MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods.

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

DNA-PKcs/AKT1 inhibits epithelial-mesenchymal transition during radiation-induced pulmonary fibrosis by inducing ubiquitination and degradation of Twist1.

INTRODUCTION: Radiation-induced pulmonary fibrosis (RIPF) is a chronic, progressive, irreversible lung interstitial disease that develops after radiotherapy. Although several previous studies have focused on the mechanism of epithelial-mesenchymal transition (EMT) in lung epithelial cells, the essential factors involved in this process remain poorly understood. The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) exhibits strong repair capacity when cells undergo radiation-induced damage; whether DNA-PKcs regulates EMT during RIPF remains unclear. OBJECTIVES: To investigate the role and molecular mechanism of DNA-PKcs in RIPF and provide an important theoretical basis for utilising DNA-PKcs-targeted drugs for preventing RIPF. METHODS: DNA-PKcs knockout (DPK-/-) mice were generated via the Cas9/sgRNA technique and subjected to whole chest ionizing radiation (IR) at a 20 Gy dose. Before whole chest IR, the mice were intragastrically administered the DNA-PKcs-targeted drug VND3207. Lung tissues were collected at 1 and 5 months after IR. RESULTS: The expression of DNA-PKcs is low in pulmonary fibrosis (PF) patients. DNA-PKcs deficiency significantly exacerbated RIPF by promoting EMT in lung epithelial cells. Mechanistically, DNA-PKcs deletion by shRNA or inhibitor NU7441 maintained the protein stability of Twist1. Furthermore, AKT1 mediated the interaction between DNA-PKcs and Twist1. High Twist1 expression and EMT-associated changes caused by DNA-PKcs deletion were blocked by insulin-like growth factor-1 (IGF-1), an AKT1 agonist. The radioprotective drug VND3207 prevented IR-induced EMT and alleviated RIPF in mice by stimulating the kinase activity of DNA-PKcs. CONCLUSION: Our study clarified the critical role and mechanism of DNA-PKcs in RIPF and showed that it could be a potential target for preventing RIPF.

Wnt signaling: Modulating tumor-associated macrophages and related immunotherapeutic insights.

Wnt signaling pathways are highly conserved cascades that mediate multiple biological processes through canonical or noncanonical pathways, from embryonic development to tissue maintenance, but they also contribute to the pathogenesis of numerous cancers. Recent studies have revealed that Wnt signaling pathways critically control the interplay between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and potentially impact the efficacy of cancer immunotherapy. In this review, we summarize the evidence that Wnt signaling pathways boost the maturation and infiltration of macrophages for immune surveillance in the steady state but also polarize TAMs toward immunosuppressive M2-like phenotypes for immune escape in the TME. Both cancer cells and TAMs utilize Wnt signaling to transmit signals, and this interaction is crucial for the carcinogenesis and progression of common solid cancers, such as colorectal, gastric, hepatocellular, breast, thyroid, prostate, kidney, and lung cancers; osteosarcoma; and glioma. Specifically, compared with those in solid cancers, Wnt signaling pathways play a distinct role in the pathogenesis of leukemia. Efforts to develop Wnt-based drugs for cancer treatment are still ongoing, and some indeed enhance the anticancer immune response. We believe that the combination of Wnt signaling-based therapy with conventional or immune therapies is a promising therapeutic approach and can facilitate personalized treatment for most cancers.

Assessing bioartificial organ function: the 3P model framework and its validation.

The rapid advancement in the fabrication and culture of in vitro organs has marked a new era in biomedical research. While strides have been made in creating structurally diverse bioartificial organs, such as the liver, which serves as the focal organ in our study, the field lacks a uniform approach for the predictive assessment of liver function. Our research bridges this gap with the introduction of a novel, machine-learning-based "3P model" framework. This model draws on a decade of experimental data across diverse culture platform studies, aiming to identify critical fabrication parameters affecting liver function, particularly in terms of albumin and urea secretion. Through meticulous statistical analysis, we evaluated the functional sustainability of the in vitro liver models. Despite the diversity of research methodologies and the consequent scarcity of standardized data, our regression model effectively captures the patterns observed in experimental findings. The insights gleaned from our study shed light on optimizing culture conditions and advance the evaluation of the functional maintenance capacity of bioartificial livers. This sets a precedent for future functional evaluations of bioartificial organs using machine learning.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Investigating the impact of sebum secretion on liquid makeup foundation darkening and its solution.

OBJECTIVES: Darkening has been an issue of concern for foundation products. The secretion of sebum plays a significant role in the process of foundation darkening, but the underlying mechanisms and solutions have been rarely reported. The aim of this study was to explore the relationship between sebum secretion and liquid foundation darkening and to provide possible solutions for reducing sebum-induced darkening in liquid foundation. METHODS: Artificial sebum in different concentrations was added to a basic liquid foundation to simulate different stages of sebum secretion. The colour of the mixture was then measured by a spectrophotometer on the standard opacity chart. Potential technical solutions for anti-darkening were applied to a basic liquid foundation, and its ability to anti-darkening was further verified in vivo. RESULTS: (1) The influences of sebum addition on liquid foundation darkening had a significant positive correlation with the increase in transmissivities (R2 = 0.852, p < 0.01). (2) A certain range of sebum addition can reduce the darkening of volatile foundations. (3) The liquid foundations using pigments with high dispersibility in sebum were less influenced by sebum. (4) The replacement of pigments with oil-fixing ability could effectively reduce the darkening of liquid foundations induced by sebum (p < 0.01). CONCLUSION: The effect of sebum on the darkening of liquid foundation was accompanied by a greater transmissivity as its pigment concentration decreased. Balanced volatility, the addition of powders with higher sebum dispersibility and the replacement of oil-fixing powders could reduce the darkening of the liquid foundation caused by sebum secretion.

OBJECTIFS: L'assombrissement a été un problème de préoccupation pour les produits de fond de teint. La sécrétion de sébum joue un rôle significatif dans le processus d'assombrissement du fond de teint, mais les mécanismes sous-jacents et les solutions ont été rarement rapportés. L'objectif de cette étude était d'explorer la relation entre la sécrétion de sébum et l'assombrissement du fond de teint liquide, et de fournir des solutions possibles pour réduire l'assombrissement induit par le sébum dans le fond de teint liquide. MÉTHODES: Du sébum artificiel à différentes concentrations a été ajouté à un fond de teint liquide de base pour simuler différents stades de sécrétion de sébum. La couleur du mélange a ensuite été mesurée par un spectrophotomètre sur le tableau standard d'opacité. Des solutions techniques potentielles pour l'anti-assombrissement ont été appliquées à un fond de teint liquide de base et leur capacité à prévenir l'assombrissement a été vérifiée in vivo. RÉSULTATS: (1) Les influences de l'ajout de sébum sur l'assombrissement du fond de teint liquide avaient une corrélation significativement positive avec l'augmentation des transmissivités (R2 = 0.852 p < 0.01). (2) Une certaine plage de concentration de sebum peut réduire l'assombrissement des fondations volatiles. (3) Les fonds de teint liquides utilisant des pigments à haute dispersibilité dans le sébum étaient moins influencés par le sébum. (4) Le remplacement des pigments par des poudres à capacité de fixation d'huile pouvait efficacement réduire l'assombrissement des fonds de teint liquides induit par le sébum (p < 0.01). CONCLUSION: L'effet du sébum sur l'assombrissement du fond de teint liquide était accompagné d'une plus grande transmissivité à mesure que la concentration de son pigment diminuait. La volatilité équilibrée, l'ajout de poudres à plus grande dispersibilité de sébum et le remplacement de poudres à capacité de fixation d'huile pourraient réduire l'assombrissement du fond de teint liquide causé par la sécrétion de sébum.

Influence of landscape patterns on nitrate and particulate organic nitrogen inputs to urban stormwater runoff.

This study investigated the effect of the landscape pattern of permeable/impermeable patches on NO3--N and particulate organic nitrogen (PON) concentrations during stormwater runoff transport and their source contributions. Six landscape pattern indices, namely, mean proximity index (MPI), largest patch index (LPI), mean shape index (MSI), landscape shape index (LSI), connect index (CONNECT), and splitting index (SPLIT), were selected to reflect the fragmentation, complexity, and connectivity of permeable patches in urban catchments. The results show that lower fragmentation, higher complexity, and greater connectivity can reduce NO3--N concentrations in road runoff and drainage flow (i.e., the flow in the stormwater drainage network), as well as PON concentrations in road runoff. Further, the above landscape pattern is effective for mitigating the contributions of NO3--N and PON from road runoff. Low impact development (LID) can be incorporated with the landscape pattern of permeable/impermeable patches to mitigate nitrogen pollution in urban stormwater at the catchment scale by optimizing the spatial arrangement.

Energy-conserving molecular dynamics is not energy conserving.

Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials properties. It is common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics by estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, e.g., even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties.

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models.

We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.

CUDC-101 as a dual-target inhibitor of EGFR and HDAC enhances the anti-myeloma effects of bortezomib by regulating G2/M cell cycle arrest. / EGFR和HDAC双靶点抑制剂CUDC-101通过调控G2/M期阻滞增强硼替佐米抗骨髓瘤的作用.

CUDC-101, an effective and multi-target inhibitor of epidermal growth factor receptor (EGFR), histone deacetylase (HDAC), and human epidermal growth factor receptor 2 (HER2), has been reported to inhibit many kinds of cancers, such as acute promyelocytic leukemia and non-Hodgkin's lymphoma. However, no studies have yet investigated whether CUDC-101 is effective against myeloma. Herein, we proved that CUDC-101 effectively inhibits the proliferation of multiple myeloma (MM) cell lines and induces cell apoptosis in a time- and dose-dependent manner. Moreover, CUDC-101 markedly blocked the signaling pathway of EGFR/phosphoinositide-3-kinase (PI3K) and HDAC, and regulated the cell cycle G2/M arrest. Moreover, we revealed through in vivo experiment that CUDC-101 is a potent anti-myeloma drug. Bortezomib is one of the important drugs in MM treatment, and we investigated whether CUDC-101 has a synergistic or additive effect with bortezomib. The results showed that this drug combination had a synergistic anti-myeloma effect by inducing G2/M phase blockade. Collectively, our findings revealed that CUDC-101 could act on its own or in conjunction with bortezomib, which provides insights into exploring new strategies for MM treatment.

Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces.

The KREG and pKREG models were proven to enable accurate learning of multidimensional single-molecule surfaces of quantum chemical properties such as ground-state potential energies, excitation energies, and oscillator strengths. These models are based on kernel ridge regression (KRR) with the Gaussian kernel function and employ a relative-to-equilibrium (RE) global molecular descriptor, while pKREG is designed to enforce invariance under atom permutations with a permutationally invariant kernel. Here we extend these two models to also explicitly include the derivative information from the training data into the models, which greatly improves their accuracy. We demonstrate on the example of learning potential energies and energy gradients that KREG and pKREG models are better or on par with state-of-the-art machine learning models. We also found that in challenging cases both energy and energy gradient labels should be learned to properly model potential energy surfaces and learning only energies or gradients is insufficient. The models' open-source implementation is freely available in the MLatom package for general-purpose atomistic machine learning simulations, which can be also performed on the MLatom@XACS cloud computing service.

[sEMG Wireless Acquisition System Based on CC3200 and ADS1299].

A multi-channel surface electromyography wireless acquisition system is designed, which is mainly composed of ADS1299 integrated analog front-end chip and CC3200 wireless MCU of TI company. The key indicators of hardware are measured according to the industry standard, and the results are better than the industry standard, which can meet the continuous use of multi-scene tasks. This system has the advantages of high performance, low power consumption and small size. It has been applied to the detection of surface EMG signal in motion gesture recognition and has a good application value.

Chidamide and venetoclax synergistically exert cytotoxicity on multiple myeloma by upregulating BIM expression.

BACKGROUND: Multiple myeloma (MM) is the second most common hematologic malignancy with almost all patients eventually having relapse or refractory MM (RRMM), thus novel drugs or combination therapies are needed for improved prognosis. Chidamide and venetoclax, which target histone deacetylase and BCL2, respectively, are two promising agents for the treatment of RRMM. RESULTS: Herein, we found that chidamide and venetoclax synergistically exert an anti-myeloma effect in vitro in human myeloma cell lines (HMCLs) with a combination index (CI) < 1. Moreover, the synergistic anti-myeloma effect of these two drugs was demonstrated in primary MM cells and MM xenograft mice. Mechanistically, co-exposure to chidamide and venetoclax led to cell cycle arrest at G0/G1 and a sharp increase in DNA double-strand breaks. In addition, the combination of chidamide and venetoclax resulted in BCL-XL downregulation and BIM upregulation, and the latter protein was proved to play a critical role in sensitizing HMCLs to co-treatment. CONCLUSION: In conclusion, these results proved the high therapeutic potential of venetoclax and chidamide combination in curing MM, representing a potent and alternative salvage therapy for the treatment of RRMM.

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials.

Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade. Nevertheless, the assignment of the dynamic NMR spectra of high-rate battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, we develop a novel machine learning (ML) protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently, which enables us to calculate dynamic NMR shifts with the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na2/3(Mg1/3Mn2/3)O2 as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental 23Na shifts (1406 and 1493 ppm) of P2-type Na2/3(Ni1/3Mn2/3)O2 originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups P63/mcm and P6322, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems.

Going green in China: how does digital finance affect environmental pollution? Mechanism discussion and empirical test.

With the continuous advancement of the technological revolution and industrial transformation, environmental governance supported by digital finance has become an important engine for achieving carbon neutrality. Based on panel data from 30 provinces in China, this study discusses the spatial spillover effect and transmission mechanism between digital finance and environmental pollution. Our research results confirm that the inhibitory effect of digital finance on local environmental pollution gradually increases with the improvement of digital finance. Interestingly, digital finance has a significant positive spatial spillover effect on environmental pollution in surrounding areas. The mediating effect shows that digital finance can alleviate environmental pollution by improving technological innovation, industrial upgrading and industrial structure rationalization. A higher degree of marketization and governmental support can increase the positive influences of digital finance on pollution reduction. This research proves the effectiveness of digital finance in improving environmental governance, and it encourages policy-makers around the world to rely on digital finance to promote ecological governance and achieve high-quality economic development.

Myeloma-derived IL-32γ induced PD-L1 expression in macrophages facilitates immune escape via the PFKFB3-JAK1 axis.

Multiple myeloma (MM) is characterized by an accumulation of monoclonal plasma cells within the bone marrow (BM). Macrophages are an abundant component of myeloma BM microenvironment and support survival of the malignant cells and foster myeloma development and progression by suppression of the immune response. In our previous study, we found that MM patients overexpress pro-inflammatory cytokine interleukin-32 (IL-32). The present study aimed to investigate the role of IL-32 in myeloma progression and mechanisms of IL-32 on macrophages functions. We discovered that the expression of IL-32 was associated with the disease stage in myeloma patients. MM-derived exosomes containing IL-32γ promoted the expression of programmed death-ligand 1(PD-L1) by macrophages, thus promoting immune evasion. Mechanistically, myeloma-secreted IL-32γ acted via proteinase 3 (PR3) to enhance 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) dependent glycolysis and subsequent PD-L1 expression. Moreover, the PFKFB3-Janus kinase 1 (JAK1) axis might contribute to the expression of PD-L1 by macrophages. To sum up, we concluded that IL-32 was a critical mediator in myeloma progression, and targeting IL-32 signaling might be used as a potential strategy for treating myeloma.

RNA demethylase ALKBH5 in cancer: from mechanisms to therapeutic potential.

RNA demethylase ALKBH5 takes part in the modulation of N6-methyladenosine (m6A) modification and controls various cell processes. ALKBH5-mediated m6A demethylation regulates gene expression by affecting multiple events in RNA metabolism, e.g., pre-mRNA processing, mRNA decay and translation. Mounting evidence shows that ALKBH5 plays critical roles in a variety of human malignancies, mostly via post-transcriptional regulation of oncogenes or tumor suppressors in an m6A-dependent manner. Meanwhile, increasing non-coding RNAs are recognized as functional targets of ALKBH5 in cancers. Here we reviewed up-to-date findings about the pathological roles of ALKBH5 in cancer, the molecular mechanisms by which it exerts its functions, as well as the underlying mechanism of its dysregulation. We also discussed the therapeutic implications of targeting ALKBH5 in cancer and potential ALKBH5-targeting strategies.

RNA demethylase ALKBH5 promotes tumorigenesis in multiple myeloma via TRAF1-mediated activation of NF-κB and MAPK signaling pathways.

N6-methyladenosine (m6A), an internal modification in mRNA, plays a critical role in regulating gene expression. Dysregulation of m6A modifiers promotes oncogenesis through enzymatic functions that disrupt the balance between the deposition and removal of m6A modification on critical transcripts. However, the roles of mRNA m6A in multiple myeloma (MM) are poorly understood. The present study showed that RNA demethylase ALKBH5 was overexpressed in MM and associated with a poor prognosis in MM patients. Knocking down ALKBH5 induced apoptosis and inhibited the growth of MM cells in vitro. Xenograft models and gene set enrichment analysis with patient transcriptome datasets also supported the oncogenic role of ALKBH5 in MM. Mechanistic studies showed that ALKBH5 exerted tumorigenic effects in myeloma in an m6A-dependent manner, and TNF receptor-associated factor 1 (TRAF1) was a critical target of ALKBH5. Specifically, ALKBH5 regulated TRAF1 expression via decreasing m6A abundance in the 3'-untranslated region (3'-UTR) of TRAF1 transcripts and enhancing TRAF1 mRNA stability. As a result, ALKBH5 promoted MM cell growth and survival through TRAF1-mediated activation of NF-κB and MAPK signaling pathways. Collectively, our data demonstrated that ALKBH5 played a critical role in MM tumorigenesis and suggested that ALKBH5 could be a novel therapeutic target in MM.

EdClust: A heuristic sequence clustering method with higher sensitivity.

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

MLatom 2: An Integrative Platform for Atomistic Machine Learning.

Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.