of Diffusive Relaxation
Hoje Chun, Hao Tang, Bin Xing +2
Processes slow compared to atomic vibrations pose significant challenges in atomistic simulations, particularly for phenomena such as diffusive relaxations and phase transitions, w…
Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
Akshay Subramanian, James Damewood, Juno Nam +3
Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic…
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gomez-Bombarelli
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (ML…
Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
Soojung Yang, Juno Nam, Johannes C. B. Dietschreit +1
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a co…
Learning a reactive potential for silica-water through uncertainty attribution
Swagata Roy, Johannes P. Dürholt, Thomas S. Asche +2
The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite ca…
Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with machine learning
Jaclyn R. Lunger, Jessica Karaguesian, Hoje Chun +6
Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to prod…
Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics
Reisel Millan, Estefania Bello-Jurado, Manual Moliner +2
Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of framework composition on transport is not fully understood. Ab initio molecu…
Machine-learning-accelerated simulations to enable automatic surface reconstruction
Xiaochen Du, James K. Damewood, Jaclyn R. Lunger +4
Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. By combining energies from electronic structure with statistical mechanics, a…
Entropy and Energy Profiles of Chemical Reactions
Johannes C. B. Dietschreit, Dennis J. Diestler, Rafael Gómez-Bombarelli
The description of chemical processes at the molecular level is often facilitated by use of reaction coordinates, or collective variables (CVs). The CV measures the progress of the…
Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling
Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli
Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and bio…
Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Šípka, Johannes C. B. Dietschreit, Lukáš Grajciar +1
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically ch…
Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS
Gavin Winter, Rafael Gómez-Bombarelli
LiGe(PS) (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for con…
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu, Zhenghao Wu, Wujie Wang +4
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…
Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
Sheng Gong, Tian Xie, Yang Shao-Horn +2
Historically, materials informatics has relied on human-designed descriptors of materials structures. In recent years, graph neural networks (GNNs) have been proposed for learning…
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
Simon Axelrod, Eugene Shakhnovich, Rafael Gomez-Bombarelli
Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life…
From Free-Energy Profiles to Activation Free Energies
Johannes C. B. Dietschreit, Dennis J. Diestler, Andreas Hulm +2
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) that discriminates between R and P, one can…
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli
Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable s…
Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning
James Damewood, Daniel Schwalbe-Koda, Rafael Gomez-Bombarelli
Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive…
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu, Wujie Wang, Shitong Luo +4
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two ste…
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce t…
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
Tian Xie, Arthur France-Lanord, Yanming Wang +8
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant c…