Machine Learning Force Fields

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10142 - 10186

Published: March 11, 2021

In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out reach due to complexity traditional electronic-structure methods. One most promising applications is construction ML-based force fields (FFs), with aim narrow gap between accuracy ab initio methods and efficiency classical FFs. The key idea learn statistical relation chemical structure potential energy without relying on a preconceived notion fixed bonds or knowledge about relevant interactions. Such universal ML approximations are principle only limited by quality quantity reference data used train them. This review gives an overview ML-FFs insights that can be obtained from core concepts underlying described detail, step-by-step guide for constructing testing them scratch given. text concludes discussion challenges remain overcome next generation ML-FFs.

Language: Английский

---

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(12), P. 10970 - 11021

Published: May 16, 2022

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems the future. The liquid electrolyte is one of most important parts battery extremely critical stabilizing electrode–electrolyte interfaces constructing safe long-life-span batteries. Tremendous efforts been devoted developing new solvents, salts, additives, recipes, where molecular dynamics (MD) simulations play an increasingly role exploring structures, physicochemical properties such as ionic conductivity, interfacial reaction mechanisms. This review affords overview applying MD study electrolytes for rechargeable First, fundamentals recent theoretical progress three-class summarized, including classical, ab initio, machine-learning (section 2). Next, application exploration electrolytes, probing bulk structures 3), deriving macroscopic conductivity dielectric constant 4), revealing mechanisms 5), sequentially presented. Finally, general conclusion insightful perspective on current challenges future directions provided. Machine-learning technologies highlighted figure out these challenging issues facing research promote rational design advanced next-generation

Language: Английский

---

Perspective on integrating machine learning into computational chemistry and materials science

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

---

Machine Learning Interatomic Potentials and Long-Range Physics

The Journal of Physical Chemistry A, Journal Year: 2023, Volume and Issue: 127(11), P. 2417 - 2431

Published: Feb. 21, 2023

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, condensed matter, model become reliant on the description short- long-range physical interactions. The latter terms be difficult to incorporate into an MLIP framework. Recent research has produced numerous considerations for nonlocal electrostatic dispersion interactions, leading a large range applications addressed MLIPs. In light this, we present Perspective focused key methodologies being used where presence physics chemistry are crucial describing system properties. strategies covered include MLIPs augmented corrections, electrostatics calculated charges predicted from atomic environment descriptors, use self-consistency message passing iterations propagated information, obtained via equilibration schemes. We aim provide pointed discussion support development learning-based systems contributions only nearsighted deficient.

Language: Английский

---

Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(4), P. 4493 - 4551

Published: Jan. 13, 2022

Operando characterization plays an important role in revealing the structure-property relationships of organic mixed ionic/electronic conductors (OMIECs), enabling direct observation dynamic changes during device operation and thus guiding development new materials. This review focuses on application different operando techniques study OMIECs, highlighting time-dependent bias-dependent structure, composition, morphology information extracted from these techniques. We first illustrate needs, requirements, challenges then provide overview relevant experimental techniques, including spectroscopy, scattering, microbalance, microprobe, electron microscopy. also compare silico methods discuss interplay computational with Finally, we outlook future for OMIEC-based devices look toward multimodal more comprehensive accurate description OMIECs.

Language: Английский

---

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Molecules, Journal Year: 2023, Volume and Issue: 28(9), P. 3906 - 3906

Published: May 5, 2023

The application of computational approaches in drug discovery has been consolidated the last decades. These families techniques are usually grouped under common name "computer-aided design" (CADD), and they now constitute one pillars pharmaceutical pipelines many academic industrial environments. Their implementation demonstrated to tremendously improve speed early steps, allowing for proficient rational choice proper compounds a desired therapeutic need among extreme vastness drug-like chemical space. Moreover, CADD allows rationalization biochemical interactive processes interest at molecular level. Because this, tools extensively used also field 3D design optimization entities starting from structural information targets, which can be experimentally resolved or obtained with other computer-based techniques. In this work, we revised state-of-the-art computer-aided methods, focusing on their different scenarios biological interest, not only highlighting great potential benefits, but discussing actual limitations eventual weaknesses. This work considered brief overview methods discovery.

Language: Английский

---

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

ACS Omega, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 8, 2024

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey field computational enzymology, highlighting key principles governing and discussing ongoing challenges promising advances. Over years, computer simulations have become indispensable in study mechanisms, with integration experimental exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies demonstrated power characterizing reaction pathways, transition states, substrate selectivity, product distribution, dynamic conformational changes various enzymes. Nevertheless, significant remain investigating multistep reactions, large-scale changes, allosteric regulation. Beyond mechanistic studies, modeling has emerged an tool computer-aided design rational discovery covalent drugs targeted therapies. Overall, design/engineering drug development can greatly benefit from our understanding detailed enzymes, such protein dynamics, entropy contributions, allostery, revealed by studies. Such convergence different research approaches expected continue, creating synergies research. This outlining ever-expanding research, aims provide guidance future directions facilitate new developments important evolving field.

Language: Английский

---

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(17)

Published: May 1, 2024

As the most important solvent, water has been at center of interest since advent computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use simple model potentials describe atomic interactions, accurate ab initio relying on first-principles calculation energies forces have opened way predictive aqueous systems. Still, these are very demanding, which prevents study complex systems their properties. Modern machine learning (MLPs) now reached a mature state, allowing us overcome limitations by combining high accuracy electronic structure calculations with efficiency empirical force fields. In this Perspective, we give concise overview about progress made in simulation employing MLPs, starting from work free molecules clusters via bulk liquid electrolyte solutions solid–liquid interfaces.

Language: Английский

---

Machine Learning of Reactive Potentials

Annual Review of Physical Chemistry, Journal Year: 2024, Volume and Issue: 75(1), P. 371 - 395

Published: June 28, 2024

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction training of MLPs enable fast accurate simulations analysis thermodynamic kinetic properties. This review focuses on application to reaction systems with consideration bond breaking formation. We development MLP models, primarily neural network kernel-based algorithms, recent applications reactive (RMLPs) at different scales. show how RMLPs are constructed, they speed up calculation dynamics, facilitate study trajectories, rates, free energy calculations, many other calculations. Different data sampling strategies applied building also discussed a focus collect structures for rare events further improve their performance active learning.

Language: Английский

---

Advancements in molecular simulation for understanding pharmaceutical pollutant Adsorption: A State-of-the-Art review

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125513 - 125513

Published: July 14, 2024

The contamination of natural water resources by pharmaceutical pollutants has become a significant environmental concern. Traditional experimental approaches for understanding the adsorption behavior these contaminants on different surfaces are often time-consuming and resource-intensive. In response, this review article explores powerful combination in silico techniques, including molecular dynamics (MD), Monte Carlo simulations (MC), quantum mechanics (QM), as comprehensive toolset to obtain broad perspectives into pollutants. By bridging multiple scales, from molecular-level interactions macroscopic impact, computational methods offer holistic processes involved. We provide an overview their ecological effects, emphasizing need efficient sustainable solutions. Subsequently, we delve theoretical foundations MD, MC, QM, highlighting respective strengths simulating pollutant adsorption. Moreover, synergistic potential combining methodologies is also discussed more characterization processes. Recent case studies illustrate successful application techniques predicting behaviors various conditions. Finally, implications discussed, along with how modelling can guide solutions mitigating impact.

Language: Английский

---