Electronic Excited States from Physically Constrained Machine Learning DOI Creative Commons
Edoardo Cignoni, Divya Suman, Jigyasa Nigam

et al.

ACS Central Science, Journal Year: 2024, Volume and Issue: 10(3), P. 637 - 648

Published: Feb. 29, 2024

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly predict the desired properties or combined explicitly with physically grounded operations. We present an example integrated modeling approach in which symmetry-adapted ML model effective Hamiltonian trained reproduce electronic excitations from quantum-mechanical calculation. The resulting can make predictions for molecules that much larger and more complex than those on it allows dramatic computational savings by indirectly targeting outputs well-converged while using parametrization corresponding minimal atom-centered basis. These results emphasize merits intertwining data-driven physical approximations, improving transferability interpretability models without affecting their accuracy efficiency providing blueprint developing ML-augmented methods.

Language: Английский

Gaussian Process Regression for Materials and Molecules DOI Creative Commons
Volker L. Deringer, Albert P. Bartók, Noam Bernstein

et al.

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10073 - 10141

Published: Aug. 16, 2021

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on atomistic properties: particular, construction interatomic potentials, or force fields, Approximation Potential (GAP) framework; beyond this, we also discuss fitting arbitrary scalar, vectorial, tensorial quantities. Methodological aspects reference data generation, representation, regression, as well question how a data-driven model may be validated, are reviewed critically discussed. A survey applications variety research questions chemistry illustrates rapid growth field. vision outlined for development methodology years come.

Language: Английский

Citations

776
Perspective on integrating machine learning into computational chemistry and materials science DOI Open Access
Julia Westermayr, Michael Gastegger, Kristof T. Schütt

et al.

The Journal of Chemical Physics, Journal Year: 2021, Volume and Issue: 154(23)

Published: June 21, 2021

Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established the construction high-dimensional interatomic potentials. Not a day goes by without another proof principle published on how can represent predict quantum mechanical properties-be they observable, such as polarizabilities, or not, atomic charges. As is becoming pervasive simulation, we provide an overview atomistic computational modeling transformed incorporation approaches. From perspective practitioner field, assess common workflows to structure, dynamics, spectroscopy affected ML. Finally, discuss tighter lasting integration with chemistry materials science be achieved what it will mean for research practice, software development, postgraduate training.

Language: Английский

Citations

168
SELFIES and the future of molecular string representations DOI Creative Commons
Mario Krenn, Qianxiang Ai, Senja Barthel

et al.

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

Citations

156
Choosing the right molecular machine learning potential DOI Creative Commons
Max Pinheiro, Fuchun Ge, Nicolas Ferré

et al.

Chemical Science, Journal Year: 2021, Volume and Issue: 12(43), P. 14396 - 14413

Published: Jan. 1, 2021

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at atomistic level and yield such important observables as reaction rates spectra. Machine learning potentials promise to significantly reduce computational cost hence enable otherwise unfeasible simulations. However, surging number begs question which one choose or whether we still need develop yet another one. Here, address this by evaluating performance popular machine in terms accuracy cost. In addition, deliver structured information for non-specialists guide them through maze acronyms, recognize each potential's main features, judge what they could expect from

Language: Английский

Citations

139
Hole utilization in solar hydrogen production DOI
Mohammad Ziaur Rahman, Tomas Edvinsson, Jorge Gascón

et al.

Nature Reviews Chemistry, Journal Year: 2022, Volume and Issue: 6(4), P. 243 - 258

Published: Feb. 2, 2022

Language: Английский

Citations

106
Extending machine learning beyond interatomic potentials for predicting molecular properties DOI
Nikita Fedik, R.I. Zubatyuk, Maksim Kulichenko

et al.

Nature Reviews Chemistry, Journal Year: 2022, Volume and Issue: 6(9), P. 653 - 672

Published: Aug. 25, 2022

Language: Английский

Citations

90
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction DOI Creative Commons
Jin Li, Naiteng Wu, Jian Zhang

et al.

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

Citations

74
Nanocrystal Assemblies: Current Advances and Open Problems DOI
Carlos L. Bassani, Greg van Anders, Uri Banin

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(23), P. 14791 - 14840

Published: May 30, 2024

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and current advances open challenges fundamental science developments applications. Nanocrystal assemblies are inherently multiscale, generation revolutionary material properties requires a precise understanding relationship between structure function, former being determined by classical effects latter often quantum effects. With an emphasis on theory computation, we discuss that hamper assembly strategies what extent nanocrystal represent thermodynamic equilibrium or kinetically trapped metastable states. also examine dynamic optimization protocols. Finally, promising functions examples their realization with assemblies.

Language: Английский

Citations

31
Artificial intelligence-enhanced quantum chemical method with broad applicability DOI Creative Commons
Peikun Zheng, R.I. Zubatyuk, Wei Wu

et al.

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Dec. 2, 2021

Abstract High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a extent by exploiting advances in artificial intelligence (AI). Here we introduce general-purpose, highly transferable intelligence–quantum method 1 (AIQM1). It approaches accuracy gold-standard coupled cluster QM with high speed approximate low-level semiempirical methods neutral, closed-shell species ground state. AIQM1 provide ground-state energies diverse organic compounds as well geometries even challenging systems such large conjugated (fullerene C 60 ) close experiment. This opens an opportunity investigate chemical previously unattainable and demonstrate determining polyyne molecules—the task difficult both experiment theory. Noteworthy, our method’s is also good ions excited-state properties, although neural network part was never fitted these properties.

Language: Английский

Citations

87
Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles DOI Creative Commons
Mario Barbatti, Mattia Bondanza, Rachel Crespo‐Otero

et al.

Journal of Chemical Theory and Computation, Journal Year: 2022, Volume and Issue: 18(11), P. 6851 - 6865

Published: Oct. 4, 2022

Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among most common methodologies in chemistry for photophysical photochemical investigations. This paper describes main features of these methods how they implemented Newton-X. It emphasizes newest developments, including zero-point-energy leakage correction, complex-valued potential energy surfaces, induced by incoherent light, machine-learning potentials, exciton multiple chromophores, supervised unsupervised machine learning techniques. interfaced with several third-party quantum-chemistry programs, spanning a broad electronic structure methods.

Language: Английский

Citations

63