Alexandre Tkatchenko
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View article: AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions Open
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-lo…
View article: Repulsive Inverse-Distance Interatomic Interaction from Many-Body Quantum Electrodynamics
Repulsive Inverse-Distance Interatomic Interaction from Many-Body Quantum Electrodynamics Open
Interactions between objects can be classified as fundamental or emergent. Fundamental interactions are either extremely short-range or decay inversely with the separation distance, such as the Coulomb potential between charges or the grav…
View article: aims-PAX: Parallel Active eXploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials
aims-PAX: Parallel Active eXploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials Open
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient acti…
View article: Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See
Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See Open
Exact determination of the electronic density of molecules and materials would provide direct access to accurate bonded and nonbonded interatomic interactions via the Hellman-Feynman theorem. However, density-functional approximations (DFA…
View article: Accurate noncovalent interactions in atomistic systems via quantum Drude oscillators
Accurate noncovalent interactions in atomistic systems via quantum Drude oscillators Open
Accurately modeling polarization and van der Waals (vdW) interactions in atomistic systems typically requires high-level quantum-mechanical methods that are computationally expensive, hence limited in applicability. To address this challen…
View article: Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions Open
Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately ac…
View article: Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction
Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction Open
Machine learning (ML) approaches have drastically advanced the exploration of structure-property and property-property relationships in computer-aided drug discovery. A central challenge in this field is the identification of molecular des…
View article: Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer Open
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability wi…
View article: Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Open
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key ste…
View article: aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields Open
Recent advances in machine learning force fields (MLFF) have significantly extended the capabilities of atomistic simulations. This progress highlights the critical need for reliable reference datasets, accurate MLFFs, and, crucially, effi…
View article: aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields Open
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient acti…
View article: A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data
A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data Open
Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from…
View article: Power of the Many‐Body Force: Magnitudes and Angles of Atomic Van der Waals Dispersion Forces in Extended Molecular Systems
Power of the Many‐Body Force: Magnitudes and Angles of Atomic Van der Waals Dispersion Forces in Extended Molecular Systems Open
A distinctive feature of soft materials such as polymers, liquids, biomolecules, and nanostructures is that their macroscopic properties are highly dependent on structural dynamics, even at equilibrium. This is a consequence of fewer coval…
View article: Atomic orbits in molecules and materials for improving machine learning force fields
Atomic orbits in molecules and materials for improving machine learning force fields Open
The accurate representation of atoms within their environment forms the backbone of any reliable machine learning force field (MLFF). While modern MLFFs treat atoms of the same type as indistinguishable, their identities can be further res…
View article: Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Open
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key ste…
View article: Chalcogen Bonding with Telluronium Cations: toward Selective Population of Tellurium σ-Holes by Lewis Bases
Chalcogen Bonding with Telluronium Cations: toward Selective Population of Tellurium σ-Holes by Lewis Bases Open
The reaction of tris[3,5-bis(trifluoromethyl)phenyl]telluronium tetrakis[3,5-bis(trifluoromethyl)phenyl]borate (BArF-) salt with phosphine oxides OPR3 in 1,2-dichloroethane is exothermic and leads to the population of…
View article: Accurate Density Functional Theory for Non-Covalent Interactions in Charged Systems
Accurate Density Functional Theory for Non-Covalent Interactions in Charged Systems Open
Accurately modeling non-covalent interactions (NCIs) involving charged systems remains an outstanding challenge in Density Functional Theory (DFT), with implications across natural and life sciences, engineering, e.g., in biochemistry, cat…
View article: A Journey With <scp>THeSeuSS</scp>: Automated Python Tool for Modeling <scp>IR</scp> and Raman Vibrational Spectra of Molecules and Solids
A Journey With <span>THeSeuSS</span>: Automated Python Tool for Modeling <span>IR</span> and Raman Vibrational Spectra of Molecules and Solids Open
Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software)—an automated computational …
View article: Molecule–Environment Embedding with Quantum Monte Carlo: Electrons Interacting with Drude Oscillators
Molecule–Environment Embedding with Quantum Monte Carlo: Electrons Interacting with Drude Oscillators Open
We present a comprehensive investigation of the El-QDO embedding method [Phys. Rev. Lett. 131, 228001 (2023)], where molecular systems described through the electronic Hamiltonian are immersed in a bath of charged quantum har…
View article: Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks
Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks Open
Semi-empirical electronic structure methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization,…
View article: What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms
What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms Open
Machine Learning (ML) methods are increasingly being used across a variety of fields, and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using dat…
View article: Atomic Orbits in Molecules and Materials for Improving Machine Learning Force Fields
Atomic Orbits in Molecules and Materials for Improving Machine Learning Force Fields Open
The accurate representation of atoms within their environment forms the backbone of any reliable machine learning force field (MLFF). While modern MLFFs treat atoms of the same type as indistinguishable, their identities can be further res…
View article: Machine learning surrogate models of many-body dispersion interactions in polymer melts
Machine learning surrogate models of many-body dispersion interactions in polymer melts Open
Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations lim…
View article: Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling
Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling Open
View article: Non-local interactions determine local structure and lithium diffusion in solid electrolytes
Non-local interactions determine local structure and lithium diffusion in solid electrolytes Open
Solid-state batteries, in which solid electrolytes (SEs) replace their liquid alternatives, promise high energy density and safety. However, understanding the relation between SE composition and properties, stemming from intricate interact…
View article: Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Open
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key ste…
View article: Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer Open
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability wi…
View article: Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions Open
Predicting the binding affinity of ligand molecules to protein pockets is a key step in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a wide range of attractive and repulsive electronic interactions invoked …
View article: Machine learning in the prediction of human wellbeing
Machine learning in the prediction of human wellbeing Open
Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-ba…
View article: Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks Open
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modelin…