Quantum Mechanical Calculations Research Articles

Computational Methods for Predicting Chemical Reactivity of Covalent Compounds.

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities. Accurately predicting and modulating this reactivity have become a critical focus in the field. In this work, we compiled a data set of 419 cysteine-targeted covalent compounds and their reactivity through an extensive literature review. Employing machine learning, deep learning, and quantum mechanical calculations, we evaluated the intrinsic reactivity of the covalent compounds. Our FP-Stack models demonstrated robust Pearson and Spearman correlations of approximately 0.80 and 0.75 on the test set, respectively. This empowers rapid and accurate reactivity predictions, significantly reducing computational costs and streamlining structural handling and experimental procedures. Experimental validation on acrylamide compounds underscored the predictive efficacy of our model. This study presents an efficient computational tool for the reactivity prediction of covalent compounds and is expected to offer valuable insights for guiding covalent drug discovery and development.

Self-consistent electron density with shell structure using neural network-based Pauli potential.

The orbital-free density functional theory (OF-DFT) based method is a convenient tool to carry out electronic structure calculations scaling almost linearly with the number of electrons. However, the main impediment in the application of this method is the unavailability of the accurate form for the non-interacting kinetic energy functional in terms of electron density. The Pauli kinetic energy functional is the unknown part of the kinetic energy functional, and the corresponding Pauli potential appears in the governing Euler equation. In the present study, we present a feed-forward neural network (NN) approach to represent the Pauli potential of a group of atomic systems possessing spherically symmetric ground-state densities. This NN-based representation of Pauli potential combined with the Hohenberg-Kohn variational principle yields self-consistent radial densities that accurately exhibit the correct atomic shell structure. For this approach, the electron density in the form of a grid serves as the input to the NN model. In addition, we calculated the non-interacting kinetic energy by summing the Pauli kinetic energy, derived from the NN-based Pauli potential, and the von Weizsäcker kinetic energy. Our results demonstrate high accuracy for smaller atoms, while larger atoms exhibit greater deviations when compared with smaller atoms. The method presented in this paper provides an efficient way to calculate the Pauli potential and the Pauli kinetic energy without the need for functional derivatives. Our study represents a significant step forward in the application of machine learning techniques to OF-DFT, showcasing the potential of NNs in improving the accuracy and efficiency of quantum mechanical calculations in atomic systems.

Toward Grid-Based Models for Molecular Association.

This paper presents a grid-based approach to model molecular association processes as an alternative to sampling-based Markov models. Our method discretizes the six-dimensional space of relative translation and orientation into grid cells. By discretizing the Fokker-Planck operator governing the system dynamics via the square-root approximation, we derive analytical expressions for the transition rate constants between grid cells. These expressions depend on geometric properties of the grid, such as the cell surface area and volume, which we provide. In addition, one needs only the molecular energy at the grid cell center, circumventing the need for extensive MD simulations and reducing the number of energy evaluations to the number of grid cells. The resulting rate matrix is closely related to the Markov state model transition matrix, offering insights into metastable states and association kinetics. We validate the accuracy of the model in identifying metastable states and binding mechanisms, though improvements are necessary to address limitations like ignoring bulk transitions and anisotropic rotational diffusion. The flexibility of this grid-based method makes it applicable to a variety of molecular systems and energy functions, including those derived from quantum mechanical calculations. The software package MolGri, which implements this approach, offers a systematic and computationally efficient tool for studying molecular association processes.

Evolution of electronic structure and optical properties of naphthalenediimide dithienylvinylene (NDI-TVT) polymer as a function of reduction level: a density functional theory study.

Naphthalenediimide (NDI)-based donor-acceptor co-polymers with tunable electronic, optical, mechanical, and transport properties have shown immense potential as n-type conducting polymers in organic (opto)electronics. During the operation, the polymers undergo reduction at different charged states, which alters their (opto)electronic properties mainly due to the formation of the quasiparticles, polaron/bipolaron. The theoretical study based on quantum mechanical calculations can provide us with a detailed understanding of their (opto)electronic properties, which is missing to a great extent. To date, a theoretical understanding of how these properties vary with reduction levels for NDI-based polymers is completely missing. Herein, the evolution of the electronic structure and optical properties of the naphthalenediimide dithienylvinylene (NDI-TVT) polymer with varying reduction levels (Cred) is studied using density functional theory and time-dependent density functional theory, respectively, in the gaseous phase and solvent phase. We have envisaged that at lower reduction levels, Cred ≤ 100% (i.e., up to one negative charge per NDI moiety), only radical anions, i.e., polarons, are formed. The bipolarons are observed to be formed only at higher reduction levels, Cred > 100%. We note the coexistence of polarons and bipolarons for the intermediate reduction levels (100% < Cred < 200%). Finally, at 200% reduction levels, the presence of two electrons per NDI unit leads to the completely spin-resolved bipolaronic state formation, where one bipolaron is localized at every NDI unit. This aforementioned evolution of polarons and bipolarons with varying reduction levels is also prominently reflected in the calculated UV-vis-NIR absorption spectra. The detailed theoretical insights gained from the evolution of the (opto)electronic properties of NDI-TVT with reduction levels due to the formation of polaronic/bipolaronic states can guide the systematic design of n-type NDI-TVT-based (opto)electronic devices and in their advancement.

Anti-CRISPR proteins in Gluconobacter oxydans inactivate FnCas12a by acetylation.

Gluconobacter oxydans is an important chassis cell for one-step production of vitamin C. Previous studies reported that CRISPR/Cas12a is naturally inactivated in G. oxydans, but the specific mechanism remains unclear. Here, we identified anti-CRISPR proteins AcrVA6, AcrVA7 and AcrVA8 in G. oxydans. They functioned as acetyltransferases to inactivate FnCas12a by respectively acetylating Lys671, Lys589 and Lys823 of FnCas12a. Lys671 and Lys823 were related residues that recognise the protospacer-adjacent motif, modification of AcrVA6 and AcrVA8 untangled the interaction between FnCas12a and dsDNA, while Lys589 played an important role in binding to the crRNA-target DNA heteroduplex, AcrVA7 prevented the formation of FnCas12a-crRNA binary complexes. In addition, histone deacetylase HDAC11 was found to prevent modification of FnCas12a by AcrVA6. Quantum mechanical calculations showed that ser37 of AcrVA6, as an intermediate between acetyl group and receptor protein, achieves acetylation through ping-pong transfer mechanism. Finally, the acetyltransferase AcrVA6 and the deacetylase HDAC11 served as photoswitches by writing and erasing acetyl groups, respectively, to achieve continuous on-off of FnCas12a. Our study reveals different mechanisms by which acetyltransferase inactivates Cas12a and successfully applies reversible acetylation to the regulation of gene editing tools, providing new insights into the function and application of acetylation.

Antimicrobial Properties of Monomeric and Dimeric Catanionic Surfactant System.

Cationic gemini surfactants are used due to their broad spectrum of activity, especially surface, anticorrosive and antimicrobial properties. Mixtures of cationic and anionic surfactants are also increasingly described. In order to investigate the effect of anionic additive on antimicrobial activity, experimental studies were carried out to obtain MIC (minimal inhibitory concentration) against E. coli and S. aureus bacteria. Two gemini surfactants (12-6-12 and 12-O-12) and two single quaternary ammonium salts (DTAB and DDAC) were analyzed. The most commonly used commercial compounds of this class, i.e., SDS and SL, were used as anionic additives. In addition, computer quantum mechanical studies were also carried out to confirm the relationship between the structure of the mixture and the activity. The obtained results of microbiological tests and quantum mechanical calculations are in agreement with each other and show the lack of synergism in catanionic mixtures in the case of antibacterial activity.

Unveiling the intermolecular forces and unique properties of [EMIM][EtSO4] + [EMIM][MeSO3]

This study delves into the intricate interplay of intermolecular forces governing the archetypal ionic liquid mixture [EMIM][EtSO4] + [EMIM][MeSO3] through a synergistic integration of experimental and computational techniques. High-precision measurements of density, viscosity, and thermal conductivity across a temperature and compositional landscape unveil the system’s phase behavior and miscibility. Complementary insights into the microscopic interactions, including hydrogen bonding and ion-specific effects, are gleaned from molecular dynamics simulations and quantum mechanical calculations. This multipronged approach fosters a comprehensive understanding of the underlying forces dictating the observed thermophysical properties, propelling fundamental knowledge in the realm of ionic liquid mixtures and paving the way for the rational design of tailor-made ionic liquids with targeted properties for diverse fields, ranging from chemical engineering to energy storage.

Unprecedented carbonic anhydrase inhibition mechanism: Targeting histidine 64 side chain through a halogen bond.

2,2'-Thio-bis(4,6-dichlorophenol), namely bithionol, is a small molecule endowed with a multifaceted bioactivity. Its peculiar polychlorinated phenolic structure makes it a suitable candidate to explore its potentialities in establishing interaction patterns with enzymes of MedChem interest, such as the human carbonic anhydrase (hCA) metalloenzymes. Herein, bithionol was tested on a panel of specific hCAs through the stopped-flow technique, showing a promising micromolar inhibitory activity for the hCA II isoform. X-ray crystallographic studies revealed an unprecedented halogen-bond interaction between one chlorine of bithionol and the N3(ε) atom of the hCA II catalytically active histidine residue, His64. Then, quantum mechanics calculations based on the fragment molecular orbital method allowed us to estimate the strength of this bond (~2.9 kcal/mol) and highlighted the contribution of a rich hydrophobic interaction network within the isoenzyme. Interestingly, the compound inactivity against the hCA III isoform, characterized by His64Lys and Leu198Phe mutations, supported the key role played by halogen bonding in the enzyme affinity. This finding might pave the way for the development of a new class of hCA inhibitors characterized by such chemical features, with the halogen bond being a key ligand-receptor interaction.

Mechanism of ultrafast flavin photoreduction in the active site of flavoenzyme LSD1 histone demethylase.

Photoreduction of oxidized flavins has a functional role in photocatalytic and photoreceptor flavoproteins. In flavoproteins without light-dependent physiological functions, ultrafast, reversible flavin photoreduction is supposedly photoprotective by nature, and holds potential for nonnatural photocatalytic applications. In this work, we combine protein mutagenesis, ultrafast spectroscopy, molecular dynamics simulations and quantum mechanics calculations to investigate the nonfunctional flavin photoreduction in a flavoenzyme, lysine-specific demethylase 1 (LSD1) which is pivotal in DNA transcription. LSD1 harbors an oxidized flavin adenine dinucleotide (FAD) cofactor and multiple electron-donating residues in the active site. Upon photoexcitation, the FAD cofactor is photoreduced in <200 fs by electron transfer (ET) from nearby residue(s), and the charge pairs recombine in ca. 2 ps. Site-directed mutagenesis pinpoints a specific tryptophan residue, W751, as the primary electron donor, whereas a tyrosine residue, Y761, despite being located closer to the flavin ring, does not effectively contribute to the process. Based on a hybrid quantum-classical computational approach, we characterize the W751-FAD and Y761-FAD charge-transfer states (CTW751 and CTY761, respectively), as well as the FAD locally excited state (LEFAD), and demonstrate that the coupling between LEFAD and CTW751 is larger than those involving CTY761 by an order of magnitude, rationalizing the experimental observations. More generally, this work highlights the role of the intrinsic protein environment and details of donor-acceptor molecular configurations on the dynamics of short-range ET involving a flavin cofactor and amino acid residue(s).

Tunnel barriers for Fe-based spin valves providing high spin-polarized current.

Employing density functional theory for ground state quantum mechanical calculations and the non-equilibrium Green's function method for transport calculations, we investigate the potential of CdS, ZnS, Cd3ZnS4, and Zn3CdS4 as tunnel barriers in magnetic tunnel junctions for spintronics. Based on the finding that the valence band edges of these semiconductors are dominated by pz orbitals and the conduction band edges by s orbitals, we show that Δ1 symmetry filtering of the Bloch states in magnetic tunnel junctions with Fe electrodes results in high tunneling magnetoresistances and high spin-polarized current (up to two orders of magnitude higher than in the case of the Fe/MgO/Fe magnetic tunnel junction).

Decoherence, entanglement, and information in the electron double-slit experiment with monitoring

This paper considers a theoretical model of the double-slit experiment with electrons whose paths are monitored. This monitoring, inspired by a recent text by Maudlin, is performed by the Coulomb scattering of the electron by a proton. A simple quantum mechanical calculation is presented, inspired in part by a recent experimental demonstration of this famous thought experiment. The results illustrate the relationship between entanglement and the loss of coherence in the interference pattern. The tradeoff between the visibility of interference and the information gained by measurement is also explored. This calculation can provide advanced undergraduates insight into decoherence, entanglement, and quantum information.

A General Nonbonded Force Field Based on Accurate Quantum Mechanics Calculations for Elements H-La and Hf-Rn.

Noncovalent interactions (NCI) play a central role in numerous physical, chemical, and biological phenomena. An accurate description of NCI is the key to success for any theoretical study in such areas. Although quantum mechanics (QM) methods such as dispersion-corrected density functional theory are sufficiently accurate, their applications are practical only for <300 atoms and <100 ps of simulation time. Thus, empirical force fields (FF) have generally been the only choice for systems with thousands to millions of atoms and for nanoseconds and longer. We want to develop a FF that can be applied to applications of thousands to millions of atoms with an accuracy comparable to QM methods. As the first step, we develop here a new general nonbonded potential (GNB) based on a novel functional form with four adjustable parameters for each element. We report here parameters for elements H-La, Hf-Rn (excluding lanthanides and actinides) by fitting the interaction energy of molecular complexes to QM calculations using the accurate Head-Gordon ωB97M-V density functional. We performed extensive testing of GNB for organic molecules, organometallic molecules, and metal organic-framework (MOF) systems. The mean absolute errors of GNB are 0.37 kcal/mol for the dispersion and mixed groups of the S66 × 8 benchmark set, 0.35 kcal/mol for CO2 adsorption on MOF materials, and 4.53 kcal/mol for the XTMC43 benchmark. GNB outperforms existing FF and in many cases has accuracy comparable to that of QM methods such as PBE-D3. GNB can potentially replace the nonbonded part of existing FFs in a wide range of applications.

Enhancing the structural and optoelectronic properties of carboxymethyl cellulose sodium filled with ZnO/GO and CuO/GO nanocomposites for antimicrobial packaging applications.

One of the biggest challenges in food packaging is the creation of sustainable and eco-friendly packaging materials to shield foods from ultraviolet (UV) photochemical damage and to preserve the distinctive physical, chemical, and biological characteristics of foods throughout the supply chain. Accordingly, this study focuses on enhancing the UV shielding properties and biological activity of carboxylmethyl cellulose sodium (CMC) through modifications using zinc oxide (ZnO), copper oxide (CuO), and graphene oxide (GO) using the solution casting technique. The hybrid nanocomposites were characterized by fourier-transform infrared (FTIR) spectrophotometer, ultraviolet-visible (UV-Vis) spectrophotometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and x-ray diffraction (XRD). Significant interactions between CMC and the metal oxide/GO nanocomposites were revealed by FTIR analysis, which reflects the formation of hydrogen bonding between CMC and the nanocomposites. XRD confirmed the functionalization of CMC with ZnO/GO and CuO/GO nanocomposites. Additionally, the CMC film showed a decrease in the optical bandgap from 5.53 to 3.43eV with improved UV shielding capacity. Moreover, the composite films had excellent refractive index and optical conductivity values of 1.97 and 1.56 × 1010Ωcm- 1, respectively. SEM and EDX analysis confirmed the formation of ZnO/GO and CuO/GO within the CMC matrix. Thus, dedicates that the CMC nanocomposites have promising applications in packaging materials. These results were confirmed by the quantum mechanical calculations utilizing density functional theory (DFT). Total dipole moment (TDM), frontier molecular orbitals (FMOs), chemical reactivity descriptors, and molecular electrostatic potential (MESP) maps were all studied using the B3LYP/LanL2DZ model. The TDM and FMO investigations revealed that the CMC/CuO/GO model has the highest TDM (84.031 Debye) and the smallest band gap energy (0.118eV). Moreover, CMC's reactivity increased after CuO/GO nanocomposites integration, as demonstrated by MESP mapping. Finally, the antibacterial activity of pure CMC, CMC/ZnO/GO, and CMC/CuO/GO nanocomposite films was evaluated against Staphylococcus aureus and Escherichia coli. The zones of inhibition data showed that both CMC/ZnO/GO and CMC/CuO/GO exhibited higher antibacterial activity than CMC alone, particularly against S. aureus. The inhibition zones for CMC/ZnO/GO and CMC/CuO/GO against S. aureus were 16mm and 14mm, respectively, suggesting enhanced susceptibility of S. aureus compared to E. coli. These results highlight the significant potential of ZnO and CuO NPs in improving the antimicrobial efficacy of CMC nanocomposites.

Impact of Intracellular Proteins on μ-Opioid Receptor Structure and Ligand Binding.

Chronic pain is a prevalent problem affecting approximately one out of every five adults in the U.S. The most effective way to treat chronic pain is with opioids, but they cause dangerous side effects such as tolerance, addiction, and respiratory depression, which makes them quite deadly. Opioids, such as fentanyl, target the μ-opioid receptor (MOR), which can then bind to the intracellular Gi protein or the β-arrestin protein. The Gi pathway is primarily responsible for pain relief and potential side effects, but the β-arrestin pathway is chiefly responsible for the unwanted side effects. Ideally, an effective pain medication without side effects would bind to MOR, which would bias signaling solely through the Gi pathway. We used the Bio3D library to conduct principal component analysis to compare the cryo-electron microscopy MOR structures in complex with the Gi versus an X-ray crystallography MOR structure with a nanobody acting as a Gi mimic. Our results agree with a previous study by Munro, which concluded that nanobody-bound MOR is structurally different than Gi-bound MOR. Furthermore, we investigated the structural diversity of opioids that can bind to MOR. Quantum mechanical calculations show that the low energy solution structures of fentanyl differ from the one bound to MOR in the experimental structure, and pKa calculations reveal that fentanyl is protonated in aqueous solution. Glide docking studies show that higher energy structures of fentanyl in solution form favorable docking complexes with MOR. Our calculations show the relative abundance of each fentanyl conformation in solution as well as the energetic barriers that need to be overcome to bind to MOR. Docking studies confirm that multiple fentanyl conformations can bind to the receptor. Perhaps a variety of conformations of fentanyl can stabilize multiple conformations of the MOR, which can explain why fentanyl can induce different intracellular signaling and multiple physiological effects.

QM40, Realistic Quantum Mechanical Dataset for Machine Learning in Molecular Science

The growing popularity of machine learning (ML) and deep learning (DL) in scientific fields is hindered by the scarcity of high-quality datasets. While quantum mechanical (QM) predictions using DL techniques such as graph neural networks (GNNs) and generative models are gaining traction, insufficient training data remains a bottleneck. The QM40 dataset addresses this challenge by representing 88% of the FDA-approved drug chemical space. It includes molecules containing 10 to 40 atoms and composed of elements commonly found in drug molecular structures (C, O, N, S, F, Cl). QM40 offers valuable resources for researchers which include the core QM40 main dataset, containing 16 key quantum mechanical parameters for 162,954 molecules calculated using the B3LYP/6-31G(2df,p) level of theory in Gaussian16, ensuring consistency with established datasets like QM9 and Alchemy. This compatibility allows for future concatenation of QM40 with these datasets. In addition to other valuable information, the QM40 dataset offers the initial and optimized Cartesian coordinates, Mulliken charges, and detailed bond information, including local vibrational mode force constants, which serve as indicators of bond strength. QM40 can be used to benchmark both existing and new methods for predicting QM calculations using ML and DL techniques.

Pseudo‐silapnictene and Bis(pseudo‐silapnictene) – Hyperconjugatively Stabilized Heavier Analogues of Imine and Ethane‐1,2‐diimine

The equilibrium geometry, bonding, and proton affinity of the pseudo‐silapnictenes and the bis(pseudo‐silapnictenes) are explored using computational quantum mechanical calculations carried out at M06/def2‐TZVPP//BP86‐D3(BJ)/def2‐SVP level of theory. The pseudo‐silapnictene 1E features a dicoordinated, monovalent group‐15 center having two lone pairs of σ‐ and π‐local symmetry. Both the lone pairs are stabilized by hyperconjugative donation to the silylene fragment. The extent of hyperconjugative stabilization of π‐type lone pair is higher than that of σ‐type lone pair and is comparable to classical Si‐E π‐bonds. Accordingly, the former interactions can be considered as pseudo‐π‐bonding interactions. The strength of the pseudo‐π‐bond reduces as the group‐15 element changes from nitrogen to bismuth. Hence, 1E can be considered as heavier analogues of imine. Even though these lone pairs are stabilized by hyperconjugative interactions, they are very reactive as indicated by the very high values of first and second proton affinities. In the bis(pseudo‐silapnictene) 2E, two Si‐E pseudo‐π‐bonds formed by hyperconjugative interactions are delocalized over the E‐Si‐Si‐E skeleton. Since the π‐bonds are majorly localized on the group‐15 element, the extent of delocalization is quite less than that of the classical π‐delocalized system. Nevertheless, the bis(pseudo‐silapnictene) can be considered as the heavier analogue of ethane‐1,2‐diimine.

Synthesis, XRD and DFT studies on Pb[ChCN]2 (Ch = O, S, Se) and Pb[SeCN][OH

We report on the synthesis of Pb[ChCN]2 (Ch = O, S, Se) and Pb[SeCN][OH] and their hitherto unknown crystal structures. All products were fully characterized by single-crystal and powder X-ray diffraction. Bulk analysis was performed by Rietveld refinement, elemental analysis, differential scanning calorimetry-thermogravimetric analysis (DSC-TGA), and solid-state Raman spectroscopy. Quantum-mechanical calculations based on the density functional theory allowed optimization of the structural parameters of these lead-based compounds, as well as assessment of their electronic structures and detailed assignment and interpretation of the vibrational spectra. In addition, the origin of bands related to difficulties in assigning the exact position of H atoms in the structures was resolved.

Bipyridine-Containing Optically Active π-Conjugated Polymers Derived from Amino Alcohols: Examination of the Higher-Order Structures, Chiral Recognition, and Metal Coordination Based on Quantum Mechanical and Molecular Dynamics Calculations.

Helical π-conjugated polymers are promising as optically active functional materials. The present paper reports the synthesis of novel bipyridine-containing π-conjugated polymers with optically active amino-alcohol-derived side chains, examination of their higher-order structures, chiral recognition, and metal coordination properties. The polymers adopt a folded helical conformation and aggregate in CHCl3/MeOH depending on the solvent composition, as supported by density functional theory calculations and molecular dynamics simulations. The polymer films showed differences in contact angles with aqueous solutions of (R)- and (S)-alcohols. Addition of some metal chlorides and perchlorates changed the intensity and color of the photoluminescence of the polymers.

Self-Assembly of Supramolecular Double Helix from a Tetrapeptide and Direct Visualization by Scanning Tunneling Microscopy.

This work reports single-crystal X-ray diffraction (XRD), Scanning Tunneling Microscopy (STM), and quantum mechanics calculations of the 310-helical peptide Z-(Aib)2-L-Dap(Boc)-Aib-NHiPr (Aib, α-aminoisobutyric acid; Dap, 2,3-diaminopropionic acid; Z, benzyloxycarbonyl; Boc, t-butoxycarbonyl). The peptide forms a double-helical superstructure, studied by XRD and STM. Such architecture is rare in short peptides. Here, we show, by combining XRD and STM that this intriguing conformational feature is not driven by crystal packing; rather, it is an intrinsic property of this peptide. Indeed, the double helix is clearly detected also by STM, where crystal packing cannot be invoked. XRD reveals that intermolecular H-bonds stabilize two left-handed supra-helices (tertiary structure) that develop around a 6-fold screw axis. Then, two supra-helices are intertwined in a quaternary structure as a left-handed, double supra-helix, where C-H⋯π interactions play a crucial role. STM images show the formation of long, isolated "necklaces" (>110 nm). They are of left and right helical handedness. Their size agrees with the XRD finding. DFT calculations allowed us to weigh the contribution of the different intermolecular interactions in the two single supra-helices and the supramolecular double-helix. Interestingly, we were able to conclude that the contribution of the C-H⋯π interactions to the binding energies is close to 50 %.

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

AbstractThis review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification of candidates with desirable properties. Recently, the development of highly accurate ML interatomic potentials and generative models has not only improved the robust prediction of physical properties, but also significantly accelerated the discovery of materials. In the past couple of years, ML methods have enabled high‐precision first‐principles predictions of electronic and optical properties for large systems, providing unprecedented opportunities in materials science. Furthermore, ML‐assisted microstructure reconstruction and physics‐informed solutions for partial differential equations have facilitated the understanding of microstructure–property relationships. Most recently, the seamless integration of various ML platforms has led to the emergence of autonomous laboratories that combine quantum mechanical calculations, large language models, and experimental validations, fundamentally transforming the traditional approach to novel materials synthesis. While highlighting the aforementioned recent advances, existing challenges are also discussed. Ultimately, ML is expected to fully integrate atomic‐scale simulations, reverse engineering, process optimization, and device fabrication, empowering autonomous and generative energy system design. This will drive transformative innovations in energy conversion, storage, and harvesting technologies.